# Building Abstractions with Procedures {#Chapter 1}

> The acts of the mind, wherein it exerts its power over simple ideas,
> are chiefly these three: 1. Combining several simple ideas into one
> compound one, and thus all complex ideas are made. 2. The second is
> bringing two ideas, whether simple or complex, together, and setting
> them by one another so as to take a view of them at once, without
> uniting them into one, by which it gets all its ideas of relations. 3.
> The third is separating them from all other ideas that accompany them
> in their real existence: this is called abstraction, and thus all its
> general ideas are made.
>
> ---John Locke, *An Essay Concerning Human Understanding* (1690)

We are about to study the idea of a
*computational process*. Computational processes are abstract beings
that inhabit computers. As they evolve, processes manipulate other
abstract things called *data*. The evolution of a process is directed by
a pattern of rules called a *program*. People create programs to direct
processes. In effect, we conjure the spirits of the computer with our
spells.

A computational process is indeed much like a sorcerer's idea of a
spirit. It cannot be seen or touched. It is not composed of matter at
all. However, it is very real. It can perform intellectual work. It can
answer questions. It can affect the world by disbursing money at a bank
or by controlling a robot arm in a factory. The programs we use to
conjure processes are like a sorcerer's spells. They are carefully
composed from symbolic expressions in arcane and esoteric *programming
languages* that prescribe the tasks we want our processes to perform.

A computational process, in a correctly working computer, executes
programs precisely and accurately. Thus, like the sorcerer's apprentice,
novice programmers must learn to understand and to anticipate the
consequences of their conjuring. Even small errors (usually called
*bugs* or *glitches*) in programs can have complex and unanticipated
consequences.

Fortunately, learning to program is considerably less dangerous than
learning sorcery, because the spirits we deal with are conveniently
contained in a secure way. Real-world programming, however, requires
care, expertise, and wisdom. A small bug in a computer-aided design
program, for example, can lead to the catastrophic collapse of an
airplane or a dam or the self-destruction of an industrial robot.

Master software engineers have the ability to organize programs so that
they can be reasonably sure that the resulting processes will perform
the tasks intended. They can visualize the behavior of their systems in
advance. They know how to structure programs so that unanticipated
problems do not lead to catastrophic consequences, and when problems do
arise, they can *debug* their programs. Well-designed computational
systems, like well-designed automobiles or nuclear reactors, are
designed in a modular manner, so that the parts can be constructed,
replaced, and debugged separately.

#### Programming in Lisp {#programming-in-lisp .unnumbered}

We need an appropriate language for describing processes, and we will
use for this purpose the programming language Lisp. Just as our everyday
thoughts are usually expressed in our natural language (such as English,
French, or Japanese), and descriptions of quantitative phenomena are
expressed with mathematical notations, our procedural thoughts will be
expressed in Lisp. Lisp was invented in the late 1950s as a formalism
for reasoning about the use of certain kinds of logical expressions,
called *recursion equations*, as a model for computation. The language
was conceived by John McCarthy and is based on his paper "Recursive
Functions of Symbolic Expressions and Their Computation by Machine"
([McCarthy 1960](#McCarthy 1960)).

Despite its inception as a mathematical formalism, Lisp is a practical
programming language. A Lisp *interpreter* is a machine that carries out
processes described in the Lisp language. The first Lisp interpreter was
implemented by McCarthy with the help of colleagues and students in the
Artificial Intelligence Group of the mit Research
Laboratory of Electronics and in the mit Computation
Center.[^1] Lisp, whose name is an acronym for LISt Processing, was
designed to provide symbol-manipulating capabilities for attacking
programming problems such as the symbolic differentiation and
integration of algebraic expressions. It included for this purpose new
data objects known as atoms and lists, which most strikingly set it
apart from all other languages of the period.

Lisp was not the product of a concerted design effort. Instead, it
evolved informally in an experimental manner in response to users' needs
and to pragmatic implementation considerations. Lisp's informal
evolution has continued through the years, and the community of Lisp
users has traditionally resisted attempts to promulgate any "official"
definition of the language. This evolution, together with the
flexibility and elegance of the initial conception, has enabled Lisp,
which is the second oldest language in widespread use today (only
Fortran is older), to continually adapt to encompass the most modern
ideas about program design. Thus, Lisp is by now a family of dialects,
which, while sharing most of the original features, may differ from one
another in significant ways. The dialect of Lisp used in this book is
called Scheme.[^2]

Because of its experimental character and its emphasis on symbol
manipulation, Lisp was at first very inefficient for numerical
computations, at least in comparison with Fortran. Over the years,
however, Lisp compilers have been developed that translate programs into
machine code that can perform numerical computations reasonably
efficiently. And for special applications, Lisp has been used with great
effectiveness.[^3] Although Lisp has not yet overcome its old reputation
as hopelessly inefficient, Lisp is now used in many applications where
efficiency is not the central concern. For example, Lisp has become a
language of choice for operating-system shell languages and for
extension languages for editors and computer-aided design systems.

If Lisp is not a mainstream language, why are we using it as the
framework for our discussion of programming? Because the language
possesses unique features that make it an excellent medium for studying
important programming constructs and data structures and for relating
them to the linguistic features that support them. The most significant
of these features is the fact that Lisp descriptions of processes,
called *procedures*, can themselves be represented and manipulated as
Lisp data. The importance of this is that there are powerful
program-design techniques that rely on the ability to blur the
traditional distinction between "passive" data and "active" processes.
As we shall discover, Lisp's flexibility in handling procedures as data
makes it one of the most convenient languages in existence for exploring
these techniques. The ability to represent procedures as data also makes
Lisp an excellent language for writing programs that must manipulate
other programs as data, such as the interpreters and compilers that
support computer languages. Above and beyond these considerations,
programming in Lisp is great fun.

## The Elements of Programming {#Section 1.1}

A powerful programming language is more than just a means for
instructing a computer to perform tasks. The language also serves as a
framework within which we organize our ideas about processes. Thus, when
we describe a language, we should pay particular attention to the means
that the language provides for combining simple ideas to form more
complex ideas. Every powerful language has three mechanisms for
accomplishing this:

-   **primitive expressions**, which represent the simplest entities the
    language is concerned with,

-   **means of combination**, by which compound elements are built from
    simpler ones, and

-   **means of abstraction**, by which compound elements can be named
    and manipulated as units.

In programming, we deal with two kinds of elements: procedures and data.
(Later we will discover that they are really not so distinct.)
Informally, data is "stuff$\kern0.1em$" that we want to manipulate, and
procedures are descriptions of the rules for manipulating the data.
Thus, any powerful programming language should be able to describe
primitive data and primitive procedures and should have methods for
combining and abstracting procedures and data.

In this chapter we will deal only with simple numerical data so that we
can focus on the rules for building procedures.[^4] In later chapters we
will see that these same rules allow us to build procedures to
manipulate compound data as well.

### Expressions {#Section 1.1.1}

One easy way to get started at programming is to examine some typical
interactions with an interpreter for the Scheme dialect of Lisp. Imagine
that you are sitting at a computer terminal. You type an *expression*,
and the interpreter responds by displaying the result of its
*evaluating* that expression.

One kind of primitive expression you might type is a number. (More
precisely, the expression that you type consists of the numerals that
represent the number in base 10.) If you present Lisp with a number

::: scheme
486
:::

the interpreter will respond by printing[^5]

::: scheme
 *486*
:::

Expressions representing numbers may be combined with an expression
representing a primitive procedure (such as `+` or `*`) to form a
compound expression that represents the application of the procedure to
those numbers. For example:

::: scheme
(+ 137 349)  *486*
:::

::: scheme
(- 1000 334)  *666*
:::

::: scheme
(\* 5 99)  *495*
:::

::: scheme
(/ 10 5)  *2*
:::

::: scheme
(+ 2.7 10)  *12.7*
:::

Expressions such as these, formed by delimiting a list of expressions
within parentheses in order to denote procedure application, are called
*combinations*. The leftmost element in the list is called the
*operator*, and the other elements are called *operands*. The value of a
combination is obtained by applying the procedure specified by the
operator to the *arguments* that are the values of the operands.

The convention of placing the operator to the left of the operands is
known as *prefix notation*, and it may be somewhat confusing at first
because it departs significantly from the customary mathematical
convention. Prefix notation has several advantages, however. One of them
is that it can accommodate procedures that may take an arbitrary number
of arguments, as in the following examples:

::: scheme
(+ 21 35 12 7)  *75*
:::

::: scheme
(\* 25 4 12)  *1200*
:::

No ambiguity can arise, because the operator is always the leftmost
element and the entire combination is delimited by the parentheses.

A second advantage of prefix notation is that it extends in a
straightforward way to allow combinations to be *nested*, that is, to
have combinations whose elements are themselves combinations:

::: scheme
(+ (\* 3 5) (- 10 6))  *19*
:::

There is no limit (in principle) to the depth of such nesting and to the
overall complexity of the expressions that the Lisp interpreter can
evaluate. It is we humans who get confused by still relatively simple
expressions such as

::: scheme
(+ (\* 3 (+ (\* 2 4) (+ 3 5))) (+ (- 10 7) 6))
:::

which the interpreter would readily evaluate to be 57. We can help
ourselves by writing such an expression in the form

::: scheme
(+ (\* 3 (+ (\* 2 4) (+ 3 5))) (+ (- 10 7) 6))
:::

following a formatting convention known as *pretty-printing*, in which
each long combination is written so that the operands are aligned
vertically. The resulting indentations display clearly the structure of
the expression.[^6]

Even with complex expressions, the interpreter always operates in the
same basic cycle: It reads an expression from the terminal, evaluates
the expression, and prints the result. This mode of operation is often
expressed by saying that the interpreter runs in a *read-eval-print
loop*. Observe in particular that it is not necessary to explicitly
instruct the interpreter to print the value of the expression.[^7]

### Naming and the Environment {#Section 1.1.2}

A critical aspect of a programming language is the means it provides for
using names to refer to computational objects. We say that the name
identifies a *variable* whose *value* is the object.

In the Scheme dialect of Lisp, we name things with `define`. Typing

::: scheme
(define size 2)
:::

causes the interpreter to associate the value 2 with the name
`size`.[^8] Once the name `size` has been associated with the number 2,
we can refer to the value 2 by name:

::: scheme
size  *2*
:::

::: scheme
(\* 5 size)  *10*
:::

Here are further examples of the use of `define`:

::: scheme
(define pi 3.14159) (define radius 10) (\* pi (\* radius radius))
 *314.159*  (define circumference (\* 2 pi radius)) circumference
 *62.8318*
:::

`define` is our language's simplest means of abstraction, for it allows
us to use simple names to refer to the results of compound operations,
such as the `circumference` computed above. In general, computational
objects may have very complex structures, and it would be extremely
inconvenient to have to remember and repeat their details each time we
want to use them. Indeed, complex programs are constructed by building,
step by step, computational objects of increasing complexity. The
interpreter makes this step-by-step program construction particularly
convenient because name-object associations can be created incrementally
in successive interactions. This feature encourages the incremental
development and testing of programs and is largely responsible for the
fact that a Lisp program usually consists of a large number of
relatively simple procedures.

It should be clear that the possibility of associating values with
symbols and later retrieving them means that the interpreter must
maintain some sort of memory that keeps track of the name-object pairs.
This memory is called the *environment* (more precisely the *global
environment*, since we will see later that a computation may involve a
number of different environments).[^9]

### Evaluating Combinations {#Section 1.1.3}

One of our goals in this chapter is to isolate issues about thinking
procedurally. As a case in point, let us consider that, in evaluating
combinations, the interpreter is itself following a procedure.

To evaluate a combination, do the following:

1.  Evaluate the subexpressions of the combination.

2.  Apply the procedure that is the value of the leftmost subexpression
    (the operator) to the arguments that are the values of the other
    subexpressions (the operands).

Even this simple rule illustrates some important points about processes
in general. First, observe that the first step dictates that in order to
accomplish the evaluation process for a combination we must first
perform the evaluation process on each element of the combination. Thus,
the evaluation rule is *recursive* in nature; that is, it includes, as
one of its steps, the need to invoke the rule itself.[^10]

Notice how succinctly the idea of recursion can be used to express what,
in the case of a deeply nested combination, would otherwise be viewed as
a rather complicated process. For example, evaluating

::: scheme
(\* (+ 2 (\* 4 6)) (+ 3 5 7))
:::

requires that the evaluation rule be applied to four different
combinations. We can obtain a picture of this process by representing
the combination in the form of a tree, as shown in [Figure
1.1](#Figure 1.1). Each combination is represented by a node with
branches corresponding to the operator and the operands of the
combination stemming from it. The terminal nodes (that is, nodes with no
branches stemming from them) represent either operators or numbers.
Viewing evaluation in terms of the tree, we can imagine that the values
of the operands percolate upward, starting from the terminal nodes and
then combining at higher and higher levels. In general, we shall see
that recursion is a very powerful technique for dealing with
hierarchical, treelike objects. In fact, the "percolate values upward"
form of the evaluation rule is an example of a general kind of process
known as *tree accumulation*.

[]{#Figure 1.1 label="Figure 1.1"}

![image](fig/chap1/Fig1.1g.pdf){width="31mm"}

> **Figure 1.1:** Tree representation, showing the value of each
> subcombination.

Next, observe that the repeated application of the first step brings us
to the point where we need to evaluate, not combinations, but primitive
expressions such as numerals, built-in operators, or other names. We
take care of the primitive cases by stipulating that

-   the values of numerals are the numbers that they name,

-   the values of built-in operators are the machine instruction
    sequences that carry out the corresponding operations, and

-   the values of other names are the objects associated with those
    names in the environment.

We may regard the second rule as a special case of the third one by
stipulating that symbols such as `+` and `*` are also included in the
global environment, and are associated with the sequences of machine
instructions that are their "values." The key point to notice is the
role of the environment in determining the meaning of the symbols in
expressions. In an interactive language such as Lisp, it is meaningless
to speak of the value of an expression such as `(+ x 1)` without
specifying any information about the environment that would provide a
meaning for the symbol `x` (or even for the symbol `+`). As we shall see
in [Chapter 3](#Chapter 3), the general notion of the environment as
providing a context in which evaluation takes place will play an
important role in our understanding of program execution.

Notice that the evaluation rule given above does not handle definitions.
For instance, evaluating `(define x 3)` does not apply `define` to two
arguments, one of which is the value of the symbol `x` and the other of
which is 3, since the purpose of the `define` is precisely to associate
`x` with a value. (That is, `(define x 3)` is not a combination.)

Such exceptions to the general evaluation rule are called *special
forms*. `define` is the only example of a special form that we have seen
so far, but we will meet others shortly. Each special form has its own
evaluation rule. The various kinds of expressions (each with its
associated evaluation rule) constitute the syntax of the programming
language. In comparison with most other programming languages, Lisp has
a very simple syntax; that is, the evaluation rule for expressions can
be described by a simple general rule together with specialized rules
for a small number of special forms.[^11]

### Compound Procedures {#Section 1.1.4}

We have identified in Lisp some of the elements that must appear in any
powerful programming language:

-   Numbers and arithmetic operations are primitive data and procedures.

-   Nesting of combinations provides a means of combining operations.

-   Definitions that associate names with values provide a limited means
    of abstraction.

Now we will learn about *procedure definitions*, a much more powerful
abstraction technique by which a compound operation can be given a name
and then referred to as a unit.

We begin by examining how to express the idea of "squaring." We might
say, "To square something, multiply it by itself." This is expressed in
our language as

::: scheme
(define (square x) (\* x x))
:::

We can understand this in the following way:

::: scheme
(define (square x) (\* x x)) \| \| \| \| \| \| To square something,
multiply it by itself.
:::

We have here a *compound procedure*, which has been given the name
`square`. The procedure represents the operation of multiplying
something by itself. The thing to be multiplied is given a local name,
`x`, which plays the same role that a pronoun plays in natural language.
Evaluating the definition creates this compound procedure and associates
it with the name `square`.[^12]

The general form of a procedure definition is

::: scheme
(define
( $\color{SchemeDark}\langle$  *name*  $\color{SchemeDark}\kern0.03em\rangle$
 $\color{SchemeDark}\langle$  *formal
parameters*  $\color{SchemeDark}\kern0.02em\rangle$ )
 $\color{SchemeDark}\langle\kern0.08em$  *body*  $\color{SchemeDark}\rangle$ )
:::

The $\langle\hbox{\sl name}\kern0.08em\rangle$ is a symbol to be
associated with the procedure definition in the environment.[^13] The
$\langle\hbox{\sl formal parameters}\kern0.08em\rangle$ are the names
used within the body of the procedure to refer to the corresponding
arguments of the procedure. The
$\langle\hbox{\sl body}\kern0.08em\rangle$ is an expression that will
yield the value of the procedure application when the formal parameters
are replaced by the actual arguments to which the procedure is
applied.[^14] The $\langle$*name*$\kern0.08em\rangle$ and the
$\langle$*formal parameters*$\kern0.08em\rangle$ are grouped within
parentheses, just as they would be in an actual call to the procedure
being defined.

Having defined `square`, we can now use it:

::: scheme
(square 21)  *441*  (square (+ 2 5))  *49*  (square (square 3))  *81*
:::

We can also use `square` as a building block in defining other
procedures. For example, $x^2 + y^2$ can be expressed as

::: scheme
(+ (square x) (square y))
:::

We can easily define a procedure `sum/of/squares` that, given any two
numbers as arguments, produces the sum of their squares:

::: scheme
(define (sum-of-squares x y) (+ (square x) (square y))) (sum-of-squares
3 4)  *25*
:::

Now we can use `sum/of/squares` as a building block in constructing
further procedures:

::: scheme
(define (f a) (sum-of-squares (+ a 1) (\* a 2))) (f 5)  *136*
:::

Compound procedures are used in exactly the same way as primitive
procedures. Indeed, one could not tell by looking at the definition of
`sum/of/squares` given above whether `square` was built into the
interpreter, like `+` and `*`, or defined as a compound procedure.

### The Substitution Model for Procedure Application {#Section 1.1.5}

To evaluate a combination whose operator names a compound procedure, the
interpreter follows much the same process as for combinations whose
operators name primitive procedures, which we described in [Section
1.1.3](#Section 1.1.3). That is, the interpreter evaluates the elements
of the combination and applies the procedure (which is the value of the
operator of the combination) to the arguments (which are the values of
the operands of the combination).

We can assume that the mechanism for applying primitive procedures to
arguments is built into the interpreter. For compound procedures, the
application process is as follows:

> To apply a compound procedure to arguments, evaluate the body of the
> procedure with each formal parameter replaced by the corresponding
> argument.

To illustrate this process, let's evaluate the combination

::: scheme
(f 5)
:::

where `f` is the procedure defined in [Section 1.1.4](#Section 1.1.4).
We begin by retrieving the body of `f`:

::: scheme
(sum-of-squares (+ a 1) (\* a 2))
:::

Then we replace the formal parameter `a` by the argument 5:

::: scheme
(sum-of-squares (+ 5 1) (\* 5 2))
:::

Thus the problem reduces to the evaluation of a combination with two
operands and an operator `sum/of/squares`. Evaluating this combination
involves three subproblems. We must evaluate the operator to get the
procedure to be applied, and we must evaluate the operands to get the
arguments. Now `(+ 5 1)` produces 6 and `(* 5 2)` produces 10, so we
must apply the `sum/of/squares` procedure to 6 and 10. These values are
substituted for the formal parameters `x` and `y` in the body of
`sum/of/squares`, reducing the expression to

::: scheme
(+ (square 6) (square 10))
:::

If we use the definition of `square`, this reduces to

::: scheme
(+ (\* 6 6) (\* 10 10))
:::

which reduces by multiplication to

::: scheme
(+ 36 100)
:::

and finally to

::: scheme
136
:::

The process we have just described is called the *substitution model*
for procedure application. It can be taken as a model that determines
the "meaning" of procedure application, insofar as the procedures in
this chapter are concerned. However, there are two points that should be
stressed:

-   The purpose of the substitution is to help us think about procedure
    application, not to provide a description of how the interpreter
    really works. Typical interpreters do not evaluate procedure
    applications by manipulating the text of a procedure to substitute
    values for the formal parameters. In practice, the "substitution" is
    accomplished by using a local environment for the formal parameters.
    We will discuss this more fully in [Chapter 3](#Chapter 3) and
    [Chapter 4](#Chapter 4) when we examine the implementation of an
    interpreter in detail.

-   Over the course of this book, we will present a sequence of
    increasingly elaborate models of how interpreters work, culminating
    with a complete implementation of an interpreter and compiler in
    [Chapter 5](#Chapter 5). The substitution model is only the first of
    these models---a way to get started thinking formally about the
    evaluation process. In general, when modeling phenomena in science
    and engineering, we begin with simplified, incomplete models. As we
    examine things in greater detail, these simple models become
    inadequate and must be replaced by more refined models. The
    substitution model is no exception. In particular, when we address
    in [Chapter 3](#Chapter 3) the use of procedures with "mutable
    data," we will see that the substitution model breaks down and must
    be replaced by a more complicated model of procedure
    application.[^15]

#### Applicative order versus normal order {#applicative-order-versus-normal-order .unnumbered}

According to the description of evaluation given in [Section
1.1.3](#Section 1.1.3), the interpreter first evaluates the operator and
operands and then applies the resulting procedure to the resulting
arguments. This is not the only way to perform evaluation. An
alternative evaluation model would not evaluate the operands until their
values were needed. Instead it would first substitute operand
expressions for parameters until it obtained an expression involving
only primitive operators, and would then perform the evaluation. If we
used this method, the evaluation of `(f 5)` would proceed according to
the sequence of expansions

::: scheme
(sum-of-squares (+ 5 1) (\* 5 2)) (+ (square (+ 5 1)) (square (\* 5 2))
) (+ (\* (+ 5 1) (+ 5 1)) (\* (\* 5 2) (\* 5 2)))
:::

followed by the reductions

::: scheme
(+ (\* 6 6) (\* 10 10)) (+ 36 100) 136
:::

This gives the same answer as our previous evaluation model, but the
process is different. In particular, the evaluations of `(+ 5 1)` and
`(* 5 2)` are each performed twice here, corresponding to the reduction
of the expression `(* x x)` with `x` replaced respectively by `(+ 5 1)`
and `(* 5 2)`.

This alternative "fully expand and then reduce" evaluation method is
known as *normal-order evaluation*, in contrast to the "evaluate the
arguments and then apply" method that the interpreter actually uses,
which is called *applicative-order evaluation*. It can be shown that,
for procedure applications that can be modeled using substitution
(including all the procedures in the first two chapters of this book)
and that yield legitimate values, normal-order and applicative-order
evaluation produce the same value. (See [Exercise 1.5](#Exercise 1.5)
for an instance of an "illegitimate" value where normal-order and
applicative-order evaluation do not give the same result.)

Lisp uses applicative-order evaluation, partly because of the additional
efficiency obtained from avoiding multiple evaluations of expressions
such as those illustrated with `(+ 5 1)` and `(* 5 2)` above and, more
significantly, because normal-order evaluation becomes much more
complicated to deal with when we leave the realm of procedures that can
be modeled by substitution. On the other hand, normal-order evaluation
can be an extremely valuable tool, and we will investigate some of its
implications in [Chapter 3](#Chapter
3) and [Chapter 4](#Chapter 4).[^16]

### Conditional Expressions and Predicates {#Section 1.1.6}

The expressive power of the class of procedures that we can define at
this point is very limited, because we have no way to make tests and to
perform different operations depending on the result of a test. For
instance, we cannot define a procedure that computes the absolute value
of a number by testing whether the number is positive, negative, or zero
and taking different actions in the different cases according to the
rule

$$|x| = \left\{ \begin{array}{r@{\quad \mathrm{if} \quad}l}
        x  &  x > 0, \\
    0  &  x = 0, \\
  \!\! -x  &  x < 0. \end{array} \right.$$

This construct is called a *case analysis*, and there is a special form
in Lisp for notating such a case analysis. It is called `cond` (which
stands for "conditional"), and it is used as follows:

::: scheme
(define (abs x) (cond ((\> x 0) x) ((= x 0) 0) ((\< x 0) (- x))))
:::

The general form of a conditional expression is

::: scheme
(cond
( $\color{SchemeDark}\langle$  *p*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *e*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$ )
( $\color{SchemeDark}\langle$  *p*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
 $\color{SchemeDark}\langle$  *e*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ )
 $\dots$
( $\color{SchemeDark}\langle$  *p*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$
 $\color{SchemeDark}\langle$  *e*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ ))
:::

consisting of the symbol `cond` followed by parenthesized pairs of
expressions

::: scheme
( $\color{SchemeDark}\langle$  *p*  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *e*  $\color{SchemeDark}\rangle$ )
:::

called *clauses*. The first expression in each pair is a
*predicate*---that is, an expression whose value is interpreted as
either true or false.[^17]

Conditional expressions are evaluated as follows. The predicate
$\langle{p_1}\rangle$ is evaluated first. If its value is false, then
$\langle{p_2}\rangle$ is evaluated. If $\langle{p_2}\rangle$'s value is
also false, then $\langle{p_3}\rangle$ is evaluated. This process
continues until a predicate is found whose value is true, in which case
the interpreter returns the value of the corresponding *consequent
expression* $\langle{e}\rangle$ of the clause as the value of the
conditional expression. If none of the $\langle{p}\rangle$'s is found to
be true, the value of the `cond` is undefined.

The word *predicate* is used for procedures that return true or false,
as well as for expressions that evaluate to true or false. The
absolute-value procedure `abs` makes use of the primitive predicates
`>`, `<`, and `=`.[^18] These take two numbers as arguments and test
whether the first number is, respectively, greater than, less than, or
equal to the second number, returning true or false accordingly.

Another way to write the absolute-value procedure is

::: scheme
(define (abs x) (cond ((\< x 0) (- x)) (else x)))
:::

which could be expressed in English as "If $x$ is less than zero return
$-x;$ otherwise return $x$." `else` is a special symbol that can be used
in place of the $\langle{p}\rangle$ in the final clause of a `cond`.
This causes the `cond` to return as its value the value of the
corresponding $\langle{e}\rangle$ whenever all previous clauses have
been bypassed. In fact, any expression that always evaluates to a true
value could be used as the $\langle{p}\rangle$ here.

Here is yet another way to write the absolute-value procedure:

::: scheme
(define (abs x) (if (\< x 0) (- x) x))
:::

This uses the special form `if`, a restricted type of conditional that
can be used when there are precisely two cases in the case analysis. The
general form of an `if` expression is

::: scheme
(if
 $\color{SchemeDark}\langle\kern0.07em$  *predicate*  $\color{SchemeDark}\kern0.06em\rangle$
 $\color{SchemeDark}\langle\kern0.07em$  *consequent*  $\color{SchemeDark}\kern0.05em\rangle$
 $\color{SchemeDark}\langle\kern0.06em$  *alternative*  $\color{SchemeDark}\kern0.06em\rangle$ )
:::

To evaluate an `if` expression, the interpreter starts by evaluating the
$\langle$*predicate*$\kern0.04em\rangle$ part of the expression. If the
$\langle$*predicate*$\kern0.04em\rangle$ evaluates to a true value, the
interpreter then evaluates the $\langle$*consequent*$\kern0.04em\rangle$
and returns its value. Otherwise it evaluates the
$\langle$*alternative*$\kern0.04em\rangle$ and returns its value.[^19]

In addition to primitive predicates such as `<`, `=`, and `>`, there are
logical composition operations, which enable us to construct compound
predicates. The three most frequently used are these:

-   $\hbox{\tt(and }\langle{e_1}\rangle\;\;\dots\;\;\langle{e_n}\rangle\hbox{\tt)}$

    The interpreter evaluates the expressions
    $\langle{e}\kern0.08em\rangle$ one at a time, in left-to-right
    order. If any $\langle{e}\kern0.08em\rangle$ evaluates to false, the
    value of the `and` expression is false, and the rest of the
    $\langle{e}\kern0.08em\rangle$'s are not evaluated. If all
    $\langle{e}\kern0.08em\rangle$'s evaluate to true values, the value
    of the `and` expression is the value of the last one.

-   $\hbox{\tt(or }\langle{e_1}\rangle\;\;\dots\;\;\langle{e_n}\rangle\hbox{\tt)}$

    The interpreter evaluates the expressions
    $\langle{e}\kern0.08em\rangle$ one at a time, in left-to-right
    order. If any $\langle{e}\kern0.08em\rangle$ evaluates to a true
    value, that value is returned as the value of the `or` expression,
    and the rest of the $\langle{e}\kern0.08em\rangle$'s are not
    evaluated. If all $\langle{e}\kern0.08em\rangle$'s evaluate to
    false, the value of the `or` expression is false.

-   $\hbox{\tt(not }\langle{e}\rangle\hbox{\tt)}$

    The value of a `not` expression is true when the expression
    $\langle{e}\kern0.08em\rangle$ evaluates to false, and false
    otherwise.

Notice that `and` and `or` are special forms, not procedures, because
the subexpressions are not necessarily all evaluated. `not` is an
ordinary procedure.

As an example of how these are used, the condition that a number $x$ be
in the range $5 < x < 10$ may be expressed as

::: scheme
(and (\> x 5) (\< x 10))
:::

As another example, we can define a predicate to test whether one number
is greater than or equal to another as

::: scheme
(define (\>= x y) (or (\> x y) (= x y)))
:::

or alternatively as

::: scheme
(define (\>= x y) (not (\< x y)))
:::

> **[]{#Exercise 1.1 label="Exercise 1.1"}Exercise 1.1:** Below is a
> sequence of expressions. What is the result printed by the interpreter
> in response to each expression? Assume that the sequence is to be
> evaluated in the order in which it is presented.
>
> ::: scheme
> 10 (+ 5 3 4) (- 9 1) (/ 6 2) (+ (\* 2 4) (- 4 6)) (define a 3) (define
> b (+ a 1)) (+ a b (\* a b)) (= a b) (if (and (\> b a) (\< b (\* a b)))
> b a)
> :::
>
> ::: scheme
> (cond ((= a 4) 6) ((= b 4) (+ 6 7 a)) (else 25))
> :::
>
> ::: scheme
> (+ 2 (if (\> b a) b a))
> :::
>
> ::: scheme
> (\* (cond ((\> a b) a) ((\< a b) b) (else -1)) (+ a 1))
> :::

> **[]{#Exercise 1.2 label="Exercise 1.2"}Exercise 1.2:** Translate the
> following expression into prefix form:
>
> $${5 + 4 + (2 - (3 - (6 + {4\over5})))\over3(6 - 2)(2 - 7)}.$$

> **[]{#Exercise 1.3 label="Exercise 1.3"}Exercise 1.3:** Define a
> procedure that takes three numbers as arguments and returns the sum of
> the squares of the two larger numbers.

> **[]{#Exercise 1.4 label="Exercise 1.4"}Exercise 1.4:** Observe that
> our model of evaluation allows for combinations whose operators are
> compound expressions. Use this observation to describe the behavior of
> the following procedure:
>
> ::: scheme
> (define (a-plus-abs-b a b) ((if (\> b 0) + -) a b))
> :::

> **[]{#Exercise 1.5 label="Exercise 1.5"}Exercise 1.5:** Ben Bitdiddle
> has invented a test to determine whether the interpreter he is faced
> with is using applicative-order evaluation or normal-order evaluation.
> He defines the following two procedures:
>
> ::: scheme
> (define (p) (p)) (define (test x y) (if (= x 0) 0 y))
> :::
>
> Then he evaluates the expression
>
> ::: scheme
> (test 0 (p))
> :::
>
> What behavior will Ben observe with an interpreter that uses
> applicative-order evaluation? What behavior will he observe with an
> interpreter that uses normal-order evaluation? Explain your answer.
> (Assume that the evaluation rule for the special form `if` is the same
> whether the interpreter is using normal or applicative order: The
> predicate expression is evaluated first, and the result determines
> whether to evaluate the consequent or the alternative expression.)

### Example: Square Roots by Newton's Method {#Section 1.1.7}

Procedures, as introduced above, are much like ordinary mathematical
functions. They specify a value that is determined by one or more
parameters. But there is an important difference between mathematical
functions and computer procedures. Procedures must be effective.

As a case in point, consider the problem of computing square roots. We
can define the square-root function as

$$\sqrt{x}\;\; = {\rm\;\; the\;\;} y
{\rm\;\; such\;\; that\;\;} y \ge 0 {\rm\;\; and\;\;} y^2 = x.$$

This describes a perfectly legitimate mathematical function. We could
use it to recognize whether one number is the square root of another, or
to derive facts about square roots in general. On the other hand, the
definition does not describe a procedure. Indeed, it tells us almost
nothing about how to actually find the square root of a given number. It
will not help matters to rephrase this definition in pseudo-Lisp:

::: scheme
(define (sqrt x) (the y (and (\>= y 0) (= (square y) x))))
:::

This only begs the question.

The contrast between function and procedure is a reflection of the
general distinction between describing properties of things and
describing how to do things, or, as it is sometimes referred to, the
distinction between declarative knowledge and imperative knowledge. In
mathematics we are usually concerned with declarative (what is)
descriptions, whereas in computer science we are usually concerned with
imperative (how to) descriptions.[^20]

How does one compute square roots? The most common way is to use
Newton's method of successive approximations, which says that whenever
we have a guess $y$ for the value of the square root of a number $x$, we
can perform a simple manipulation to get a better guess (one closer to
the actual square root) by averaging $y$ with $x / y$.[^21] For example,
we can compute the square root of 2 as follows. Suppose our initial
guess is 1:

Guess Quotient Average 1 (2/1) = 2 ((2 + 1)/2) = 1.5 1.5 (2/1.5) =
1.3333 ((1.3333 + 1.5)/2) = 1.4167 1.4167 (2/1.4167) = 1.4118 ((1.4167 +
1.4118)/2) = 1.4142 1.4142 \... \...

Continuing this process, we obtain better and better approximations to
the square root.

Now let's formalize the process in terms of procedures. We start with a
value for the radicand (the number whose square root we are trying to
compute) and a value for the guess. If the guess is good enough for our
purposes, we are done; if not, we must repeat the process with an
improved guess. We write this basic strategy as a procedure:

::: scheme
(define (sqrt-iter guess x) (if (good-enough? guess x) guess (sqrt-iter
(improve guess x) x)))
:::

A guess is improved by averaging it with the quotient of the radicand
and the old guess:

::: scheme
(define (improve guess x) (average guess (/ x guess)))
:::

where

::: scheme
(define (average x y) (/ (+ x y) 2))
:::

We also have to say what we mean by "good enough." The following will do
for illustration, but it is not really a very good test. (See [Exercise
1.7](#Exercise 1.7).) The idea is to improve the answer until it is
close enough so that its square differs from the radicand by less than a
predetermined tolerance (here 0.001):[^22]

::: scheme
(define (good-enough? guess x) (\< (abs (- (square guess) x)) 0.001))
:::

Finally, we need a way to get started. For instance, we can always guess
that the square root of any number is 1:[^23]

::: scheme
(define (sqrt x) (sqrt-iter 1.0 x))
:::

If we type these definitions to the interpreter, we can use `sqrt` just
as we can use any procedure:

::: scheme
(sqrt 9)  *3.00009155413138*

(sqrt (+ 100 37))  *11.704699917758145*

(sqrt (+ (sqrt 2) (sqrt 3)))  *1.7739279023207892*

(square (sqrt 1000))  *1000.000369924366*
:::

The `sqrt` program also illustrates that the simple procedural language
we have introduced so far is sufficient for writing any purely numerical
program that one could write in, say, C or Pascal. This might seem
surprising, since we have not included in our language any iterative
(looping) constructs that direct the computer to do something over and
over again. `sqrt/iter`, on the other hand, demonstrates how iteration
can be accomplished using no special construct other than the ordinary
ability to call a procedure.[^24]

> **[]{#Exercise 1.6 label="Exercise 1.6"}Exercise 1.6:** Alyssa P.
> Hacker doesn't see why `if` needs to be provided as a special form.
> "Why can't I just define it as an ordinary procedure in terms of
> `cond`?" she asks. Alyssa's friend Eva Lu Ator claims this can indeed
> be done, and she defines a new version of `if`:
>
> ::: scheme
> (define (new-if predicate then-clause else-clause) (cond (predicate
> then-clause) (else else-clause)))
> :::
>
> Eva demonstrates the program for Alyssa:
>
> ::: scheme
> (new-if (= 2 3) 0 5)  *5*  (new-if (= 1 1) 0 5)  *0*
> :::
>
> Delighted, Alyssa uses `new/if` to rewrite the square-root program:
>
> ::: scheme
> (define (sqrt-iter guess x) (new-if (good-enough? guess x) guess
> (sqrt-iter (improve guess x) x)))
> :::
>
> What happens when Alyssa attempts to use this to compute square roots?
> Explain.

> **[]{#Exercise 1.7 label="Exercise 1.7"}Exercise 1.7:** The
> `good/enough?` test used in computing square roots will not be very
> effective for finding the square roots of very small numbers. Also, in
> real computers, arithmetic operations are almost always performed with
> limited precision. This makes our test inadequate for very large
> numbers. Explain these statements, with examples showing how the test
> fails for small and large numbers. An alternative strategy for
> implementing `good/enough?` is to watch how `guess` changes from one
> iteration to the next and to stop when the change is a very small
> fraction of the guess. Design a square-root procedure that uses this
> kind of end test. Does this work better for small and large numbers?

> **[]{#Exercise 1.8 label="Exercise 1.8"}Exercise 1.8:** Newton's
> method for cube roots is based on the fact that if $y$ is an
> approximation to the cube root of $x$, then a better approximation is
> given by the value
>
> $${{x / y^2} + 2y \over 3}.$$
>
> Use this formula to implement a cube-root procedure analogous to the
> square-root procedure. (In [Section 1.3.4](#Section 1.3.4) we will see
> how to implement Newton's method in general as an abstraction of these
> square-root and cube-root procedures.)

### Procedures as Black-Box Abstractions {#Section 1.1.8}

`sqrt` is our first example of a process defined by a set of mutually
defined procedures. Notice that the definition of `sqrt/iter` is
*recursive*; that is, the procedure is defined in terms of itself. The
idea of being able to define a procedure in terms of itself may be
disturbing; it may seem unclear how such a "circular" definition could
make sense at all, much less specify a well-defined process to be
carried out by a computer. This will be addressed more carefully in
[Section 1.2](#Section 1.2). But first let's consider some other
important points illustrated by the `sqrt` example.

Observe that the problem of computing square roots breaks up naturally
into a number of subproblems: how to tell whether a guess is good
enough, how to improve a guess, and so on. Each of these tasks is
accomplished by a separate procedure. The entire `sqrt` program can be
viewed as a cluster of procedures (shown in [Figure 1.2](#Figure 1.2))
that mirrors the decomposition of the problem into subproblems.

[]{#Figure 1.2 label="Figure 1.2"}

![image](fig/chap1/Fig1.2.pdf){width="44mm"}

> **Figure 1.2:** Procedural decomposition of the `sqrt` program.

The importance of this decomposition strategy is not simply that one is
dividing the program into parts. After all, we could take any large
program and divide it into parts---the first ten lines, the next ten
lines, the next ten lines, and so on. Rather, it is crucial that each
procedure accomplishes an identifiable task that can be used as a module
in defining other procedures. For example, when we define the
`good/enough?` procedure in terms of `square`, we are able to regard the
`square` procedure as a "black box." We are not at that moment concerned
with *how* the procedure computes its result, only with the fact that it
computes the square. The details of how the square is computed can be
suppressed, to be considered at a later time. Indeed, as far as the
`good/enough?` procedure is concerned, `square` is not quite a procedure
but rather an abstraction of a procedure, a so-called *procedural
abstraction*. At this level of abstraction, any procedure that computes
the square is equally good.

Thus, considering only the values they return, the following two
procedures for squaring a number should be indistinguishable. Each takes
a numerical argument and produces the square of that number as the
value.[^25]

::: scheme
(define (square x) (\* x x)) (define (square x) (exp (double (log x))))
(define (double x) (+ x x))
:::

So a procedure definition should be able to suppress detail. The users
of the procedure may not have written the procedure themselves, but may
have obtained it from another programmer as a black box. A user should
not need to know how the procedure is implemented in order to use it.

#### Local names {#local-names .unnumbered}

One detail of a procedure's implementation that should not matter to the
user of the procedure is the implementer's choice of names for the
procedure's formal parameters. Thus, the following procedures should not
be distinguishable:

::: scheme
(define (square x) (\* x x)) (define (square y) (\* y y))
:::

This principle---that the meaning of a procedure should be independent
of the parameter names used by its author---seems on the surface to be
self-evident, but its consequences are profound. The simplest
consequence is that the parameter names of a procedure must be local to
the body of the procedure. For example, we used `square` in the
definition of `good/enough?` in our square-root procedure:

::: scheme
(define (good-enough? guess x) (\< (abs (- (square guess) x)) 0.001))
:::

The intention of the author of `good/enough?` is to determine if the
square of the first argument is within a given tolerance of the second
argument. We see that the author of `good/enough?` used the name `guess`
to refer to the first argument and `x` to refer to the second argument.
The argument of `square` is `guess`. If the author of `square` used `x`
(as above) to refer to that argument, we see that the `x` in
`good/enough?` must be a different `x` than the one in `square`. Running
the procedure `square` must not affect the value of `x` that is used by
`good/enough?`, because that value of `x` may be needed by
`good/enough?` after `square` is done computing.

If the parameters were not local to the bodies of their respective
procedures, then the parameter `x` in `square` could be confused with
the parameter `x` in `good/enough?`, and the behavior of `good/enough?`
would depend upon which version of `square` we used. Thus, `square`
would not be the black box we desired.

A formal parameter of a procedure has a very special role in the
procedure definition, in that it doesn't matter what name the formal
parameter has. Such a name is called a *bound variable*, and we say that
the procedure definition *binds* its formal parameters. The meaning of a
procedure definition is unchanged if a bound variable is consistently
renamed throughout the definition.[^26] If a variable is not bound, we
say that it is *free*. The set of expressions for which a binding
defines a name is called the *scope* of that name. In a procedure
definition, the bound variables declared as the formal parameters of the
procedure have the body of the procedure as their scope.

In the definition of `good/enough?` above, `guess` and `x` are bound
variables but `<`, `-`, `abs`, and `square` are free. The meaning of
`good/enough?` should be independent of the names we choose for `guess`
and `x` so long as they are distinct and different from `<`, `-`, `abs`,
and `square`. (If we renamed `guess` to `abs` we would have introduced a
bug by *capturing* the variable `abs`. It would have changed from free
to bound.) The meaning of `good/enough?` is not independent of the names
of its free variables, however. It surely depends upon the fact
(external to this definition) that the symbol `abs` names a procedure
for computing the absolute value of a number. `good/enough?` will
compute a different function if we substitute `cos` for `abs` in its
definition.

#### Internal definitions and block structure {#internal-definitions-and-block-structure .unnumbered}

We have one kind of name isolation available to us so far: The formal
parameters of a procedure are local to the body of the procedure. The
square-root program illustrates another way in which we would like to
control the use of names. The existing program consists of separate
procedures:

::: scheme
(define (sqrt x) (sqrt-iter 1.0 x)) (define (sqrt-iter guess x) (if
(good-enough? guess x) guess (sqrt-iter (improve guess x) x))) (define
(good-enough? guess x) (\< (abs (- (square guess) x)) 0.001)) (define
(improve guess x) (average guess (/ x guess)))
:::

The problem with this program is that the only procedure that is
important to users of `sqrt` is `sqrt`. The other procedures
(`sqrt/iter`, `good/enough?`, and `improve`) only clutter up their
minds. They may not define any other procedure called `good/enough?` as
part of another program to work together with the square-root program,
because `sqrt` needs it. The problem is especially severe in the
construction of large systems by many separate programmers. For example,
in the construction of a large library of numerical procedures, many
numerical functions are computed as successive approximations and thus
might have procedures named `good/enough?` and `improve` as auxiliary
procedures. We would like to localize the subprocedures, hiding them
inside `sqrt` so that `sqrt` could coexist with other successive
approximations, each having its own private `good/enough?` procedure. To
make this possible, we allow a procedure to have internal definitions
that are local to that procedure. For example, in the square-root
problem we can write

::: scheme
(define (sqrt x) (define (good-enough? guess x) (\< (abs (- (square
guess) x)) 0.001)) (define (improve guess x) (average guess (/ x
guess))) (define (sqrt-iter guess x) (if (good-enough? guess x) guess
(sqrt-iter (improve guess x) x))) (sqrt-iter 1.0 x))
:::

Such nesting of definitions, called *block structure*, is basically the
right solution to the simplest name-packaging problem. But there is a
better idea lurking here. In addition to internalizing the definitions
of the auxiliary procedures, we can simplify them. Since `x` is bound in
the definition of `sqrt`, the procedures `good/enough?`, `improve`, and
`sqrt/iter`, which are defined internally to `sqrt`, are in the scope of
`x`. Thus, it is not necessary to pass `x` explicitly to each of these
procedures. Instead, we allow `x` to be a free variable in the internal
definitions, as shown below. Then `x` gets its value from the argument
with which the enclosing procedure `sqrt` is called. This discipline is
called *lexical scoping*.[^27]

::: scheme
(define (sqrt x) (define (good-enough? guess) (\< (abs (- (square guess)
x)) 0.001)) (define (improve guess) (average guess (/ x guess))) (define
(sqrt-iter guess) (if (good-enough? guess) guess (sqrt-iter (improve
guess)))) (sqrt-iter 1.0))
:::

We will use block structure extensively to help us break up large
programs into tractable pieces.[^28] The idea of block structure
originated with the programming language Algol 60. It appears in most
advanced programming languages and is an important tool for helping to
organize the construction of large programs.

## Procedures and the Processes They Generate {#Section 1.2}

We have now considered the elements of programming: We have used
primitive arithmetic operations, we have combined these operations, and
we have abstracted these composite operations by defining them as
compound procedures. But that is not enough to enable us to say that we
know how to program. Our situation is analogous to that of someone who
has learned the rules for how the pieces move in chess but knows nothing
of typical openings, tactics, or strategy. Like the novice chess player,
we don't yet know the common patterns of usage in the domain. We lack
the knowledge of which moves are worth making (which procedures are
worth defining). We lack the experience to predict the consequences of
making a move (executing a procedure).

The ability to visualize the consequences of the actions under
consideration is crucial to becoming an expert programmer, just as it is
in any synthetic, creative activity. In becoming an expert photographer,
for example, one must learn how to look at a scene and know how dark
each region will appear on a print for each possible choice of exposure
and development conditions. Only then can one reason backward, planning
framing, lighting, exposure, and development to obtain the desired
effects. So it is with programming, where we are planning the course of
action to be taken by a process and where we control the process by
means of a program. To become experts, we must learn to visualize the
processes generated by various types of procedures. Only after we have
developed such a skill can we learn to reliably construct programs that
exhibit the desired behavior.

A procedure is a pattern for the *local evolution* of a computational
process. It specifies how each stage of the process is built upon the
previous stage. We would like to be able to make statements about the
overall, or *global*, behavior of a process whose local evolution has
been specified by a procedure. This is very difficult to do in general,
but we can at least try to describe some typical patterns of process
evolution.

In this section we will examine some common "shapes" for processes
generated by simple procedures. We will also investigate the rates at
which these processes consume the important computational resources of
time and space. The procedures we will consider are very simple. Their
role is like that played by test patterns in photography: as
oversimplified prototypical patterns, rather than practical examples in
their own right.

### Linear Recursion and Iteration {#Section 1.2.1}

We begin by considering the factorial function, defined by

$$n! = n \cdot (n - 1) \cdot (n - 2) \cdots 3 \cdot 2 \cdot 1.$$

There are many ways to compute factorials. One way is to make use of the
observation that $n!$ is equal to $n$ times $(n - 1)!$ for any positive
integer $n$:

$$n! = n \cdot [(n - 1) \cdot (n - 2) \cdots 3 \cdot 2 \cdot 1] = n \cdot (n - 1)!.$$

Thus, we can compute $n!$ by computing $(n - 1)!$ and multiplying the
result by $n$. If we add the stipulation that 1! is equal to 1, this
observation translates directly into a procedure:

::: scheme
(define (factorial n) (if (= n 1) 1 (\* n (factorial (- n 1)))))
:::

We can use the substitution model of [Section 1.1.5](#Section 1.1.5) to
watch this procedure in action computing 6!, as shown in [Figure
1.3](#Figure 1.3).

[]{#Figure 1.3 label="Figure 1.3"}

![image](fig/chap1/Fig1.3c.pdf){width="82mm"}

**Figure 1.3:** A linear recursive process for computing 6!.

Now let's take a different perspective on computing factorials. We could
describe a rule for computing $n!$ by specifying that we first multiply
1 by 2, then multiply the result by 3, then by 4, and so on until we
reach $n$. More formally, we maintain a running product, together with a
counter that counts from 1 up to $n$. We can describe the computation by
saying that the counter and the product simultaneously change from one
step to the next according to the rule

::: scheme
product  $\color{SchemeDark}\gets$  counter \* product counter
 $\color{SchemeDark}\gets$  counter + 1
:::

and stipulating that $n!$ is the value of the product when the counter
exceeds $n$.

[]{#Figure 1.4 label="Figure 1.4"}

![image](fig/chap1/Fig1.4c.pdf){width="36mm"}

**Figure 1.4:** A linear iterative process for computing 6!.

Once again, we can recast our description as a procedure for computing
factorials:[^29]

::: scheme
(define (factorial n) (fact-iter 1 1 n)) (define (fact-iter product
counter max-count) (if (\> counter max-count) product (fact-iter (\*
counter product) (+ counter 1) max-count)))
:::

As before, we can use the substitution model to visualize the process of
computing 6!, as shown in [Figure 1.4](#Figure 1.4).

Compare the two processes. From one point of view, they seem hardly
different at all. Both compute the same mathematical function on the
same domain, and each requires a number of steps proportional to $n$ to
compute $n!$. Indeed, both processes even carry out the same sequence of
multiplications, obtaining the same sequence of partial products. On the
other hand, when we consider the "shapes" of the two processes, we find
that they evolve quite differently.

Consider the first process. The substitution model reveals a shape of
expansion followed by contraction, indicated by the arrow in [Figure
1.3](#Figure 1.3). The expansion occurs as the process builds up a chain
of *deferred operations* (in this case, a chain of multiplications). The
contraction occurs as the operations are actually performed. This type
of process, characterized by a chain of deferred operations, is called a
*recursive process*. Carrying out this process requires that the
interpreter keep track of the operations to be performed later on. In
the computation of $n!$, the length of the chain of deferred
multiplications, and hence the amount of information needed to keep
track of it, grows linearly with $n$ (is proportional to $n$), just like
the number of steps. Such a process is called a *linear recursive
process*.

By contrast, the second process does not grow and shrink. At each step,
all we need to keep track of, for any $n$, are the current values of the
variables `product`, `counter`, and `max/count`. We call this an
*iterative process*. In general, an iterative process is one whose state
can be summarized by a fixed number of *state variables*, together with
a fixed rule that describes how the state variables should be updated as
the process moves from state to state and an (optional) end test that
specifies conditions under which the process should terminate. In
computing $n!$, the number of steps required grows linearly with $n$.
Such a process is called a *linear iterative process*.

The contrast between the two processes can be seen in another way. In
the iterative case, the program variables provide a complete description
of the state of the process at any point. If we stopped the computation
between steps, all we would need to do to resume the computation is to
supply the interpreter with the values of the three program variables.
Not so with the recursive process. In this case there is some additional
"hidden" information, maintained by the interpreter and not contained in
the program variables, which indicates "where the process is" in
negotiating the chain of deferred operations. The longer the chain, the
more information must be maintained.[^30]

In contrasting iteration and recursion, we must be careful not to
confuse the notion of a recursive *process* with the notion of a
recursive *procedure*. When we describe a procedure as recursive, we are
referring to the syntactic fact that the procedure definition refers
(either directly or indirectly) to the procedure itself. But when we
describe a process as following a pattern that is, say, linearly
recursive, we are speaking about how the process evolves, not about the
syntax of how a procedure is written. It may seem disturbing that we
refer to a recursive procedure such as `fact/iter` as generating an
iterative process. However, the process really is iterative: Its state
is captured completely by its three state variables, and an interpreter
need keep track of only three variables in order to execute the process.

One reason that the distinction between process and procedure may be
confusing is that most implementations of common languages (including
Ada, Pascal, and C) are designed in such a way that the interpretation
of any recursive procedure consumes an amount of memory that grows with
the number of procedure calls, even when the process described is, in
principle, iterative. As a consequence, these languages can describe
iterative processes only by resorting to special-purpose "looping
constructs" such as `do`, `repeat`, `until`, `for`, and `while`. The
implementation of Scheme we shall consider in [Chapter 5](#Chapter 5)
does not share this defect. It will execute an iterative process in
constant space, even if the iterative process is described by a
recursive procedure. An implementation with this property is called
*tail-recursive*. With a tail-recursive implementation, iteration can be
expressed using the ordinary procedure call mechanism, so that special
iteration constructs are useful only as syntactic sugar.[^31]

> **[]{#Exercise 1.9 label="Exercise 1.9"}Exercise 1.9:** Each of the
> following two procedures defines a method for adding two positive
> integers in terms of the procedures `inc`, which increments its
> argument by 1, and `dec`, which decrements its argument by 1.
>
> ::: scheme
> (define (+ a b) (if (= a 0) b (inc (+ (dec a) b)))) (define (+ a b)
> (if (= a 0) b (+ (dec a) (inc b))))
> :::
>
> Using the substitution model, illustrate the process generated by each
> procedure in evaluating `(+ 4 5)`. Are these processes iterative or
> recursive?

> **[]{#Exercise 1.10 label="Exercise 1.10"}Exercise 1.10:** The
> following procedure computes a mathematical function called
> Ackermann's function.
>
> ::: scheme
> (define (A x y) (cond ((= y 0) 0) ((= x 0) (\* 2 y)) ((= y 1) 2) (else
> (A (- x 1) (A x (- y 1))))))
> :::
>
> What are the values of the following expressions?
>
> ::: scheme
> (A 1 10) (A 2 4) (A 3 3)
> :::
>
> Consider the following procedures, where `A` is the procedure defined
> above:
>
> ::: scheme
> (define (f n) (A 0 n)) (define (g n) (A 1 n)) (define (h n) (A 2 n))
> (define (k n) (\* 5 n n))
> :::
>
> Give concise mathematical definitions for the functions computed by
> the procedures `f`, `g`, and `h` for positive integer values of $n$.
> For example, `(k n)` computes $5n^2$.

### Tree Recursion {#Section 1.2.2}

Another common pattern of computation is called *tree recursion*. As an
example, consider computing the sequence of Fibonacci numbers, in which
each number is the sum of the preceding two:

$$0,\; 1,\; 1,\; 2,\; 3,\; 5,\; 8,\; 13,\; 21,\; \dots.$$

In general, the Fibonacci numbers can be defined by the rule

$${\rm Fib}(n) =
\begin{cases}
        \; 0 & {\rm if} \;\; n=0, \\
    \; 1 & {\rm if} \;\; n=1, \\
    \; {\rm Fib}(n-1) + {\rm Fib}(n-2) \quad & {\rm otherwise}.
\end{cases}$$

We can immediately translate this definition into a recursive procedure
for computing Fibonacci numbers:

::: scheme
(define (fib n) (cond ((= n 0) 0) ((= n 1) 1) (else (+ (fib (- n 1))
(fib (- n 2))))))
:::

Consider the pattern of this computation. To compute `(fib 5)`, we
compute `(fib 4)` and `(fib 3)`. To compute `(fib 4)`, we compute
`(fib 3)` and `(fib 2)`. In general, the evolved process looks like a
tree, as shown in [Figure 1.5](#Figure 1.5). Notice that the branches
split into two at each level (except at the bottom); this reflects the
fact that the `fib` procedure calls itself twice each time it is
invoked.

This procedure is instructive as a prototypical tree recursion, but it
is a terrible way to compute Fibonacci numbers because it does so much
redundant computation. Notice in [Figure 1.5](#Figure 1.5) that the
entire computation of `(fib 3)`---almost half the work---is duplicated.
In fact, it is not hard to show that the number of times the procedure
will compute `(fib 1)` or `(fib 0)` (the number of leaves in the above
tree, in general) is precisely Fib($n+1$). To get an idea of how bad
this is, one can show that the value of Fib($n$) grows exponentially
with $n$. More precisely (see [Exercise 1.13](#Exercise 1.13)), Fib($n$)
is the closest integer to $\varphi^n / \sqrt{5}$, where

$$\varphi = {1 + \sqrt{5}\over2} \approx 1.6180$$

is the *golden ratio*, which satisfies the equation

$$\varphi^2 = \varphi + 1.$$

[]{#Figure 1.5 label="Figure 1.5"}

![image](fig/chap1/Fig1.5c.pdf){width="90mm"}

> **Figure 1.5:** The tree-recursive process generated in computing
> `(fib 5)`.

Thus, the process uses a number of steps that grows exponentially with
the input. On the other hand, the space required grows only linearly
with the input, because we need keep track only of which nodes are above
us in the tree at any point in the computation. In general, the number
of steps required by a tree-recursive process will be proportional to
the number of nodes in the tree, while the space required will be
proportional to the maximum depth of the tree.

We can also formulate an iterative process for computing the Fibonacci
numbers. The idea is to use a pair of integers $a$ and $b$, initialized
to Fib(1) = 1 and Fib(0) = 0, and to repeatedly apply the simultaneous
transformations

$$\begin{array}{l@{\;\;\gets\;\;}l}
  a & a + b, \\
  b & a.
\end{array}$$

It is not hard to show that, after applying this transformation $n$
times, $a$ and $b$ will be equal, respectively, to Fib($n+1$) and
Fib($n$). Thus, we can compute Fibonacci numbers iteratively using the
procedure

::: scheme
(define (fib n) (fib-iter 1 0 n)) (define (fib-iter a b count) (if (=
count 0) b (fib-iter (+ a b) a (- count 1))))
:::

This second method for computing Fib($n$) is a linear iteration. The
difference in number of steps required by the two methods---one linear
in $n$, one growing as fast as Fib($n$) itself---is enormous, even for
small inputs.

One should not conclude from this that tree-recursive processes are
useless. When we consider processes that operate on hierarchically
structured data rather than numbers, we will find that tree recursion is
a natural and powerful tool.[^32] But even in numerical operations,
tree-recursive processes can be useful in helping us to understand and
design programs. For instance, although the first `fib` procedure is
much less efficient than the second one, it is more straightforward,
being little more than a translation into Lisp of the definition of the
Fibonacci sequence. To formulate the iterative algorithm required
noticing that the computation could be recast as an iteration with three
state variables.

#### Example: Counting change {#example-counting-change .unnumbered}

It takes only a bit of cleverness to come up with the iterative
Fibonacci algorithm. In contrast, consider the following problem: How
many different ways can we make change of \$1.00, given half-dollars,
quarters, dimes, nickels, and pennies? More generally, can we write a
procedure to compute the number of ways to change any given amount of
money?

This problem has a simple solution as a recursive procedure. Suppose we
think of the types of coins available as arranged in some order. Then
the following relation holds:

The number of ways to change amount $a$ using $n$ kinds of coins equals

-   the number of ways to change amount $a$ using all but the first kind
    of coin, plus

-   the number of ways to change amount $a - d$ using all $n$ kinds of
    coins, where $d$ is the denomination of the first kind of coin.

To see why this is true, observe that the ways to make change can be
divided into two groups: those that do not use any of the first kind of
coin, and those that do. Therefore, the total number of ways to make
change for some amount is equal to the number of ways to make change for
the amount without using any of the first kind of coin, plus the number
of ways to make change assuming that we do use the first kind of coin.
But the latter number is equal to the number of ways to make change for
the amount that remains after using a coin of the first kind.

Thus, we can recursively reduce the problem of changing a given amount
to the problem of changing smaller amounts using fewer kinds of coins.
Consider this reduction rule carefully, and convince yourself that we
can use it to describe an algorithm if we specify the following
degenerate cases:[^33]

-   If $a$ is exactly 0, we should count that as 1 way to make change.

-   If $a$ is less than 0, we should count that as 0 ways to make
    change.

-   If $n$ is 0, we should count that as 0 ways to make change.

We can easily translate this description into a recursive procedure:

::: scheme
(define (count-change amount) (cc amount 5)) (define (cc amount
kinds-of-coins) (cond ((= amount 0) 1) ((or (\< amount 0) (=
kinds-of-coins 0)) 0) (else (+ (cc amount (- kinds-of-coins 1)) (cc (-
amount (first-denomination kinds-of-coins)) kinds-of-coins))))) (define
(first-denomination kinds-of-coins) (cond ((= kinds-of-coins 1) 1) ((=
kinds-of-coins 2) 5) ((= kinds-of-coins 3) 10) ((= kinds-of-coins 4) 25)
((= kinds-of-coins 5) 50)))
:::

(The `first/denomination` procedure takes as input the number of kinds
of coins available and returns the denomination of the first kind. Here
we are thinking of the coins as arranged in order from largest to
smallest, but any order would do as well.) We can now answer our
original question about changing a dollar:

::: scheme
(count-change 100)  *292*
:::

`count/change` generates a tree-recursive process with redundancies
similar to those in our first implementation of `fib`. (It will take
quite a while for that 292 to be computed.) On the other hand, it is not
obvious how to design a better algorithm for computing the result, and
we leave this problem as a challenge. The observation that a
tree-recursive process may be highly inefficient but often easy to
specify and understand has led people to propose that one could get the
best of both worlds by designing a "smart compiler" that could transform
tree-recursive procedures into more efficient procedures that compute
the same result.[^34]

> **[]{#Exercise 1.11 label="Exercise 1.11"}Exercise 1.11:** A function
> $f$ is defined by the rule that
>
> $$f(n) =
> \begin{cases}
> \;\; n \quad \text{if \; \( n < 3 \),} \\
> \;\; f(n-1) + 2\kern-0.08em f(n-2) + 3\kern-0.08em f(n-3) \quad \text{if \; \( n \ge 3 \).}
> \end{cases}$$
>
> Write a procedure that computes $f$ by means of a recursive process.
> Write a procedure that computes $f$ by means of an iterative process.

> **[]{#Exercise 1.12 label="Exercise 1.12"}Exercise 1.12:** The
> following pattern of numbers is called *Pascal's triangle*.
>
> 1 1 1 1 2 1 1 3 3 1 1 4 6 4 1 . . .
>
> The numbers at the edge of the triangle are all 1, and each number
> inside the triangle is the sum of the two numbers above it.[^35] Write
> a procedure that computes elements of Pascal's triangle by means of a
> recursive process.

> **[]{#Exercise 1.13 label="Exercise 1.13"}Exercise 1.13:** Prove that
> Fib($n$) is the closest integer to $\varphi^n / \sqrt{5}$, where
> $\varphi = (1 +
> \sqrt{5}) / 2$. Hint: Let $\psi = (1 - \sqrt{5}) / 2$. Use induction
> and the definition of the Fibonacci numbers (see [Section
> 1.2.2](#Section 1.2.2)) to prove that
> $\text{Fib}(n) = (\varphi^n - \psi^n) / \sqrt{5}$.

### Orders of Growth {#Section 1.2.3}

The previous examples illustrate that processes can differ considerably
in the rates at which they consume computational resources. One
convenient way to describe this difference is to use the notion of
*order of growth* to obtain a gross measure of the resources required by
a process as the inputs become larger.

Let $n$ be a parameter that measures the size of the problem, and let
$R(n)$ be the amount of resources the process requires for a problem of
size $n$. In our previous examples we took $n$ to be the number for
which a given function is to be computed, but there are other
possibilities. For instance, if our goal is to compute an approximation
to the square root of a number, we might take $n$ to be the number of
digits accuracy required. For matrix multiplication we might take $n$ to
be the number of rows in the matrices. In general there are a number of
properties of the problem with respect to which it will be desirable to
analyze a given process. Similarly, $R(n)$ might measure the number of
internal storage registers used, the number of elementary machine
operations performed, and so on. In computers that do only a fixed
number of operations at a time, the time required will be proportional
to the number of elementary machine operations performed.

We say that $R(n)$ has order of growth $\Theta(f(n))$, written $R(n)$ =
$\Theta(f(n))$ (pronounced "theta of $f(n)$"), if there are positive
constants $k_1$ and $k_2$ independent of $n$ such that
$k_1f(n) \le R(n) \le k_2f(n)$ for any sufficiently large value of $n$.
(In other words, for large $n$, the value $R(n)$ is sandwiched between
$k_1f(n)$ and $k_2f(n)$.)

For instance, with the linear recursive process for computing factorial
described in [Section 1.2.1](#Section 1.2.1) the number of steps grows
proportionally to the input $n$. Thus, the steps required for this
process grows as $\Theta(n)$. We also saw that the space required grows
as $\Theta(n)$. For the iterative factorial, the number of steps is
still $\Theta(n)$ but the space is $\Theta(1)$---that is, constant.[^36]
The tree-recursive Fibonacci computation requires $\Theta(\varphi^n)$
steps and space $\Theta(n)$, where $\varphi$ is the golden ratio
described in [Section 1.2.2](#Section 1.2.2).

Orders of growth provide only a crude description of the behavior of a
process. For example, a process requiring $n^2$ steps and a process
requiring $1000n^2$ steps and a process requiring $3n^2 + 10n + 17$
steps all have $\Theta(n^2)$ order of growth. On the other hand, order
of growth provides a useful indication of how we may expect the behavior
of the process to change as we change the size of the problem. For a
$\Theta(n)$ (linear) process, doubling the size will roughly double the
amount of resources used. For an exponential process, each increment in
problem size will multiply the resource utilization by a constant
factor. In the remainder of [Section 1.2](#Section 1.2) we will examine
two algorithms whose order of growth is logarithmic, so that doubling
the problem size increases the resource requirement by a constant
amount.

> **[]{#Exercise 1.14 label="Exercise 1.14"}Exercise 1.14:** Draw the
> tree illustrating the process generated by the `count/change`
> procedure of [Section 1.2.2](#Section 1.2.2) in making change for 11
> cents. What are the orders of growth of the space and number of steps
> used by this process as the amount to be changed increases?

> **[]{#Exercise 1.15 label="Exercise 1.15"}Exercise 1.15:** The sine of
> an angle (specified in radians) can be computed by making use of the
> approximation $\sin x \approx x$ if $x$ is sufficiently small, and the
> trigonometric identity
>
> $$\sin x = 3\sin {x\over3} - 4\sin^3 {x\over3}$$
>
> to reduce the size of the argument of sin. (For purposes of this
> exercise an angle is considered "sufficiently small" if its magnitude
> is not greater than 0.1 radians.) These ideas are incorporated in the
> following procedures:
>
> ::: scheme
> (define (cube x) (\* x x x)) (define (p x) (- (\* 3 x) (\* 4 (cube
> x)))) (define (sine angle) (if (not (\> (abs angle) 0.1)) angle (p
> (sine (/ angle 3.0)))))
> :::
>
> a.  How many times is the procedure `p` applied when `(sine 12.15)` is
>     evaluated?
>
> b.  What is the order of growth in space and number of steps (as a
>     function of $a$) used by the process generated by the `sine`
>     procedure when `(sine a)` is evaluated?

### Exponentiation {#Section 1.2.4}

Consider the problem of computing the exponential of a given number. We
would like a procedure that takes as arguments a base $b$ and a positive
integer exponent $n$ and computes $b^n$. One way to do this is via the
recursive definition

$$\begin{array}{l@{{}={}}l}
  b^n & b\cdot b^{n-1}, \\
  b^0 & 1,
\end{array}$$

which translates readily into the procedure

::: scheme
(define (expt b n) (if (= n 0) 1 (\* b (expt b (- n 1)))))
:::

This is a linear recursive process, which requires $\Theta(n)$ steps and
$\Theta(n)$ space. Just as with factorial, we can readily formulate an
equivalent linear iteration:

::: scheme
(define (expt b n) (expt-iter b n 1)) (define (expt-iter b counter
product) (if (= counter 0) product (expt-iter b (- counter 1) (\* b
product))))
:::

This version requires $\Theta(n)$ steps and $\Theta(1)$ space.

We can compute exponentials in fewer steps by using successive squaring.
For instance, rather than computing $b^8$ as

$$b\cdot (b\cdot (b\cdot (b\cdot (b\cdot (b\cdot (b\cdot b))))))\,,$$

we can compute it using three multiplications:

$$\begin{array}{l@{{}={}}l}
  b^2 & b\cdot b, \\
  b^4 & b^2\cdot b^2, \\
  b^8 & b^4\cdot b^4.
\end{array}$$

This method works fine for exponents that are powers of 2. We can also
take advantage of successive squaring in computing exponentials in
general if we use the rule

$$\begin{array}{l@{{}={}}lr@{\ n\ }l}
  b^n & (b^{n / 2})^2  \;\; & \mbox{if\,} & \mbox{\,is\, even}, \\
  b^n & b\cdot b^{n-1} \;\; & \mbox{if\,} & \mbox{\,is\, odd}.
\end{array}$$

We can express this method as a procedure:

::: scheme
(define (fast-expt b n) (cond ((= n 0) 1) ((even? n) (square (fast-expt
b (/ n 2)))) (else (\* b (fast-expt b (- n 1))))))
:::

where the predicate to test whether an integer is even is defined in
terms of the primitive procedure `remainder` by

::: scheme
(define (even? n) (= (remainder n 2) 0))
:::

The process evolved by `fast/expt` grows logarithmically with $n$ in
both space and number of steps. To see this, observe that computing
$b^{2n}$ using `fast/expt` requires only one more multiplication than
computing $b^n$. The size of the exponent we can compute therefore
doubles (approximately) with every new multiplication we are allowed.
Thus, the number of multiplications required for an exponent of $n$
grows about as fast as the logarithm of $n$ to the base 2. The process
has $\Theta(\log n)$ growth.[^37]

The difference between $\Theta(\log n)$ growth and $\Theta(n)$ growth
becomes striking as $n$ becomes large. For example, `fast/expt` for $n$
= 1000 requires only 14 multiplications.[^38] It is also possible to use
the idea of successive squaring to devise an iterative algorithm that
computes exponentials with a logarithmic number of steps (see [Exercise
1.16](#Exercise 1.16)), although, as is often the case with iterative
algorithms, this is not written down so straightforwardly as the
recursive algorithm.[^39]

> **[]{#Exercise 1.16 label="Exercise 1.16"}Exercise 1.16:** Design a
> procedure that evolves an iterative exponentiation process that uses
> successive squaring and uses a logarithmic number of steps, as does
> `fast/expt`. (Hint: Using the observation that
> $(b^{n / 2})^2 = (b^2)^{n / 2}$, keep, along with the exponent $n$ and
> the base $b$, an additional state variable $a$, and define the state
> transformation in such a way that the product $ab^n$ is unchanged from
> state to state. At the beginning of the process $a$ is taken to be 1,
> and the answer is given by the value of $a$ at the end of the process.
> In general, the technique of defining an *invariant quantity* that
> remains unchanged from state to state is a powerful way to think about
> the design of iterative algorithms.)

> **[]{#Exercise 1.17 label="Exercise 1.17"}Exercise 1.17:** The
> exponentiation algorithms in this section are based on performing
> exponentiation by means of repeated multiplication. In a similar way,
> one can perform integer multiplication by means of repeated addition.
> The following multiplication procedure (in which it is assumed that
> our language can only add, not multiply) is analogous to the `expt`
> procedure:
>
> ::: scheme
> (define (\* a b) (if (= b 0) 0 (+ a (\* a (- b 1)))))
> :::
>
> This algorithm takes a number of steps that is linear in `b`. Now
> suppose we include, together with addition, operations `double`, which
> doubles an integer, and `halve`, which divides an (even) integer by 2.
> Using these, design a multiplication procedure analogous to
> `fast/expt` that uses a logarithmic number of steps.

> **[]{#Exercise 1.18 label="Exercise 1.18"}Exercise 1.18:** Using the
> results of [Exercise 1.16](#Exercise 1.16) and [Exercise
> 1.17](#Exercise 1.17), devise a procedure that generates an iterative
> process for multiplying two integers in terms of adding, doubling, and
> halving and uses a logarithmic number of steps.[^40]

> **[]{#Exercise 1.19 label="Exercise 1.19"}Exercise 1.19:** There is a
> clever algorithm for computing the Fibonacci numbers in a logarithmic
> number of steps. Recall the transformation of the state variables $a$
> and $b$ in the `fib/iter` process of [Section 1.2.2](#Section 1.2.2):
> $a \gets a + b$ and $b \gets a$. Call this transformation $T$, and
> observe that applying $T$ over and over again $n$ times, starting with
> 1 and 0, produces the pair Fib($n+1$) and Fib($n$). In other words,
> the Fibonacci numbers are produced by applying $T^n$, the
> $n^{\mathrm{th}}$ power of the transformation $T$, starting with the
> pair (1, 0). Now consider $T$ to be the special case of $p=0$ and
> $q=1$ in a family of transformations $T_{pq}$, where $T_{pq}$
> transforms the pair $(a, b)$ according to $a \gets bq + aq + ap$ and
> $b \gets bp + aq$. Show that if we apply such a transformation
> $T_{pq}$ twice, the effect is the same as using a single
> transformation $T_{p'\!q'}$ of the same form, and compute $p'\!$ and
> $q'\!$ in terms of $p$ and $q$. This gives us an explicit way to
> square these transformations, and thus we can compute $T^n$ using
> successive squaring, as in the `fast/expt` procedure. Put this all
> together to complete the following procedure, which runs in a
> logarithmic number of steps:[^41]
>
> ::: scheme
> (define (fib n) (fib-iter 1 0 0 1 n)) (define (fib-iter a b p q count)
> (cond ((= count 0) b) ((even? count) (fib-iter a b
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$   [;
> compute $p'$]{.roman}
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$   [;
> compute $q'$]{.roman}  (/ count 2))) (else (fib-iter (+ (\* b q) (\* a
> q) (\* a p)) (+ (\* b p) (\* a q)) p q (- count 1)))))
> :::

### Greatest Common Divisors {#Section 1.2.5}

The greatest common divisor (gcd) of two integers $a$ and
$b$ is defined to be the largest integer that divides both $a$ and $b$
with no remainder. For example, the gcd of 16 and 28 is 4.
In [Chapter 2](#Chapter
2), when we investigate how to implement rational-number arithmetic, we
will need to be able to compute gcds in order to reduce
rational numbers to lowest terms. (To reduce a rational number to lowest
terms, we must divide both the numerator and the denominator by their
gcd. For example, 16/28 reduces to 4/7.) One way to find
the gcd of two integers is to factor them and search for
common factors, but there is a famous algorithm that is much more
efficient.

The idea of the algorithm is based on the observation that, if $r$ is
the remainder when $a$ is divided by $b$, then the common divisors of
$a$ and $b$ are precisely the same as the common divisors of $b$ and
$r$. Thus, we can use the equation

GCD(a,b) = GCD(b,r)

to successively reduce the problem of computing a gcd to
the problem of computing the gcd of smaller and smaller
pairs of integers. For example,

GCD(206,40) = GCD(40,6) = GCD(6,4) = GCD(4,2) = GCD(2,0) = 2

reduces gcd(206, 40) to gcd(2, 0), which is
2. It is possible to show that starting with any two positive integers
and performing repeated reductions will always eventually produce a pair
where the second number is 0. Then the gcd is the other
number in the pair. This method for computing the gcd is
known as *Euclid's Algorithm*.[^42]

It is easy to express Euclid's Algorithm as a procedure:

::: scheme
(define (gcd a b) (if (= b 0) a (gcd b (remainder a b))))
:::

This generates an iterative process, whose number of steps grows as the
logarithm of the numbers involved.

The fact that the number of steps required by Euclid's Algorithm has
logarithmic growth bears an interesting relation to the Fibonacci
numbers:

> **Lamé's Theorem:** If Euclid's Algorithm requires $k$ steps to
> compute the gcd of some pair, then the smaller number in
> the pair must be greater than or equal to the $k^{\mathrm{th}}$
> Fibonacci number.[^43]

We can use this theorem to get an order-of-growth estimate for Euclid's
Algorithm. Let $n$ be the smaller of the two inputs to the procedure. If
the process takes $k$ steps, then we must have $n \ge {\rm Fib}(k)
\approx \varphi^k / \sqrt{5}$. Therefore the number of steps $k$ grows
as the logarithm (to the base $\varphi$) of $n$. Hence, the order of
growth is $\Theta(\log n)$.

> **[]{#Exercise 1.20 label="Exercise 1.20"}Exercise 1.20:** The process
> that a procedure generates is of course dependent on the rules used by
> the interpreter. As an example, consider the iterative `gcd` procedure
> given above. Suppose we were to interpret this procedure using
> normal-order evaluation, as discussed in [Section
> 1.1.5](#Section 1.1.5). (The normal-order-evaluation rule for `if` is
> described in [Exercise 1.5](#Exercise 1.5).) Using the substitution
> method (for normal order), illustrate the process generated in
> evaluating `(gcd 206 40)` and indicate the `remainder` operations that
> are actually performed. How many `remainder` operations are actually
> performed in the normal-order evaluation of `(gcd 206 40)`? In the
> applicative-order evaluation?

### Example: Testing for Primality {#Section 1.2.6}

This section describes two methods for checking the primality of an
integer $n$, one with order of growth $\Theta(\sqrt{n})$, and a
"probabilistic" algorithm with order of growth $\Theta(\log n)$. The
exercises at the end of this section suggest programming projects based
on these algorithms.

#### Searching for divisors {#searching-for-divisors .unnumbered}

Since ancient times, mathematicians have been fascinated by problems
concerning prime numbers, and many people have worked on the problem of
determining ways to test if numbers are prime. One way to test if a
number is prime is to find the number's divisors. The following program
finds the smallest integral divisor (greater than 1) of a given number
$n$. It does this in a straightforward way, by testing $n$ for
divisibility by successive integers starting with 2.

::: scheme
(define (smallest-divisor n) (find-divisor n 2)) (define (find-divisor n
test-divisor) (cond ((\> (square test-divisor) n) n) ((divides?
test-divisor n) test-divisor) (else (find-divisor n (+ test-divisor
1))))) (define (divides? a b) (= (remainder b a) 0))
:::

We can test whether a number is prime as follows: $n$ is prime if and
only if $n$ is its own smallest divisor.

::: scheme
(define (prime? n) (= n (smallest-divisor n)))
:::

The end test for `find/divisor` is based on the fact that if $n$ is not
prime it must have a divisor less than or equal to $\sqrt{n}$.[^44] This
means that the algorithm need only test divisors between 1 and
$\sqrt{n}$. Consequently, the number of steps required to identify $n$
as prime will have order of growth $\Theta(\sqrt{n})$.

#### The Fermat test {#the-fermat-test .unnumbered}

The $\Theta(\log n)$ primality test is based on a result from number
theory known as Fermat's Little Theorem.[^45]

> **Fermat's Little Theorem:** If $n$ is a prime number and $a$ is any
> positive integer less than $n$, then $a$ raised to the
> $n^{\mathrm{th}}$ power is congruent to $a$ modulo $n$.

(Two numbers are said to be *congruent modulo* $n$ if they both have the
same remainder when divided by $n$. The remainder of a number $a$ when
divided by $n$ is also referred to as the *remainder of* $a$ *modulo*
$n$, or simply as $a$ *modulo* $n$.)

If $n$ is not prime, then, in general, most of the numbers $a < n$ will
not satisfy the above relation. This leads to the following algorithm
for testing primality: Given a number $n$, pick a random number $a < n$
and compute the remainder of $a^n$ modulo $n$. If the result is not
equal to $a$, then $n$ is certainly not prime. If it is $a$, then
chances are good that $n$ is prime. Now pick another random number $a$
and test it with the same method. If it also satisfies the equation,
then we can be even more confident that $n$ is prime. By trying more and
more values of $a$, we can increase our confidence in the result. This
algorithm is known as the Fermat test.

To implement the Fermat test, we need a procedure that computes the
exponential of a number modulo another number:

::: scheme
(define (expmod base exp m) (cond ((= exp 0) 1) ((even? exp) (remainder
(square (expmod base (/ exp 2) m)) m)) (else (remainder (\* base (expmod
base (- exp 1) m)) m))))
:::

This is very similar to the `fast/expt` procedure of [Section
1.2.4](#Section 1.2.4). It uses successive squaring, so that the number
of steps grows logarithmically with the exponent.[^46]

The Fermat test is performed by choosing at random a number $a$ between
1 and $n-1$ inclusive and checking whether the remainder modulo $n$ of
the $n^{\mathrm{th}}$ power of $a$ is equal to $a$. The random number
$a$ is chosen using the procedure `random`, which we assume is included
as a primitive in Scheme. `random` returns a nonnegative integer less
than its integer input. Hence, to obtain a random number between 1 and
$n-1$, we call `random` with an input of $n-1$ and add 1 to the result:

::: scheme
(define (fermat-test n) (define (try-it a) (= (expmod a n n) a)) (try-it
(+ 1 (random (- n 1)))))
:::

The following procedure runs the test a given number of times, as
specified by a parameter. Its value is true if the test succeeds every
time, and false otherwise.

::: scheme
(define (fast-prime? n times) (cond ((= times 0) true) ((fermat-test n)
(fast-prime? n (- times 1))) (else false)))
:::

#### Probabilistic methods {#probabilistic-methods .unnumbered}

The Fermat test differs in character from most familiar algorithms, in
which one computes an answer that is guaranteed to be correct. Here, the
answer obtained is only probably correct. More precisely, if $n$ ever
fails the Fermat test, we can be certain that $n$ is not prime. But the
fact that $n$ passes the test, while an extremely strong indication, is
still not a guarantee that $n$ is prime. What we would like to say is
that for any number $n$, if we perform the test enough times and find
that $n$ always passes the test, then the probability of error in our
primality test can be made as small as we like.

Unfortunately, this assertion is not quite correct. There do exist
numbers that fool the Fermat test: numbers $n$ that are not prime and
yet have the property that $a^n$ is congruent to $a$ modulo $n$ for all
integers $a < n$. Such numbers are extremely rare, so the Fermat test is
quite reliable in practice.[^47]

There are variations of the Fermat test that cannot be fooled. In these
tests, as with the Fermat method, one tests the primality of an integer
$n$ by choosing a random integer $a < n$ and checking some condition
that depends upon $n$ and $a$. (See [Exercise 1.28](#Exercise 1.28) for
an example of such a test.) On the other hand, in contrast to the Fermat
test, one can prove that, for any $n$, the condition does not hold for
most of the integers $a < n$ unless $n$ is prime. Thus, if $n$ passes
the test for some random choice of $a$, the chances are better than even
that $n$ is prime. If $n$ passes the test for two random choices of $a$,
the chances are better than 3 out of 4 that $n$ is prime. By running the
test with more and more randomly chosen values of $a$ we can make the
probability of error as small as we like.

The existence of tests for which one can prove that the chance of error
becomes arbitrarily small has sparked interest in algorithms of this
type, which have come to be known as *probabilistic algorithms*. There
is a great deal of research activity in this area, and probabilistic
algorithms have been fruitfully applied to many fields.[^48]

> **[]{#Exercise 1.21 label="Exercise 1.21"}Exercise 1.21:** Use the
> `smallest/divisor` procedure to find the smallest divisor of each of
> the following numbers: 199, 1999, 19999.

> **[]{#Exercise 1.22 label="Exercise 1.22"}Exercise 1.22:** Most Lisp
> implementations include a primitive called `runtime` that returns an
> integer that specifies the amount of time the system has been running
> (measured, for example, in microseconds). The following
> `timed/prime/test` procedure, when called with an integer $n$, prints
> $n$ and checks to see if $n$ is prime. If $n$ is prime, the procedure
> prints three asterisks followed by the amount of time used in
> performing the test.
>
> ::: scheme
> (define (timed-prime-test n) (newline) (display n) (start-prime-test n
> (runtime))) (define (start-prime-test n start-time) (if (prime? n)
> (report-prime (- (runtime) start-time)))) (define (report-prime
> elapsed-time) (display \" \*\*\* \") (display elapsed-time))
> :::
>
> Using this procedure, write a procedure `search/for/primes` that
> checks the primality of consecutive odd integers in a specified range.
> Use your procedure to find the three smallest primes larger than 1000;
> larger than 10,000; larger than 100,000; larger than 1,000,000. Note
> the time needed to test each prime. Since the testing algorithm has
> order of growth of $\Theta(\sqrt{n})$, you should expect that testing
> for primes around 10,000 should take about $\sqrt{10}$ times as long
> as testing for primes around 1000. Do your timing data bear this out?
> How well do the data for 100,000 and 1,000,000 support the
> $\Theta(\sqrt{n})$ prediction? Is your result compatible with the
> notion that programs on your machine run in time proportional to the
> number of steps required for the computation?

> **[]{#Exercise 1.23 label="Exercise 1.23"}Exercise 1.23:** The
> `smallest/divisor` procedure shown at the start of this section does
> lots of needless testing: After it checks to see if the number is
> divisible by 2 there is no point in checking to see if it is divisible
> by any larger even numbers. This suggests that the values used for
> `test/divisor` should not be 2, 3, 4, 5, 6, $\dots$, but rather 2, 3,
> 5, 7, 9, $\dots$. To implement this change, define a procedure `next`
> that returns 3 if its input is equal to 2 and otherwise returns its
> input plus 2. Modify the `smallest/divisor` procedure to use
> `(next test/divisor)` instead of `(+ test/divisor 1)`. With
> `timed/prime/test` incorporating this modified version of
> `smallest/divisor`, run the test for each of the 12 primes found in
> [Exercise 1.22](#Exercise 1.22). Since this modification halves the
> number of test steps, you should expect it to run about twice as fast.
> Is this expectation confirmed? If not, what is the observed ratio of
> the speeds of the two algorithms, and how do you explain the fact that
> it is different from 2?

> **[]{#Exercise 1.24 label="Exercise 1.24"}Exercise 1.24:** Modify the
> `timed/prime/test` procedure of [Exercise 1.22](#Exercise 1.22) to use
> `fast/prime?` (the Fermat method), and test each of the 12 primes you
> found in that exercise. Since the Fermat test has $\Theta(\log n)$
> growth, how would you expect the time to test primes near 1,000,000 to
> compare with the time needed to test primes near 1000? Do your data
> bear this out? Can you explain any discrepancy you find?

> **[]{#Exercise 1.25 label="Exercise 1.25"}Exercise 1.25:** Alyssa P.
> Hacker complains that we went to a lot of extra work in writing
> `expmod`. After all, she says, since we already know how to compute
> exponentials, we could have simply written
>
> ::: scheme
> (define (expmod base exp m) (remainder (fast-expt base exp) m))
> :::
>
> Is she correct? Would this procedure serve as well for our fast prime
> tester? Explain.

> **[]{#Exercise 1.26 label="Exercise 1.26"}Exercise 1.26:** Louis
> Reasoner is having great difficulty doing [Exercise
> 1.24](#Exercise 1.24). His `fast/prime?` test seems to run more slowly
> than his `prime?` test. Louis calls his friend Eva Lu Ator over to
> help. When they examine Louis's code, they find that he has rewritten
> the `expmod` procedure to use an explicit multiplication, rather than
> calling `square`:
>
> ::: scheme
> (define (expmod base exp m) (cond ((= exp 0) 1) ((even? exp)
> (remainder (\* (expmod base (/ exp 2) m) (expmod base (/ exp 2) m))
> m)) (else (remainder (\* base (expmod base (- exp 1) m)) m))))
> :::
>
> "I don't see what difference that could make," says Louis. "I do."
> says Eva. "By writing the procedure like that, you have transformed
> the $\Theta(\log n)$ process into a $\Theta(n)$ process." Explain.

> **[]{#Exercise 1.27 label="Exercise 1.27"}Exercise 1.27:** Demonstrate
> that the Carmichael numbers listed in [Footnote 1.47](#Footnote 1.47)
> really do fool the Fermat test. That is, write a procedure that takes
> an integer $n$ and tests whether $a^n$ is congruent to $a$ modulo $n$
> for every $a < n$, and try your procedure on the given Carmichael
> numbers.

> **[]{#Exercise 1.28 label="Exercise 1.28"}Exercise 1.28:** One variant
> of the Fermat test that cannot be fooled is called the *Miller-Rabin
> test* ([Miller 1976](#Miller 1976); [Rabin 1980](#Rabin 1980)). This
> starts from an alternate form of Fermat's Little Theorem, which states
> that if $n$ is a prime number and $a$ is any positive integer less
> than $n$, then $a$ raised to the ($n-1$)-st power is congruent to 1
> modulo $n$. To test the primality of a number $n$ by the Miller-Rabin
> test, we pick a random number $a < n$ and raise $a$ to the ($n-1$)-st
> power modulo $n$ using the `expmod` procedure. However, whenever we
> perform the squaring step in `expmod`, we check to see if we have
> discovered a "nontrivial square root of 1 modulo $n$," that is, a
> number not equal to 1 or $n-1$ whose square is equal to 1 modulo $n$.
> It is possible to prove that if such a nontrivial square root of 1
> exists, then $n$ is not prime. It is also possible to prove that if
> $n$ is an odd number that is not prime, then, for at least half the
> numbers $a < n$, computing $a^{n-1}$ in this way will reveal a
> nontrivial square root of 1 modulo $n$. (This is why the Miller-Rabin
> test cannot be fooled.) Modify the `expmod` procedure to signal if it
> discovers a nontrivial square root of 1, and use this to implement the
> Miller-Rabin test with a procedure analogous to `fermat/test`. Check
> your procedure by testing various known primes and non-primes. Hint:
> One convenient way to make `expmod` signal is to have it return 0.

## Formulating Abstractions with Higher-Order Procedures {#Section 1.3}

We have seen that procedures are, in effect, abstractions that describe
compound operations on numbers independent of the particular numbers.
For example, when we

::: scheme
(define (cube x) (\* x x x))
:::

we are not talking about the cube of a particular number, but rather
about a method for obtaining the cube of any number. Of course we could
get along without ever defining this procedure, by always writing
expressions such as

::: scheme
(\* 3 3 3) (\* x x x) (\* y y y)
:::

and never mentioning `cube` explicitly. This would place us at a serious
disadvantage, forcing us to work always at the level of the particular
operations that happen to be primitives in the language (multiplication,
in this case) rather than in terms of higher-level operations. Our
programs would be able to compute cubes, but our language would lack the
ability to express the concept of cubing. One of the things we should
demand from a powerful programming language is the ability to build
abstractions by assigning names to common patterns and then to work in
terms of the abstractions directly. Procedures provide this ability.
This is why all but the most primitive programming languages include
mechanisms for defining procedures.

Yet even in numerical processing we will be severely limited in our
ability to create abstractions if we are restricted to procedures whose
parameters must be numbers. Often the same programming pattern will be
used with a number of different procedures. To express such patterns as
concepts, we will need to construct procedures that can accept
procedures as arguments or return procedures as values. Procedures that
manipulate procedures are called *higher-order procedures*. This section
shows how higher-order procedures can serve as powerful abstraction
mechanisms, vastly increasing the expressive power of our language.

### Procedures as Arguments {#Section 1.3.1}

Consider the following three procedures. The first computes the sum of
the integers from `a` through `b`:

::: scheme
(define (sum-integers a b) (if (\> a b) 0 (+ a (sum-integers (+ a 1)
b))))
:::

The second computes the sum of the cubes of the integers in the given
range:

::: scheme
(define (sum-cubes a b) (if (\> a b) 0 (+ (cube a) (sum-cubes (+ a 1)
b))))
:::

The third computes the sum of a sequence of terms in the series

$${1\over1\cdot 3} +  {1\over5\cdot 7} + {1\over9\cdot 11} + \dots,$$

which converges to $\pi / 8$ (very slowly):[^49]

::: scheme
(define (pi-sum a b) (if (\> a b) 0 (+ (/ 1.0 (\* a (+ a 2))) (pi-sum (+
a 4) b))))
:::

These three procedures clearly share a common underlying pattern. They
are for the most part identical, differing only in the name of the
procedure, the function of `a` used to compute the term to be added, and
the function that provides the next value of `a`. We could generate each
of the procedures by filling in slots in the same template:

::: scheme
(define
( $\color{SchemeDark}\langle$  *name*  $\color{SchemeDark}\rangle$  a b)
(if (\> a b) 0 (+
( $\color{SchemeDark}\langle$  *term*  $\color{SchemeDark}\rangle$  a)
( $\color{SchemeDark}\langle$  *name*  $\color{SchemeDark}\rangle$
( $\color{SchemeDark}\langle$  *next*  $\color{SchemeDark}\rangle$  a)
b))))
:::

The presence of such a common pattern is strong evidence that there is a
useful abstraction waiting to be brought to the surface. Indeed,
mathematicians long ago identified the abstraction of *summation of a
series* and invented "sigma notation," for example

$$\sum\limits_{n=a}^b f(n) = f(a) + \dots + f(b),$$

to express this concept. The power of sigma notation is that it allows
mathematicians to deal with the concept of summation itself rather than
only with particular sums---for example, to formulate general results
about sums that are independent of the particular series being summed.

Similarly, as program designers, we would like our language to be
powerful enough so that we can write a procedure that expresses the
concept of summation itself rather than only procedures that compute
particular sums. We can do so readily in our procedural language by
taking the common template shown above and transforming the "slots" into
formal parameters:

::: scheme
(define (sum term a next b) (if (\> a b) 0 (+ (term a) (sum term (next
a) next b))))
:::

Notice that `sum` takes as its arguments the lower and upper bounds `a`
and `b` together with the procedures `term` and `next`. We can use `sum`
just as we would any procedure. For example, we can use it (along with a
procedure `inc` that increments its argument by 1) to define
`sum/cubes`:

::: scheme
(define (inc n) (+ n 1)) (define (sum-cubes a b) (sum cube a inc b))
:::

Using this, we can compute the sum of the cubes of the integers from 1
to 10:

::: scheme
(sum-cubes 1 10)  *3025*
:::

With the aid of an identity procedure to compute the term, we can define
`sum/integers` in terms of `sum`:

::: scheme
(define (identity x) x) (define (sum-integers a b) (sum identity a inc
b))
:::

Then we can add up the integers from 1 to 10:

::: scheme
(sum-integers 1 10)  *55*
:::

We can also define `pi/sum` in the same way:[^50]

::: scheme
(define (pi-sum a b) (define (pi-term x) (/ 1.0 (\* x (+ x 2)))) (define
(pi-next x) (+ x 4)) (sum pi-term a pi-next b))
:::

Using these procedures, we can compute an approximation to $\pi$:

::: scheme
(\* 8 (pi-sum 1 1000))  *3.139592655589783*
:::

Once we have `sum`, we can use it as a building block in formulating
further concepts. For instance, the definite integral of a function $f$
between the limits $a$ and $b$ can be approximated numerically using the
formula

$${\int_a^b \!\!\! f} = {\left[\;f\! \left(a + {dx \over 2}\right)
        + f\! \left(a + dx + {dx \over 2}\right)
        + f\! \left(a + 2dx + {dx \over 2}\right) + \,\dots \;\right]\! dx}$$

for small values of $dx$. We can express this directly as a procedure:

::: scheme
(define (integral f a b dx) (define (add-dx x) (+ x dx)) (\* (sum f (+ a
(/ dx 2.0)) add-dx b) dx))

(integral cube 0 1 0.01)  *.24998750000000042*

(integral cube 0 1 0.001)  *.249999875000001*
:::

(The exact value of the integral of `cube` between 0 and 1 is 1/4.)

> **[]{#Exercise 1.29 label="Exercise 1.29"}Exercise 1.29:** Simpson's
> Rule is a more accurate method of numerical integration than the
> method illustrated above. Using Simpson's Rule, the integral of a
> function $f$ between $a$ and $b$ is approximated as
>
> $${h\over 3}(y_0 + 4y_1 + 2y_2 + 4y_3 + 2y_4 + \dots + 2y_{n-2} + 4y_{n-1} + y_n),$$
>
> where $h = (b - a) / n$, for some even integer $n$, and
> $y_k = f(a + kh)$. (Increasing $n$ increases the accuracy of the
> approximation.) Define a procedure that takes as arguments $f$, $a$,
> $b$, and $n$ and returns the value of the integral, computed using
> Simpson's Rule. Use your procedure to integrate `cube` between 0 and 1
> (with $n = 100$ and $n = 1000$), and compare the results to those of
> the `integral` procedure shown above.

> **[]{#Exercise 1.30 label="Exercise 1.30"}Exercise 1.30:** The `sum`
> procedure above generates a linear recursion. The procedure can be
> rewritten so that the sum is performed iteratively. Show how to do
> this by filling in the missing expressions in the following
> definition:
>
> ::: scheme
> (define (sum term a next b) (define (iter a result) (if
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$  (iter
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ ))) (iter
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ ))
> :::

> **[]{#Exercise 1.31 label="Exercise 1.31"}Exercise 1.31:**
>
> a.  The `sum` procedure is only the simplest of a vast number of
>     similar abstractions that can be captured as higher-order
>     procedures.[^51] Write an analogous procedure called `product`
>     that returns the product of the values of a function at points
>     over a given range. Show how to define `factorial` in terms of
>     `product`. Also use `product` to compute approximations to $\pi$
>     using the formula[^52]
>
>     $${\pi\over 4} = {2\cdot 4\cdot 4\cdot 6\cdot 6\cdot 8\cdots\over
>                3\cdot 3\cdot 5\cdot 5\cdot 7\cdot 7\cdots}\,.$$
>
> b.  If your `product` procedure generates a recursive process, write
>     one that generates an iterative process. If it generates an
>     iterative process, write one that generates a recursive process.

> **[]{#Exercise 1.32 label="Exercise 1.32"}Exercise 1.32:**
>
> a.  Show that `sum` and `product` ([Exercise 1.31](#Exercise 1.31))
>     are both special cases of a still more general notion called
>     `accumulate` that combines a collection of terms, using some
>     general accumulation function:
>
>     ::: scheme
>     (accumulate combiner null-value term a next b)
>     :::
>
>     `accumulate` takes as arguments the same term and range
>     specifications as `sum` and `product`, together with a `combiner`
>     procedure (of two arguments) that specifies how the current term
>     is to be combined with the accumulation of the preceding terms and
>     a `null/value` that specifies what base value to use when the
>     terms run out. Write `accumulate` and show how `sum` and `product`
>     can both be defined as simple calls to `accumulate`.
>
> b.  If your `accumulate` procedure generates a recursive process,
>     write one that generates an iterative process. If it generates an
>     iterative process, write one that generates a recursive process.

> **[]{#Exercise 1.33 label="Exercise 1.33"}Exercise 1.33:** You can
> obtain an even more general version of `accumulate` ([Exercise
> 1.32](#Exercise 1.32)) by introducing the notion of a *filter* on the
> terms to be combined. That is, combine only those terms derived from
> values in the range that satisfy a specified condition. The resulting
> `filtered/accumulate` abstraction takes the same arguments as
> accumulate, together with an additional predicate of one argument that
> specifies the filter. Write `filtered/accumulate` as a procedure. Show
> how to express the following using `filtered/accumulate`:
>
> a.  the sum of the squares of the prime numbers in the interval $a$ to
>     $b$ (assuming that you have a `prime?` predicate already written)
>
> b.  the product of all the positive integers less than $n$ that are
>     relatively prime to $n$ (i.e., all positive integers $i < n$ such
>     that $\textsc{gcd}(i, n) = 1$).

### Constructing Procedures Using `lambda` {#Section 1.3.2}

In using `sum` as in [Section 1.3.1](#Section 1.3.1), it seems terribly
awkward to have to define trivial procedures such as `pi/term` and
`pi/next` just so we can use them as arguments to our higher-order
procedure. Rather than define `pi/next` and `pi/term`, it would be more
convenient to have a way to directly specify "the procedure that returns
its input incremented by 4" and "the procedure that returns the
reciprocal of its input times its input plus 2." We can do this by
introducing the special form `lambda`, which creates procedures. Using
`lambda` we can describe what we want as

::: scheme
(lambda (x) (+ x 4))
:::

and

::: scheme
(lambda (x) (/ 1.0 (\* x (+ x 2))))
:::

Then our `pi/sum` procedure can be expressed without defining any
auxiliary procedures as

::: scheme
(define (pi-sum a b) (sum (lambda (x) (/ 1.0 (\* x (+ x 2)))) a (lambda
(x) (+ x 4)) b))
:::

Again using `lambda`, we can write the `integral` procedure without
having to define the auxiliary procedure `add/dx`:

::: scheme
(define (integral f a b dx) (\* (sum f (+ a (/ dx 2.0)) (lambda (x) (+ x
dx)) b) dx))
:::

In general, `lambda` is used to create procedures in the same way as
`define`, except that no name is specified for the procedure:

::: scheme
(lambda
( $\color{SchemeDark}\langle$  *formal-parameters*  $\color{SchemeDark}\rangle$ )
 $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
:::

The resulting procedure is just as much a procedure as one that is
created using `define`. The only difference is that it has not been
associated with any name in the environment. In fact,

::: scheme
(define (plus4 x) (+ x 4))
:::

is equivalent to

::: scheme
(define plus4 (lambda (x) (+ x 4)))
:::

We can read a `lambda` expression as follows:

::: scheme
(lambda (x) (+ x 4)) \| \| \| \| \| the procedure of an argument x that
adds x and 4
:::

Like any expression that has a procedure as its value, a `lambda`
expression can be used as the operator in a combination such as

::: scheme
((lambda (x y z) (+ x y (square z))) 1 2 3)  *12*
:::

or, more generally, in any context where we would normally use a
procedure name.[^53]

#### Using `let` to create local variables {#using-let-to-create-local-variables .unnumbered}

Another use of `lambda` is in creating local variables. We often need
local variables in our procedures other than those that have been bound
as formal parameters. For example, suppose we wish to compute the
function

$$f(x,y) = x(1 + xy)^2 + y(1 - y) + (1 + xy)(1 - y),$$

which we could also express as

$$\begin{array}{r@{{}={}}l}
  a       &  1 + xy, \\
  b       &  1 - y,  \\
  f(x,y)  &  xa^2 + yb + ab.
\end{array}$$

In writing a procedure to compute $f$, we would like to include as local
variables not only $x$ and $y$ but also the names of intermediate
quantities like $a$ and $b$. One way to accomplish this is to use an
auxiliary procedure to bind the local variables:

::: scheme
(define (f x y) (define (f-helper a b) (+ (\* x (square a)) (\* y b) (\*
a b))) (f-helper (+ 1 (\* x y)) (- 1 y)))
:::

Of course, we could use a `lambda` expression to specify an anonymous
procedure for binding our local variables. The body of `f` then becomes
a single call to that procedure:

::: scheme
(define (f x y) ((lambda (a b) (+ (\* x (square a)) (\* y b) (\* a b)))
(+ 1 (\* x y)) (- 1 y)))
:::

This construct is so useful that there is a special form called `let` to
make its use more convenient. Using `let`, the `f` procedure could be
written as

::: scheme
(define (f x y) (let ((a (+ 1 (\* x y))) (b (- 1 y))) (+ (\* x (square
a)) (\* y b) (\* a b))))
:::

The general form of a `let` expression is

::: scheme
(let
(( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$ )
( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ )
 $\dots$
( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ ))
 $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
:::

which can be thought of as saying

::: scheme
 let
 $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 have the value
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 and
 $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
 have the value
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
 and   $\dots$
 $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$
 have the value
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$
 in   $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$
:::

The first part of the `let` expression is a list of name-expression
pairs. When the `let` is evaluated, each name is associated with the
value of the corresponding expression. The body of the `let` is
evaluated with these names bound as local variables. The way this
happens is that the `let` expression is interpreted as an alternate
syntax for

::: scheme
((lambda
( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
 $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
:::

No new mechanism is required in the interpreter in order to provide
local variables. A `let` expression is simply syntactic sugar for the
underlying `lambda` application.

We can see from this equivalence that the scope of a variable specified
by a `let` expression is the body of the `let`. This implies that:

-   `let` allows one to bind variables as locally as possible to where
    they are to be used. For example, if the value of `x` is 5, the
    value of the expression

    ::: scheme
    (+ (let ((x 3)) (+ x (\* x 10))) x)
    :::

    is 38. Here, the `x` in the body of the `let` is 3, so the value of
    the `let` expression is 33. On the other hand, the `x` that is the
    second argument to the outermost `+` is still 5.

-   The variables' values are computed outside the `let`. This matters
    when the expressions that provide the values for the local variables
    depend upon variables having the same names as the local variables
    themselves. For example, if the value of `x` is 2, the expression

    ::: scheme
    (let ((x 3) (y (+ x 2))) (\* x y))
    :::

    will have the value 12 because, inside the body of the `let`, `x`
    will be 3 and `y` will be 4 (which is the outer `x` plus 2).

Sometimes we can use internal definitions to get the same effect as with
`let`. For example, we could have defined the procedure `f` above as

::: scheme
(define (f x y) (define a (+ 1 (\* x y))) (define b (- 1 y)) (+ (\* x
(square a)) (\* y b) (\* a b)))
:::

We prefer, however, to use `let` in situations like this and to use
internal `define` only for internal procedures.[^54]

> **[]{#Exercise 1.34 label="Exercise 1.34"}Exercise 1.34:** Suppose we
> define the procedure
>
> ::: scheme
> (define (f g) (g 2))
> :::
>
> Then we have
>
> ::: scheme
> (f square)  *4*  (f (lambda (z) (\* z (+ z 1))))  *6*
> :::
>
> What happens if we (perversely) ask the interpreter to evaluate the
> combination `(f f)`? Explain.

### Procedures as General Methods {#Section 1.3.3}

We introduced compound procedures in [Section 1.1.4](#Section 1.1.4) as
a mechanism for abstracting patterns of numerical operations so as to
make them independent of the particular numbers involved. With
higher-order procedures, such as the `integral` procedure of [Section
1.3.1](#Section 1.3.1), we began to see a more powerful kind of
abstraction: procedures used to express general methods of computation,
independent of the particular functions involved. In this section we
discuss two more elaborate examples---general methods for finding zeros
and fixed points of functions---and show how these methods can be
expressed directly as procedures.

#### Finding roots of equations by the half-interval method {#finding-roots-of-equations-by-the-half-interval-method .unnumbered}

The *half-interval method* is a simple but powerful technique for
finding roots of an equation $f(x) = 0$, where $f$ is a continuous
function. The idea is that, if we are given points $a$ and $b$ such that
$f(a) < 0 < f(b)$, then $f$ must have at least one zero between $a$ and
$b$. To locate a zero, let $x$ be the average of $a$ and $b$, and
compute $f(x)$. If $f(x) > 0$, then $f$ must have a zero between $a$ and
$x$. If $f(x) < 0$, then $f$ must have a zero between $x$ and $b$.
Continuing in this way, we can identify smaller and smaller intervals on
which $f$ must have a zero. When we reach a point where the interval is
small enough, the process stops. Since the interval of uncertainty is
reduced by half at each step of the process, the number of steps
required grows as $\Theta(\log(L / T))$, where $L$ is the length of the
original interval and $T$ is the error tolerance (that is, the size of
the interval we will consider "small enough"). Here is a procedure that
implements this strategy:

::: scheme
(define (search f neg-point pos-point) (let ((midpoint (average
neg-point pos-point))) (if (close-enough? neg-point pos-point) midpoint
(let ((test-value (f midpoint))) (cond ((positive? test-value) (search f
neg-point midpoint)) ((negative? test-value) (search f midpoint
pos-point)) (else midpoint))))))
:::

We assume that we are initially given the function $f$ together with
points at which its values are negative and positive. We first compute
the midpoint of the two given points. Next we check to see if the given
interval is small enough, and if so we simply return the midpoint as our
answer. Otherwise, we compute as a test value the value of $f$ at the
midpoint. If the test value is positive, then we continue the process
with a new interval running from the original negative point to the
midpoint. If the test value is negative, we continue with the interval
from the midpoint to the positive point. Finally, there is the
possibility that the test value is 0, in which case the midpoint is
itself the root we are searching for.

To test whether the endpoints are "close enough" we can use a procedure
similar to the one used in [Section 1.1.7](#Section 1.1.7) for computing
square roots:[^55]

::: scheme
(define (close-enough? x y) (\< (abs (- x y)) 0.001))
:::

`search` is awkward to use directly, because we can accidentally give it
points at which $f$'s values do not have the required sign, in which
case we get a wrong answer. Instead we will use `search` via the
following procedure, which checks to see which of the endpoints has a
negative function value and which has a positive value, and calls the
`search` procedure accordingly. If the function has the same sign on the
two given points, the half-interval method cannot be used, in which case
the procedure signals an error.[^56]

::: scheme
(define (half-interval-method f a b) (let ((a-value (f a)) (b-value (f
b))) (cond ((and (negative? a-value) (positive? b-value)) (search f a
b)) ((and (negative? b-value) (positive? a-value)) (search f b a)) (else
(error \"Values are not of opposite sign\" a b)))))
:::

The following example uses the half-interval method to approximate $\pi$
as the root between 2 and 4 of $\sin x = 0$:

::: scheme
(half-interval-method sin 2.0 4.0)  *3.14111328125*
:::

Here is another example, using the half-interval method to search for a
root of the equation $x^3 - 2x - 3 = 0$ between 1 and 2:

::: scheme
(half-interval-method (lambda (x) (- (\* x x x) (\* 2 x) 3)) 1.0 2.0)
 *1.89306640625*
:::

#### Finding fixed points of functions {#finding-fixed-points-of-functions .unnumbered}

A number $x$ is called a *fixed point* of a function $f$ if $x$
satisfies the equation $f(x) = x$. For some functions $f$ we can locate
a fixed point by beginning with an initial guess and applying $f$
repeatedly,

$$f(x),\quad f(f(x)),\quad f(f(f(x))), \quad\dots,$$

until the value does not change very much. Using this idea, we can
devise a procedure `fixed/point` that takes as inputs a function and an
initial guess and produces an approximation to a fixed point of the
function. We apply the function repeatedly until we find two successive
values whose difference is less than some prescribed tolerance:

::: scheme
(define tolerance 0.00001) (define (fixed-point f first-guess) (define
(close-enough? v1 v2) (\< (abs (- v1 v2)) tolerance)) (define (try
guess) (let ((next (f guess))) (if (close-enough? guess next) next (try
next)))) (try first-guess))
:::

For example, we can use this method to approximate the fixed point of
the cosine function, starting with 1 as an initial approximation:[^57]

::: scheme
(fixed-point cos 1.0)  *.7390822985224023*
:::

Similarly, we can find a solution to the equation $y = \sin y + \cos y$:

::: scheme
(fixed-point (lambda (y) (+ (sin y) (cos y))) 1.0)
 *1.2587315962971173*
:::

The fixed-point process is reminiscent of the process we used for
finding square roots in [Section 1.1.7](#Section 1.1.7). Both are based
on the idea of repeatedly improving a guess until the result satisfies
some criterion. In fact, we can readily formulate the square-root
computation as a fixed-point search. Computing the square root of some
number $x$ requires finding a $y$ such that $y^2 = x$. Putting this
equation into the equivalent form $y = x / y$, we recognize that we are
looking for a fixed point of the function[^58] $y \mapsto x / y$, and we
can therefore try to compute square roots as

::: scheme
(define (sqrt x) (fixed-point (lambda (y) (/ x y)) 1.0))
:::

Unfortunately, this fixed-point search does not converge. Consider an
initial guess $y_1$. The next guess is $y_2 = x / y_1$ and the next
guess is $y_3 = x / y_2 = x / (x / y_1) = y_1$. This results in an
infinite loop in which the two guesses $y_1$ and $y_2$ repeat over and
over, oscillating about the answer.

One way to control such oscillations is to prevent the guesses from
changing so much. Since the answer is always between our guess $y$ and
$x / y$, we can make a new guess that is not as far from $y$ as $x / y$
by averaging $y$ with $x / y$, so that the next guess after $y$ is
${1\over2}(y + x / y)$ instead of $x / y$. The process of making such a
sequence of guesses is simply the process of looking for a fixed point
of $y \mapsto {1\over2}(y + x / y)$:

::: scheme
(define (sqrt x) (fixed-point (lambda (y) (average y (/ x y))) 1.0))
:::

(Note that $y = {1\over2}(y + x / y)$ is a simple transformation of the
equation $y = x / y;$ to derive it, add $y$ to both sides of the
equation and divide by 2.)

With this modification, the square-root procedure works. In fact, if we
unravel the definitions, we can see that the sequence of approximations
to the square root generated here is precisely the same as the one
generated by our original square-root procedure of [Section
1.1.7](#Section 1.1.7). This approach of averaging successive
approximations to a solution, a technique that we call *average
damping*, often aids the convergence of fixed-point searches.

> **[]{#Exercise 1.35 label="Exercise 1.35"}Exercise 1.35:** Show that
> the golden ratio $\varphi$ ([Section 1.2.2](#Section 1.2.2)) is a
> fixed point of the transformation $x \mapsto 1 + 1 / x$, and use this
> fact to compute $\varphi$ by means of the `fixed/point` procedure.

> **[]{#Exercise 1.36 label="Exercise 1.36"}Exercise 1.36:** Modify
> `fixed/point` so that it prints the sequence of approximations it
> generates, using the `newline` and `display` primitives shown in
> [Exercise 1.22](#Exercise 1.22). Then find a solution to $x^x = 1000$
> by finding a fixed point of $x \mapsto
> \log(1000) / \log(x)$. (Use Scheme's primitive `log` procedure, which
> computes natural logarithms.) Compare the number of steps this takes
> with and without average damping. (Note that you cannot start
> `fixed/point` with a guess of 1, as this would cause division by
> $\log(1) = 0$.)

> **[]{#Exercise 1.37 label="Exercise 1.37"}Exercise 1.37:**
>
> a.  An infinite *continued fraction* is an expression of the form
>
>     $${f} = \cfrac{N_1}{D_1 + \cfrac{N_2}{D_2 + \cfrac{N_3}{D_3 + \dots}}}\,.$$
>
>     As an example, one can show that the infinite continued fraction
>     expansion with the $N_i$ and the $D_i$ all equal to 1 produces
>     $1 / \varphi$, where $\varphi$ is the golden ratio (described in
>     [Section 1.2.2](#Section 1.2.2)). One way to approximate an
>     infinite continued fraction is to truncate the expansion after a
>     given number of terms. Such a truncation---a so-called **k*-term
>     finite continued fraction*---has the form
>
>     $$\cfrac{N_1}{D_1 + \cfrac{N_2}{\ddots + \cfrac{N_k}{D_k}}}\,.$$
>
>     Suppose that `n` and `d` are procedures of one argument (the term
>     index $i$) that return the $N_i$ and $D_i$ of the terms of the
>     continued fraction. Define a procedure `cont/frac` such that
>     evaluating `(cont/frac n d k)` computes the value of the $k$-term
>     finite continued fraction. Check your procedure by approximating
>     $1 / \varphi$ using
>
>     ::: scheme
>     (cont-frac (lambda (i) 1.0) (lambda (i) 1.0) k)
>     :::
>
>     for successive values of `k`. How large must you make `k` in order
>     to get an approximation that is accurate to 4 decimal places?
>
> b.  If your `cont/frac` procedure generates a recursive process, write
>     one that generates an iterative process. If it generates an
>     iterative process, write one that generates a recursive process.

> **[]{#Exercise 1.38 label="Exercise 1.38"}Exercise 1.38:** In 1737,
> the Swiss mathematician Leonhard Euler published a memoir *De
> Fractionibus Continuis*, which included a continued fraction expansion
> for $e - 2$, where $e$ is the base of the natural logarithms. In this
> fraction, the $N_i$ are all 1, and the $D_i$ are successively 1, 2, 1,
> 1, 4, 1, 1, 6, 1, 1, 8, $\dots$. Write a program that uses your
> `cont/frac` procedure from [Exercise 1.37](#Exercise 1.37) to
> approximate $e$, based on Euler's expansion.

> **[]{#Exercise 1.39 label="Exercise 1.39"}Exercise 1.39:** A continued
> fraction representation of the tangent function was published in 1770
> by the German mathematician J.H. Lambert:
>
> $${\tan x} = \cfrac{x}{1 - \cfrac{x^2}{3 - \cfrac{x^2}{5 - \dots}}}\,,$$
>
> where $x$ is in radians. Define a procedure `(tan/cf x k)` that
> computes an approximation to the tangent function based on Lambert's
> formula. `k` specifies the number of terms to compute, as in [Exercise
> 1.37](#Exercise 1.37).

### Procedures as Returned Values {#Section 1.3.4}

The above examples demonstrate how the ability to pass procedures as
arguments significantly enhances the expressive power of our programming
language. We can achieve even more expressive power by creating
procedures whose returned values are themselves procedures.

We can illustrate this idea by looking again at the fixed-point example
described at the end of [Section 1.3.3](#Section 1.3.3). We formulated a
new version of the square-root procedure as a fixed-point search,
starting with the observation that $\sqrt{x}$ is a fixed-point of the
function $y \mapsto
x / y$. Then we used average damping to make the approximations
converge. Average damping is a useful general technique in itself.
Namely, given a function $f$, we consider the function whose value at
$x$ is equal to the average of $x$ and $f(x)$.

We can express the idea of average damping by means of the following
procedure:

::: scheme
(define (average-damp f) (lambda (x) (average x (f x))))
:::

`average/damp` is a procedure that takes as its argument a procedure `f`
and returns as its value a procedure (produced by the `lambda`) that,
when applied to a number `x`, produces the average of `x` and `(f x)`.
For example, applying `average/damp` to the `square` procedure produces
a procedure whose value at some number $x$ is the average of $x$ and
$x^2$. Applying this resulting procedure to 10 returns the average of 10
and 100, or 55:[^59]

::: scheme
((average-damp square) 10)  *55*
:::

Using `average/damp`, we can reformulate the square-root procedure as
follows:

::: scheme
(define (sqrt x) (fixed-point (average-damp (lambda (y) (/ x y))) 1.0))
:::

Notice how this formulation makes explicit the three ideas in the
method: fixed-point search, average damping, and the function
$y \mapsto x / y$. It is instructive to compare this formulation of the
square-root method with the original version given in [Section
1.1.7](#Section 1.1.7). Bear in mind that these procedures express the
same process, and notice how much clearer the idea becomes when we
express the process in terms of these abstractions. In general, there
are many ways to formulate a process as a procedure. Experienced
programmers know how to choose procedural formulations that are
particularly perspicuous, and where useful elements of the process are
exposed as separate entities that can be reused in other applications.
As a simple example of reuse, notice that the cube root of $x$ is a
fixed point of the function $y \mapsto x / y^2$, so we can immediately
generalize our square-root procedure to one that extracts cube
roots:[^60]

::: scheme
(define (cube-root x) (fixed-point (average-damp (lambda (y) (/ x
(square y)))) 1.0))
:::

#### Newton's method {#newtons-method .unnumbered}

When we first introduced the square-root procedure, in [Section
1.1.7](#Section 1.1.7), we mentioned that this was a special case of
*Newton's method*. If $x
\mapsto g(x)$ is a differentiable function, then a solution of the
equation $g(x) = 0$ is a fixed point of the function $x \mapsto f(x)$,
where

$${f(x) = x} - {g(x)\over Dg(x)}$$

and $Dg(x)$ is the derivative of $g$ evaluated at $x$. Newton's method
is the use of the fixed-point method we saw above to approximate a
solution of the equation by finding a fixed point of the function
$f\!.$[^61]

For many functions $g$ and for sufficiently good initial guesses for
$x$, Newton's method converges very rapidly to a solution of
$g(x) = 0.$[^62]

In order to implement Newton's method as a procedure, we must first
express the idea of derivative. Note that "derivative," like average
damping, is something that transforms a function into another function.
For instance, the derivative of the function $x \mapsto x^3$ is the
function $x \mapsto 3x^2\!.$ In general, if $g$ is a function and $dx$
is a small number, then the derivative $Dg$ of $g$ is the function whose
value at any number $x$ is given (in the limit of small $dx$) by

$${Dg(x)} = {g(x + {\it dx}) - g(x) \over {\it dx}}\,.$$

Thus, we can express the idea of derivative (taking $dx$ to be, say,
0.00001) as the procedure

::: scheme
(define (deriv g) (lambda (x) (/ (- (g (+ x dx)) (g x)) dx)))
:::

along with the definition

::: scheme
(define dx 0.00001)
:::

Like `average/damp`, `deriv` is a procedure that takes a procedure as
argument and returns a procedure as value. For example, to approximate
the derivative of $x \mapsto x^3$ at 5 (whose exact value is 75) we can
evaluate

::: scheme
(define (cube x) (\* x x x)) ((deriv cube) 5)  *75.00014999664018*
:::

With the aid of `deriv`, we can express Newton's method as a fixed-point
process:

::: scheme
(define (newton-transform g) (lambda (x) (- x (/ (g x) ((deriv g) x)))))
(define (newtons-method g guess) (fixed-point (newton-transform g)
guess))
:::

The `newton/transform` procedure expresses the formula at the beginning
of this section, and `newtons/method` is readily defined in terms of
this. It takes as arguments a procedure that computes the function for
which we want to find a zero, together with an initial guess. For
instance, to find the square root of $x$, we can use Newton's method to
find a zero of the function $y \mapsto y^2 - x$ starting with an initial
guess of 1.[^63]

This provides yet another form of the square-root procedure:

::: scheme
(define (sqrt x) (newtons-method (lambda (y) (- (square y) x)) 1.0))
:::

#### Abstractions and first-class procedures {#abstractions-and-first-class-procedures .unnumbered}

We've seen two ways to express the square-root computation as an
instance of a more general method, once as a fixed-point search and once
using Newton's method. Since Newton's method was itself expressed as a
fixed-point process, we actually saw two ways to compute square roots as
fixed points. Each method begins with a function and finds a fixed point
of some transformation of the function. We can express this general idea
itself as a procedure:

::: scheme
(define (fixed-point-of-transform g transform guess) (fixed-point
(transform g) guess))
:::

This very general procedure takes as its arguments a procedure `g` that
computes some function, a procedure that transforms `g`, and an initial
guess. The returned result is a fixed point of the transformed function.

Using this abstraction, we can recast the first square-root computation
from this section (where we look for a fixed point of the average-damped
version of $y \mapsto x / y$) as an instance of this general method:

::: scheme
(define (sqrt x) (fixed-point-of-transform (lambda (y) (/ x y))
average-damp 1.0))
:::

Similarly, we can express the second square-root computation from this
section (an instance of Newton's method that finds a fixed point of the
Newton transform of $y \mapsto y^2 - x$) as

::: scheme
(define (sqrt x) (fixed-point-of-transform (lambda (y) (- (square y) x))
newton-transform 1.0))
:::

We began [Section 1.3](#Section 1.3) with the observation that compound
procedures are a crucial abstraction mechanism, because they permit us
to express general methods of computing as explicit elements in our
programming language. Now we've seen how higher-order procedures permit
us to manipulate these general methods to create further abstractions.

As programmers, we should be alert to opportunities to identify the
underlying abstractions in our programs and to build upon them and
generalize them to create more powerful abstractions. This is not to say
that one should always write programs in the most abstract way possible;
expert programmers know how to choose the level of abstraction
appropriate to their task. But it is important to be able to think in
terms of these abstractions, so that we can be ready to apply them in
new contexts. The significance of higher-order procedures is that they
enable us to represent these abstractions explicitly as elements in our
programming language, so that they can be handled just like other
computational elements.

In general, programming languages impose restrictions on the ways in
which computational elements can be manipulated. Elements with the
fewest restrictions are said to have *first-class* status. Some of the
"rights and privileges" of first-class elements are:[^64]

-   They may be named by variables.

-   They may be passed as arguments to procedures.

-   They may be returned as the results of procedures.

-   They may be included in data structures.[^65]

Lisp, unlike other common programming languages, awards procedures full
first-class status. This poses challenges for efficient implementation,
but the resulting gain in expressive power is enormous.[^66]

> **[]{#Exercise 1.40 label="Exercise 1.40"}Exercise 1.40:** Define a
> procedure `cubic` that can be used together with the `newtons/method`
> procedure in expressions of the form
>
> ::: scheme
> (newtons-method (cubic a b c) 1)
> :::
>
> to approximate zeros of the cubic $x^3 + ax^2 + bx + c$.

> **[]{#Exercise 1.41 label="Exercise 1.41"}Exercise 1.41:** Define a
> procedure `double` that takes a procedure of one argument as argument
> and returns a procedure that applies the original procedure twice. For
> example, if `inc` is a procedure that adds 1 to its argument, then
> `(double inc)` should be a procedure that adds 2. What value is
> returned by
>
> ::: scheme
> (((double (double double)) inc) 5)
> :::

> **[]{#Exercise 1.42 label="Exercise 1.42"}Exercise 1.42:** Let $f$ and
> $g$ be two one-argument functions. The *composition* $f$ after $g$ is
> defined to be the function $x \mapsto f(g(x))$. Define a procedure
> `compose` that implements composition. For example, if `inc` is a
> procedure that adds 1 to its argument,
>
> ::: scheme
> ((compose square inc) 6)  *49*
> :::

> **[]{#Exercise 1.43 label="Exercise 1.43"}Exercise 1.43:** If $f$ is a
> numerical function and $n$ is a positive integer, then we can form the
> $n^{\mathrm{th}}$ repeated application of $f$, which is defined to be
> the function whose value at $x$ is $f(f(\dots (f(x))\dots ))$. For
> example, if $f$ is the function $x \mapsto x + 1$, then the
> $n^{\mathrm{th}}$ repeated application of $f$ is the function
> $x \mapsto x + n$. If $f$ is the operation of squaring a number, then
> the $n^{\mathrm{th}}$ repeated application of $f$ is the function that
> raises its argument to the $2^n$-th power. Write a procedure that
> takes as inputs a procedure that computes $f$ and a positive integer
> $n$ and returns the procedure that computes the $n^{\mathrm{th}}$
> repeated application of $f$. Your procedure should be able to be used
> as follows:
>
> ::: scheme
> ((repeated square 2) 5)  *625*
> :::
>
> Hint: You may find it convenient to use `compose` from [Exercise
> 1.42](#Exercise 1.42).

> **[]{#Exercise 1.44 label="Exercise 1.44"}Exercise 1.44:** The idea of
> *smoothing* a function is an important concept in signal processing.
> If $f$ is a function and $dx$ is some small number, then the smoothed
> version of $f$ is the function whose value at a point $x$ is the
> average of $f(x - dx)$, $f(x)$, and $f(x + dx)$. Write a procedure
> `smooth` that takes as input a procedure that computes $f$ and returns
> a procedure that computes the smoothed $f$. It is sometimes valuable
> to repeatedly smooth a function (that is, smooth the smoothed
> function, and so on) to obtain the **n*-fold smoothed function*. Show
> how to generate the *n*-fold smoothed function of any given function
> using `smooth` and `repeated` from [Exercise 1.43](#Exercise 1.43).

> **[]{#Exercise 1.45 label="Exercise 1.45"}Exercise 1.45:** We saw in
> [Section 1.3.3](#Section 1.3.3) that attempting to compute square
> roots by naively finding a fixed point of $y \mapsto x / y$ does not
> converge, and that this can be fixed by average damping. The same
> method works for finding cube roots as fixed points of the
> average-damped $y \mapsto x / y^2$. Unfortunately, the process does
> not work for fourth roots---a single average damp is not enough to
> make a fixed-point search for $y \mapsto x / y^3$ converge. On the
> other hand, if we average damp twice (i.e., use the average damp of
> the average damp of $y \mapsto x / y^3$) the fixed-point search does
> converge. Do some experiments to determine how many average damps are
> required to compute $n^{\mathrm{th}}$ roots as a fixed-point search
> based upon repeated average damping of $y \mapsto x / y^{n-1}$. Use
> this to implement a simple procedure for computing $n^{\mathrm{th}}$
> roots using `fixed/point`, `average/damp`, and the `repeated`
> procedure of [Exercise 1.43](#Exercise 1.43). Assume that any
> arithmetic operations you need are available as primitives.

> **[]{#Exercise 1.46 label="Exercise 1.46"}Exercise 1.46:** Several of
> the numerical methods described in this chapter are instances of an
> extremely general computational strategy known as *iterative
> improvement*. Iterative improvement says that, to compute something,
> we start with an initial guess for the answer, test if the guess is
> good enough, and otherwise improve the guess and continue the process
> using the improved guess as the new guess. Write a procedure
> `iterative/improve` that takes two procedures as arguments: a method
> for telling whether a guess is good enough and a method for improving
> a guess. `iterative/improve` should return as its value a procedure
> that takes a guess as argument and keeps improving the guess until it
> is good enough. Rewrite the `sqrt` procedure of [Section
> 1.1.7](#Section 1.1.7) and the `fixed/point` procedure of [Section
> 1.3.3](#Section 1.3.3) in terms of `iterative/improve`.

# Building Abstractions with Data {#Chapter 2}

> We now come to the decisive step of mathematical abstraction: we
> forget about what the symbols stand for. $\dots$\[The mathematician\]
> need not be idle; there are many operations which he may carry out
> with these symbols, without ever having to look at the things they
> stand for.
>
> ---Hermann Weyl, *The Mathematical Way of Thinking*

We concentrated in [Chapter 1](#Chapter 1) on
computational processes and on the role of procedures in program design.
We saw how to use primitive data (numbers) and primitive operations
(arithmetic operations), how to combine procedures to form compound
procedures through composition, conditionals, and the use of parameters,
and how to abstract procedures by using `define`. We saw that a
procedure can be regarded as a pattern for the local evolution of a
process, and we classified, reasoned about, and performed simple
algorithmic analyses of some common patterns for processes as embodied
in procedures. We also saw that higher-order procedures enhance the
power of our language by enabling us to manipulate, and thereby to
reason in terms of, general methods of computation. This is much of the
essence of programming.

In this chapter we are going to look at more complex data. All the
procedures in chapter 1 operate on simple numerical data, and simple
data are not sufficient for many of the problems we wish to address
using computation. Programs are typically designed to model complex
phenomena, and more often than not one must construct computational
objects that have several parts in order to model real-world phenomena
that have several aspects. Thus, whereas our focus in chapter 1 was on
building abstractions by combining procedures to form compound
procedures, we turn in this chapter to another key aspect of any
programming language: the means it provides for building abstractions by
combining data objects to form *compound data*.

Why do we want compound data in a programming language? For the same
reasons that we want compound procedures: to elevate the conceptual
level at which we can design our programs, to increase the modularity of
our designs, and to enhance the expressive power of our language. Just
as the ability to define procedures enables us to deal with processes at
a higher conceptual level than that of the primitive operations of the
language, the ability to construct compound data objects enables us to
deal with data at a higher conceptual level than that of the primitive
data objects of the language.

Consider the task of designing a system to perform arithmetic with
rational numbers. We could imagine an operation `add/rat` that takes two
rational numbers and produces their sum. In terms of simple data, a
rational number can be thought of as two integers: a numerator and a
denominator. Thus, we could design a program in which each rational
number would be represented by two integers (a numerator and a
denominator) and where `add/rat` would be implemented by two procedures
(one producing the numerator of the sum and one producing the
denominator). But this would be awkward, because we would then need to
explicitly keep track of which numerators corresponded to which
denominators. In a system intended to perform many operations on many
rational numbers, such bookkeeping details would clutter the programs
substantially, to say nothing of what they would do to our minds. It
would be much better if we could "glue together" a numerator and
denominator to form a pair---a *compound data object*---that our
programs could manipulate in a way that would be consistent with
regarding a rational number as a single conceptual unit.

The use of compound data also enables us to increase the modularity of
our programs. If we can manipulate rational numbers directly as objects
in their own right, then we can separate the part of our program that
deals with rational numbers per se from the details of how rational
numbers may be represented as pairs of integers. The general technique
of isolating the parts of a program that deal with how data objects are
represented from the parts of a program that deal with how data objects
are used is a powerful design methodology called *data abstraction*. We
will see how data abstraction makes programs much easier to design,
maintain, and modify.

The use of compound data leads to a real increase in the expressive
power of our programming language. Consider the idea of forming a
"linear combination" $ax + by$. We might like to write a procedure that
would accept $a$, $b$, $x$, and $y$ as arguments and return the value of
$ax + by$. This presents no difficulty if the arguments are to be
numbers, because we can readily define the procedure

::: scheme
(define (linear-combination a b x y) (+ (\* a x) (\* b y)))
:::

But suppose we are not concerned only with numbers. Suppose we would
like to express, in procedural terms, the idea that one can form linear
combinations whenever addition and multiplication are defined---for
rational numbers, complex numbers, polynomials, or whatever. We could
express this as a procedure of the form

::: scheme
(define (linear-combination a b x y) (add (mul a x) (mul b y)))
:::

where `add` and `mul` are not the primitive procedures `+` and `*` but
rather more complex things that will perform the appropriate operations
for whatever kinds of data we pass in as the arguments `a`, `b`, `x`,
and `y`. The key point is that the only thing `linear/combination`
should need to know about `a`, `b`, `x`, and `y` is that the procedures
`add` and `mul` will perform the appropriate manipulations. From the
perspective of the procedure `linear/combination`, it is irrelevant what
`a`, `b`, `x`, and `y` are and even more irrelevant how they might
happen to be represented in terms of more primitive data. This same
example shows why it is important that our programming language provide
the ability to manipulate compound objects directly: Without this, there
is no way for a procedure such as `linear/combination` to pass its
arguments along to `add` and `mul` without having to know their detailed
structure.[^67]

We begin this chapter by implementing the rational-number arithmetic
system mentioned above. This will form the background for our discussion
of compound data and data abstraction. As with compound procedures, the
main issue to be addressed is that of abstraction as a technique for
coping with complexity, and we will see how data abstraction enables us
to erect suitable *abstraction barriers* between different parts of a
program.

We will see that the key to forming compound data is that a programming
language should provide some kind of "glue" so that data objects can be
combined to form more complex data objects. There are many possible
kinds of glue. Indeed, we will discover how to form compound data using
no special "data" operations at all, only procedures. This will further
blur the distinction between "procedure" and "data," which was already
becoming tenuous toward the end of chapter 1. We will also explore some
conventional techniques for representing sequences and trees. One key
idea in dealing with compound data is the notion of *closure*---that the
glue we use for combining data objects should allow us to combine not
only primitive data objects, but compound data objects as well. Another
key idea is that compound data objects can serve as *conventional
interfaces* for combining program modules in mix-and-match ways. We
illustrate some of these ideas by presenting a simple graphics language
that exploits closure.

We will then augment the representational power of our language by
introducing *symbolic expressions*---data whose elementary parts can be
arbitrary symbols rather than only numbers. We explore various
alternatives for representing sets of objects. We will find that, just
as a given numerical function can be computed by many different
computational processes, there are many ways in which a given data
structure can be represented in terms of simpler objects, and the choice
of representation can have significant impact on the time and space
requirements of processes that manipulate the data. We will investigate
these ideas in the context of symbolic differentiation, the
representation of sets, and the encoding of information.

Next we will take up the problem of working with data that may be
represented differently by different parts of a program. This leads to
the need to implement *generic operations*, which must handle many
different types of data. Maintaining modularity in the presence of
generic operations requires more powerful abstraction barriers than can
be erected with simple data abstraction alone. In particular, we
introduce *data-directed programming* as a technique that allows
individual data representations to be designed in isolation and then
combined *additively* (i.e., without modification). To illustrate the
power of this approach to system design, we close the chapter by
applying what we have learned to the implementation of a package for
performing symbolic arithmetic on polynomials, in which the coefficients
of the polynomials can be integers, rational numbers, complex numbers,
and even other polynomials.

## Introduction to Data Abstraction {#Section 2.1}

In [Section 1.1.8](#Section 1.1.8), we noted that a procedure used as an
element in creating a more complex procedure could be regarded not only
as a collection of particular operations but also as a procedural
abstraction. That is, the details of how the procedure was implemented
could be suppressed, and the particular procedure itself could be
replaced by any other procedure with the same overall behavior. In other
words, we could make an abstraction that would separate the way the
procedure would be used from the details of how the procedure would be
implemented in terms of more primitive procedures. The analogous notion
for compound data is called *data abstraction*. Data abstraction is a
methodology that enables us to isolate how a compound data object is
used from the details of how it is constructed from more primitive data
objects.

The basic idea of data abstraction is to structure the programs that are
to use compound data objects so that they operate on "abstract data."
That is, our programs should use data in such a way as to make no
assumptions about the data that are not strictly necessary for
performing the task at hand. At the same time, a "concrete" data
representation is defined independent of the programs that use the data.
The interface between these two parts of our system will be a set of
procedures, called *selectors* and *constructors*, that implement the
abstract data in terms of the concrete representation. To illustrate
this technique, we will consider how to design a set of procedures for
manipulating rational numbers.

### Example: Arithmetic Operations for Rational Numbers {#Section 2.1.1}

Suppose we want to do arithmetic with rational numbers. We want to be
able to add, subtract, multiply, and divide them and to test whether two
rational numbers are equal.

Let us begin by assuming that we already have a way of constructing a
rational number from a numerator and a denominator. We also assume that,
given a rational number, we have a way of extracting (or selecting) its
numerator and its denominator. Let us further assume that the
constructor and selectors are available as procedures:

-   $\hbox{\tt(make-rat}\;\langle{n}\rangle\;\langle{d}\kern0.06em\rangle\hbox{\tt)}$
    returns the rational number whose numerator is the integer
    $\langle{n}\rangle$ and whose denominator is the integer
    $\langle{d}\kern0.06em\rangle$.

-   $\hbox{\tt(numer}\;\;\langle{x}\rangle\hbox{\tt)}$ returns the
    numerator of the rational number $\langle{x}\rangle$.

-   $\hbox{\tt(denom}\;\;\langle{x}\rangle\hbox{\tt)}$ returns the
    denominator of the rational number $\langle{x}\rangle$.

We are using here a powerful strategy of synthesis: *wishful thinking*.
We haven't yet said how a rational number is represented, or how the
procedures `numer`, `denom`, and `make/rat` should be implemented. Even
so, if we did have these three procedures, we could then add, subtract,
multiply, divide, and test equality by using the following relations:

$$\begin{aligned}
  {n_1 \over d_1} + {n_2 \over d_2}     &= {n_1 d_2 + n_2 d_1 \over d_1 d_2}, \\
  {n_1 \over d_1} - {n_2 \over d_2}     &= {n_1 d_2 - n_2 d_1 \over d_1 d_2}, \\
  {n_1 \over d_1} \cdot {n_2 \over d_2} &= {n_1 n_2 \over d_1 d_2}, \\
  {n_1 / d_1} \over {n_2 / d_2}     &= {n_1 d_2 \over d_1 n_2}, \\
  {n_1 \over d_1}           &= {n_2 \over d_2} \quad
                        {\rm\ if\ and\ only\ if\quad}
                        n_1 d_2 = n_2 d_1.
\end{aligned}$$ We can express these rules as procedures:

::: scheme
(define (add-rat x y) (make-rat (+ (\* (numer x) (denom y)) (\* (numer
y) (denom x))) (\* (denom x) (denom y)))) (define (sub-rat x y)
(make-rat (- (\* (numer x) (denom y)) (\* (numer y) (denom x))) (\*
(denom x) (denom y)))) (define (mul-rat x y) (make-rat (\* (numer x)
(numer y)) (\* (denom x) (denom y)))) (define (div-rat x y) (make-rat
(\* (numer x) (denom y)) (\* (denom x) (numer y))))

(define (equal-rat? x y) (= (\* (numer x) (denom y)) (\* (numer y)
(denom x))))
:::

Now we have the operations on rational numbers defined in terms of the
selector and constructor procedures `numer`, `denom`, and `make/rat`.
But we haven't yet defined these. What we need is some way to glue
together a numerator and a denominator to form a rational number.

#### Pairs {#pairs .unnumbered}

To enable us to implement the concrete level of our data abstraction,
our language provides a compound structure called a *pair*, which can be
constructed with the primitive procedure `cons`. This procedure takes
two arguments and returns a compound data object that contains the two
arguments as parts. Given a pair, we can extract the parts using the
primitive procedures `car` and `cdr`.[^68] Thus, we can use `cons`,
`car`, and `cdr` as follows:

::: scheme
(define x (cons 1 2)) (car x)  *1*  (cdr x)  *2*
:::

Notice that a pair is a data object that can be given a name and
manipulated, just like a primitive data object. Moreover, `cons` can be
used to form pairs whose elements are pairs, and so on:

::: scheme
(define x (cons 1 2)) (define y (cons 3 4)) (define z (cons x y)) (car
(car z))  *1*  (car (cdr z))  *3*
:::

In [Section 2.2](#Section 2.2) we will see how this ability to combine
pairs means that pairs can be used as general-purpose building blocks to
create all sorts of complex data structures. The single compound-data
primitive *pair*, implemented by the procedures `cons`, `car`, and
`cdr`, is the only glue we need. Data objects constructed from pairs are
called *list-structured* data.

#### Representing rational numbers {#representing-rational-numbers .unnumbered}

Pairs offer a natural way to complete the rational-number system. Simply
represent a rational number as a pair of two integers: a numerator and a
denominator. Then `make/rat`, `numer`, and `denom` are readily
implemented as follows:[^69]

::: scheme
(define (make-rat n d) (cons n d)) (define (numer x) (car x)) (define
(denom x) (cdr x))
:::

Also, in order to display the results of our computations, we can print
rational numbers by printing the numerator, a slash, and the
denominator:[^70]

::: scheme
(define (print-rat x) (newline) (display (numer x)) (display \"/\")
(display (denom x)))
:::

Now we can try our rational-number procedures:

::: scheme
(define one-half (make-rat 1 2)) (print-rat one-half)  *1/2*  (define
one-third (make-rat 1 3)) (print-rat (add-rat one-half one-third))
 *5/6*  (print-rat (mul-rat one-half one-third))  *1/6*  (print-rat
(add-rat one-third one-third))  *6/9*
:::

As the final example shows, our rational-number implementation does not
reduce rational numbers to lowest terms. We can remedy this by changing
`make/rat`. If we have a `gcd` procedure like the one in [Section
1.2.5](#Section 1.2.5) that produces the greatest common divisor of two
integers, we can use `gcd` to reduce the numerator and the denominator
to lowest terms before constructing the pair:

::: scheme
(define (make-rat n d) (let ((g (gcd n d))) (cons (/ n g) (/ d g))))
:::

Now we have

::: scheme
(print-rat (add-rat one-third one-third))  *2/3*
:::

as desired. This modification was accomplished by changing the
constructor `make/rat` without changing any of the procedures (such as
`add/rat` and `mul/rat`) that implement the actual operations.

> **[]{#Exercise 2.1 label="Exercise 2.1"}Exercise 2.1:** Define a
> better version of `make/rat` that handles both positive and negative
> arguments. `make/rat` should normalize the sign so that if the
> rational number is positive, both the numerator and denominator are
> positive, and if the rational number is negative, only the numerator
> is negative.

### Abstraction Barriers {#Section 2.1.2}

Before continuing with more examples of compound data and data
abstraction, let us consider some of the issues raised by the
rational-number example. We defined the rational-number operations in
terms of a constructor `make/rat` and selectors `numer` and `denom`. In
general, the underlying idea of data abstraction is to identify for each
type of data object a basic set of operations in terms of which all
manipulations of data objects of that type will be expressed, and then
to use only those operations in manipulating the data.

[]{#Figure 2.1 label="Figure 2.1"}

![image](fig/chap2/Fig2.1c.pdf){width="91mm"}

> **Figure 2.1:** Data-abstraction barriers in the rational-number
> package.

We can envision the structure of the rational-number system as shown in
[Figure 2.1](#Figure 2.1). The horizontal lines represent *abstraction
barriers* that isolate different "levels" of the system. At each level,
the barrier separates the programs (above) that use the data abstraction
from the programs (below) that implement the data abstraction. Programs
that use rational numbers manipulate them solely in terms of the
procedures supplied "for public use" by the rational-number package:
`add/rat`, `sub/rat`, `mul/rat`, `div/rat`, and `equal/rat?`. These, in
turn, are implemented solely in terms of the constructor and selectors
`make/rat`, `numer`, and `denom`, which themselves are implemented in
terms of pairs. The details of how pairs are implemented are irrelevant
to the rest of the rational-number package so long as pairs can be
manipulated by the use of `cons`, `car`, and `cdr`. In effect,
procedures at each level are the interfaces that define the abstraction
barriers and connect the different levels.

This simple idea has many advantages. One advantage is that it makes
programs much easier to maintain and to modify. Any complex data
structure can be represented in a variety of ways with the primitive
data structures provided by a programming language. Of course, the
choice of representation influences the programs that operate on it;
thus, if the representation were to be changed at some later time, all
such programs might have to be modified accordingly. This task could be
time-consuming and expensive in the case of large programs unless the
dependence on the representation were to be confined by design to a very
few program modules.

For example, an alternate way to address the problem of reducing
rational numbers to lowest terms is to perform the reduction whenever we
access the parts of a rational number, rather than when we construct it.
This leads to different constructor and selector procedures:

::: scheme
(define (make-rat n d) (cons n d)) (define (numer x) (let ((g (gcd (car
x) (cdr x)))) (/ (car x) g))) (define (denom x) (let ((g (gcd (car x)
(cdr x)))) (/ (cdr x) g)))
:::

The difference between this implementation and the previous one lies in
when we compute the `gcd`. If in our typical use of rational numbers we
access the numerators and denominators of the same rational numbers many
times, it would be preferable to compute the `gcd` when the rational
numbers are constructed. If not, we may be better off waiting until
access time to compute the `gcd`. In any case, when we change from one
representation to the other, the procedures `add/rat`, `sub/rat`, and so
on do not have to be modified at all.

Constraining the dependence on the representation to a few interface
procedures helps us design programs as well as modify them, because it
allows us to maintain the flexibility to consider alternate
implementations. To continue with our simple example, suppose we are
designing a rational-number package and we can't decide initially
whether to perform the `gcd` at construction time or at selection time.
The data-abstraction methodology gives us a way to defer that decision
without losing the ability to make progress on the rest of the system.

> **[]{#Exercise 2.2 label="Exercise 2.2"}Exercise 2.2:** Consider the
> problem of representing line segments in a plane. Each segment is
> represented as a pair of points: a starting point and an ending point.
> Define a constructor `make/segment` and selectors `start/segment` and
> `end/segment` that define the representation of segments in terms of
> points. Furthermore, a point can be represented as a pair of numbers:
> the $x$ coordinate and the $y$ coordinate. Accordingly, specify a
> constructor `make/point` and selectors `x/point` and `y/point` that
> define this representation. Finally, using your selectors and
> constructors, define a procedure `midpoint/segment` that takes a line
> segment as argument and returns its midpoint (the point whose
> coordinates are the average of the coordinates of the endpoints). To
> try your procedures, you'll need a way to print points:
>
> ::: scheme
> (define (print-point p) (newline) (display \"(\") (display (x-point
> p)) (display \",\") (display (y-point p)) (display \")\"))
> :::

> **[]{#Exercise 2.3 label="Exercise 2.3"}Exercise 2.3:** Implement a
> representation for rectangles in a plane. (Hint: You may want to make
> use of [Exercise 2.2](#Exercise 2.2).) In terms of your constructors
> and selectors, create procedures that compute the perimeter and the
> area of a given rectangle. Now implement a different representation
> for rectangles. Can you design your system with suitable abstraction
> barriers, so that the same perimeter and area procedures will work
> using either representation?

### What Is Meant by Data? {#Section 2.1.3}

We began the rational-number implementation in [Section
2.1.1](#Section 2.1.1) by implementing the rational-number operations
`add/rat`, `sub/rat`, and so on in terms of three unspecified
procedures: `make/rat`, `numer`, and `denom`. At that point, we could
think of the operations as being defined in terms of data
objects---numerators, denominators, and rational numbers---whose
behavior was specified by the latter three procedures.

But exactly what is meant by *data*? It is not enough to say "whatever
is implemented by the given selectors and constructors." Clearly, not
every arbitrary set of three procedures can serve as an appropriate
basis for the rational-number implementation. We need to guarantee that,
if we construct a rational number `x` from a pair of integers `n` and
`d`, then extracting the `numer` and the `denom` of `x` and dividing
them should yield the same result as dividing `n` by `d`. In other
words, `make/rat`, `numer`, and `denom` must satisfy the condition that,
for any integer `n` and any non-zero integer `d`, if `x` is
`(make/rat n d)`, then

$${\hbox{\tt(numer x)} \over \hbox{\tt(denom x)}} = {{\tt n} \over {\tt d}}\,.$$

In fact, this is the only condition `make/rat`, `numer`, and `denom`
must fulfill in order to form a suitable basis for a rational-number
representation. In general, we can think of data as defined by some
collection of selectors and constructors, together with specified
conditions that these procedures must fulfill in order to be a valid
representation.[^71]

This point of view can serve to define not only "high-level" data
objects, such as rational numbers, but lower-level objects as well.
Consider the notion of a pair, which we used in order to define our
rational numbers. We never actually said what a pair was, only that the
language supplied procedures `cons`, `car`, and `cdr` for operating on
pairs. But the only thing we need to know about these three operations
is that if we glue two objects together using `cons` we can retrieve the
objects using `car` and `cdr`. That is, the operations satisfy the
condition that, for any objects `x` and `y`, if `z` is `(cons x y)` then
`(car z)` is `x` and `(cdr z)` is `y`. Indeed, we mentioned that these
three procedures are included as primitives in our language. However,
any triple of procedures that satisfies the above condition can be used
as the basis for implementing pairs. This point is illustrated
strikingly by the fact that we could implement `cons`, `car`, and `cdr`
without using any data structures at all but only using procedures. Here
are the definitions:

::: scheme
(define (cons x y) (define (dispatch m) (cond ((= m 0) x) ((= m 1) y)
(else (error \"Argument not 0 or 1: CONS\" m)))) dispatch) (define (car
z) (z 0)) (define (cdr z) (z 1))
:::

This use of procedures corresponds to nothing like our intuitive notion
of what data should be. Nevertheless, all we need to do to show that
this is a valid way to represent pairs is to verify that these
procedures satisfy the condition given above.

The subtle point to notice is that the value returned by `(cons x y)` is
a procedure---namely the internally defined procedure `dispatch`, which
takes one argument and returns either `x` or `y` depending on whether
the argument is 0 or 1. Correspondingly, `(car z)` is defined to apply
`z` to 0. Hence, if `z` is the procedure formed by `(cons x y)`, then
`z` applied to 0 will yield `x`. Thus, we have shown that
`(car (cons x y))` yields `x`, as desired. Similarly, `(cdr (cons x y))`
applies the procedure returned by `(cons x y)` to 1, which returns `y`.
Therefore, this procedural implementation of pairs is a valid
implementation, and if we access pairs using only `cons`, `car`, and
`cdr` we cannot distinguish this implementation from one that uses
"real" data structures.

The point of exhibiting the procedural representation of pairs is not
that our language works this way (Scheme, and Lisp systems in general,
implement pairs directly, for efficiency reasons) but that it could work
this way. The procedural representation, although obscure, is a
perfectly adequate way to represent pairs, since it fulfills the only
conditions that pairs need to fulfill. This example also demonstrates
that the ability to manipulate procedures as objects automatically
provides the ability to represent compound data. This may seem a
curiosity now, but procedural representations of data will play a
central role in our programming repertoire. This style of programming is
often called *message passing*, and we will be using it as a basic tool
in [Chapter 3](#Chapter 3) when we address the issues of modeling and
simulation.

> **[]{#Exercise 2.4 label="Exercise 2.4"}Exercise 2.4:** Here is an
> alternative procedural representation of pairs. For this
> representation, verify that `(car (cons x y))` yields `x` for any
> objects `x` and `y`.
>
> ::: scheme
> (define (cons x y) (lambda (m) (m x y))) (define (car z) (z (lambda (p
> q) p)))
> :::
>
> What is the corresponding definition of `cdr`? (Hint: To verify that
> this works, make use of the substitution model of [Section
> 1.1.5](#Section 1.1.5).)

> **[]{#Exercise 2.5 label="Exercise 2.5"}Exercise 2.5:** Show that we
> can represent pairs of nonnegative integers using only numbers and
> arithmetic operations if we represent the pair $a$ and $b$ as the
> integer that is the product $2^a 3^b$. Give the corresponding
> definitions of the procedures `cons`, `car`, and `cdr`.

> **[]{#Exercise 2.6 label="Exercise 2.6"}Exercise 2.6:** In case
> representing pairs as procedures wasn't mind-boggling enough, consider
> that, in a language that can manipulate procedures, we can get by
> without numbers (at least insofar as nonnegative integers are
> concerned) by implementing 0 and the operation of adding 1 as
>
> ::: scheme
> (define zero (lambda (f) (lambda (x) x))) (define (add-1 n) (lambda
> (f) (lambda (x) (f ((n f) x)))))
> :::
>
> This representation is known as *Church numerals*, after its inventor,
> Alonzo Church, the logician who invented the λ-calculus.
>
> Define `one` and `two` directly (not in terms of `zero` and `add/1`).
> (Hint: Use substitution to evaluate `(add/1 zero)`). Give a direct
> definition of the addition procedure `+` (not in terms of repeated
> application of `add/1`).

### Extended Exercise: Interval Arithmetic {#Section 2.1.4}

Alyssa P. Hacker is designing a system to help people solve engineering
problems. One feature she wants to provide in her system is the ability
to manipulate inexact quantities (such as measured parameters of
physical devices) with known precision, so that when computations are
done with such approximate quantities the results will be numbers of
known precision.

Electrical engineers will be using Alyssa's system to compute electrical
quantities. It is sometimes necessary for them to compute the value of a
parallel equivalent resistance $R_p$ of two resistors $R_1$, $R_2$ using
the formula

$$R_p = {1 \over 1 / R_1 + 1 / R_2}.$$

Resistance values are usually known only up to some tolerance guaranteed
by the manufacturer of the resistor. For example, if you buy a resistor
labeled "6.8 ohms with 10% tolerance" you can only be sure that the
resistor has a resistance between $6.8 - 0.68 = 6.12$ and
$6.8 + 0.68 = 7.48$ ohms. Thus, if you have a 6.8-ohm 10% resistor in
parallel with a 4.7-ohm 5% resistor, the resistance of the combination
can range from about 2.58 ohms (if the two resistors are at the lower
bounds) to about 2.97 ohms (if the two resistors are at the upper
bounds).

Alyssa's idea is to implement "interval arithmetic" as a set of
arithmetic operations for combining "intervals" (objects that represent
the range of possible values of an inexact quantity). The result of
adding, subtracting, multiplying, or dividing two intervals is itself an
interval, representing the range of the result.

Alyssa postulates the existence of an abstract object called an
"interval" that has two endpoints: a lower bound and an upper bound. She
also presumes that, given the endpoints of an interval, she can
construct the interval using the data constructor `make/interval`.
Alyssa first writes a procedure for adding two intervals. She reasons
that the minimum value the sum could be is the sum of the two lower
bounds and the maximum value it could be is the sum of the two upper
bounds:

::: scheme
(define (add-interval x y) (make-interval (+ (lower-bound x)
(lower-bound y)) (+ (upper-bound x) (upper-bound y))))
:::

Alyssa also works out the product of two intervals by finding the
minimum and the maximum of the products of the bounds and using them as
the bounds of the resulting interval. (`min` and `max` are primitives
that find the minimum or maximum of any number of arguments.)

::: scheme
(define (mul-interval x y) (let ((p1 (\* (lower-bound x) (lower-bound
y))) (p2 (\* (lower-bound x) (upper-bound y))) (p3 (\* (upper-bound x)
(lower-bound y))) (p4 (\* (upper-bound x) (upper-bound y))))
(make-interval (min p1 p2 p3 p4) (max p1 p2 p3 p4))))
:::

To divide two intervals, Alyssa multiplies the first by the reciprocal
of the second. Note that the bounds of the reciprocal interval are the
reciprocal of the upper bound and the reciprocal of the lower bound, in
that order.

::: scheme
(define (div-interval x y) (mul-interval x (make-interval (/ 1.0
(upper-bound y)) (/ 1.0 (lower-bound y)))))
:::

> **[]{#Exercise 2.7 label="Exercise 2.7"}Exercise 2.7:** Alyssa's
> program is incomplete because she has not specified the implementation
> of the interval abstraction. Here is a definition of the interval
> constructor:
>
> ::: scheme
> (define (make-interval a b) (cons a b))
> :::
>
> Define selectors `upper/bound` and `lower/bound` to complete the
> implementation.

> **[]{#Exercise 2.8 label="Exercise 2.8"}Exercise 2.8:** Using
> reasoning analogous to Alyssa's, describe how the difference of two
> intervals may be computed. Define a corresponding subtraction
> procedure, called `sub/interval`.

> **[]{#Exercise 2.9 label="Exercise 2.9"}Exercise 2.9:** The *width* of
> an interval is half of the difference between its upper and lower
> bounds. The width is a measure of the uncertainty of the number
> specified by the interval. For some arithmetic operations the width of
> the result of combining two intervals is a function only of the widths
> of the argument intervals, whereas for others the width of the
> combination is not a function of the widths of the argument intervals.
> Show that the width of the sum (or difference) of two intervals is a
> function only of the widths of the intervals being added (or
> subtracted). Give examples to show that this is not true for
> multiplication or division.

> **[]{#Exercise 2.10 label="Exercise 2.10"}Exercise 2.10:** Ben
> Bitdiddle, an expert systems programmer, looks over Alyssa's shoulder
> and comments that it is not clear what it means to divide by an
> interval that spans zero. Modify Alyssa's code to check for this
> condition and to signal an error if it occurs.

> **[]{#Exercise 2.11 label="Exercise 2.11"}Exercise 2.11:** In passing,
> Ben also cryptically comments: "By testing the signs of the endpoints
> of the intervals, it is possible to break `mul/interval` into nine
> cases, only one of which requires more than two multiplications."
> Rewrite this procedure using Ben's suggestion.
>
> After debugging her program, Alyssa shows it to a potential user, who
> complains that her program solves the wrong problem. He wants a
> program that can deal with numbers represented as a center value and
> an additive tolerance; for example, he wants to work with intervals
> such as $3.5 \pm 0.15$ rather than \[3.35, 3.65\]. Alyssa returns to
> her desk and fixes this problem by supplying an alternate constructor
> and alternate selectors:
>
> ::: scheme
> (define (make-center-width c w) (make-interval (- c w) (+ c w)))
> (define (center i) (/ (+ (lower-bound i) (upper-bound i)) 2)) (define
> (width i) (/ (- (upper-bound i) (lower-bound i)) 2))
> :::
>
> Unfortunately, most of Alyssa's users are engineers. Real engineering
> situations usually involve measurements with only a small uncertainty,
> measured as the ratio of the width of the interval to the midpoint of
> the interval. Engineers usually specify percentage tolerances on the
> parameters of devices, as in the resistor specifications given
> earlier.

> **[]{#Exercise 2.12 label="Exercise 2.12"}Exercise 2.12:** Define a
> constructor `make/center/percent` that takes a center and a percentage
> tolerance and produces the desired interval. You must also define a
> selector `percent` that produces the percentage tolerance for a given
> interval. The `center` selector is the same as the one shown above.

> **[]{#Exercise 2.13 label="Exercise 2.13"}Exercise 2.13:** Show that
> under the assumption of small percentage tolerances there is a simple
> formula for the approximate percentage tolerance of the product of two
> intervals in terms of the tolerances of the factors. You may simplify
> the problem by assuming that all numbers are positive.
>
> After considerable work, Alyssa P. Hacker delivers her finished
> system. Several years later, after she has forgotten all about it, she
> gets a frenzied call from an irate user, Lem E. Tweakit. It seems that
> Lem has noticed that the formula for parallel resistors can be written
> in two algebraically equivalent ways:
>
> $$R_1 R_2 \over R_1 + R_2$$
>
> and
>
> $${1 \over 1 / R_1 + 1 / R_2}.$$
>
> He has written the following two programs, each of which computes the
> parallel-resistors formula differently:
>
> ::: scheme
> (define (par1 r1 r2) (div-interval (mul-interval r1 r2) (add-interval
> r1 r2)))
> :::
>
> ::: scheme
> (define (par2 r1 r2) (let ((one (make-interval 1 1))) (div-interval
> one (add-interval (div-interval one r1) (div-interval one r2)))))
> :::
>
> Lem complains that Alyssa's program gives different answers for the
> two ways of computing. This is a serious complaint.

> **[]{#Exercise 2.14 label="Exercise 2.14"}Exercise 2.14:** Demonstrate
> that Lem is right. Investigate the behavior of the system on a variety
> of arithmetic expressions. Make some intervals $A$ and $B$, and use
> them in computing the expressions $A / A$ and $A / B$. You will get
> the most insight by using intervals whose width is a small percentage
> of the center value. Examine the results of the computation in
> center-percent form (see [Exercise 2.12](#Exercise 2.12)).

> **[]{#Exercise 2.15 label="Exercise 2.15"}Exercise 2.15:** Eva Lu
> Ator, another user, has also noticed the different intervals computed
> by different but algebraically equivalent expressions. She says that a
> formula to compute with intervals using Alyssa's system will produce
> tighter error bounds if it can be written in such a form that no
> variable that represents an uncertain number is repeated. Thus, she
> says, `par2` is a "better" program for parallel resistances than
> `par1`. Is she right? Why?

> **[]{#Exercise 2.16 label="Exercise 2.16"}Exercise 2.16:** Explain, in
> general, why equivalent algebraic expressions may lead to different
> answers. Can you devise an interval-arithmetic package that does not
> have this shortcoming, or is this task impossible? (Warning: This
> problem is very difficult.)

## Hierarchical Data and the Closure Property {#Section 2.2}

As we have seen, pairs provide a primitive "glue" that we can use to
construct compound data objects. [Figure 2.2](#Figure 2.2) shows a
standard way to visualize a pair---in this case, the pair formed by
`(cons 1 2)`. In this representation, which is called *box-and-pointer
notation*, each object is shown as a *pointer* to a box. The box for a
primitive object contains a representation of the object. For example,
the box for a number contains a numeral. The box for a pair is actually
a double box, the left part containing (a pointer to) the `car` of the
pair and the right part containing the `cdr`.

We have already seen that `cons` can be used to combine not only numbers
but pairs as well. (You made use of this fact, or should have, in doing
[Exercise 2.2](#Exercise 2.2) and [Exercise 2.3](#Exercise 2.3).) As a
consequence, pairs provide a universal building block from which we can
construct all sorts of data structures. [Figure 2.3](#Figure 2.3) shows
two ways to use pairs to combine the numbers 1, 2, 3, and 4.

[]{#Figure 2.2 label="Figure 2.2"}

![image](fig/chap2/Fig2.2c.pdf){width="34mm"}

> **Figure 2.2:** Box-and-pointer representation of `(cons 1 2)`.

[]{#Figure 2.3 label="Figure 2.3"}

![image](fig/chap2/Fig2.3c.pdf){width="96mm"}

> **Figure 2.3:** Two ways to combine 1, 2, 3, and 4 using pairs.

The ability to create pairs whose elements are pairs is the essence of
list structure's importance as a representational tool. We refer to this
ability as the *closure property* of `cons`. In general, an operation
for combining data objects satisfies the closure property if the results
of combining things with that operation can themselves be combined using
the same operation.[^72] Closure is the key to power in any means of
combination because it permits us to create *hierarchical*
structures---structures made up of parts, which themselves are made up
of parts, and so on.

From the outset of [Chapter 1](#Chapter 1), we've made essential use of
closure in dealing with procedures, because all but the very simplest
programs rely on the fact that the elements of a combination can
themselves be combinations. In this section, we take up the consequences
of closure for compound data. We describe some conventional techniques
for using pairs to represent sequences and trees, and we exhibit a
graphics language that illustrates closure in a vivid way.[^73]

### Representing Sequences {#Section 2.2.1}

One of the useful structures we can build with pairs is a
*sequence*---an ordered collection of data objects. There are, of
course, many ways to represent sequences in terms of pairs. One
particularly straightforward representation is illustrated in [Figure
2.4](#Figure 2.4), where the sequence 1, 2, 3, 4 is represented as a
chain of pairs. The `car` of each pair is the corresponding item in the
chain, and the `cdr` of the pair is the next pair in the chain. The
`cdr` of the final pair signals the end of the sequence by pointing to a
distinguished value that is not a pair, represented in box-and-pointer
diagrams as a diagonal line and in programs as the value of the variable
`nil`. The entire sequence is constructed by nested `cons` operations:

::: scheme
(cons 1 (cons 2 (cons 3 (cons 4 nil))))
:::

[]{#Figure 2.4 label="Figure 2.4"}

![image](fig/chap2/Fig2.4c.pdf){width="76mm"}

> **Figure 2.4:** The sequence 1, 2, 3, 4 represented as a chain of
> pairs.

Such a sequence of pairs, formed by nested `cons`es, is called a *list*,
and Scheme provides a primitive called `list` to help in constructing
lists.[^74] The above sequence could be produced by `(list 1 2 3 4)`. In
general,

::: scheme
(list
 $\color{SchemeDark}\langle$  *a*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *a*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *a*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
:::

is equivalent to

::: scheme
(cons
 $\color{SchemeDark}\langle$  *a*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
(cons
 $\color{SchemeDark}\langle$  *a*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
(cons  $\dots$  (cons
 $\color{SchemeDark}\langle$  *a*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$
nil) $\dots$ )))
:::

Lisp systems conventionally print lists by printing the sequence of
elements, enclosed in parentheses. Thus, the data object in [Figure
2.4](#Figure 2.4) is printed as `(1 2 3 4)`:

::: scheme
(define one-through-four (list 1 2 3 4)) one-through-four  *(1 2 3 4)*
:::

Be careful not to confuse the expression `(list 1 2 3 4)` with the list
`(1 2 3 4)`, which is the result obtained when the expression is
evaluated. Attempting to evaluate the expression `(1 2 3 4)` will signal
an error when the interpreter tries to apply the procedure `1` to
arguments `2`, `3`, and `4`.

We can think of `car` as selecting the first item in the list, and of
`cdr` as selecting the sublist consisting of all but the first item.
Nested applications of `car` and `cdr` can be used to extract the
second, third, and subsequent items in the list.[^75] The constructor
`cons` makes a list like the original one, but with an additional item
at the beginning.

::: scheme
(car one-through-four)  *1*  (cdr one-through-four)  *(2 3 4)*  (car
(cdr one-through-four))  *2*  (cons 10 one-through-four)  *(10 1 2 3
4)*  (cons 5 one-through-four)  *(5 1 2 3 4)*
:::

The value of `nil`, used to terminate the chain of pairs, can be thought
of as a sequence of no elements, the *empty list*. The word *nil* is a
contraction of the Latin word *nihil*, which means "nothing."[^76]

#### List operations {#list-operations .unnumbered}

The use of pairs to represent sequences of elements as lists is
accompanied by conventional programming techniques for manipulating
lists by successively "`cdr`ing down" the lists. For example, the
procedure `list/ref` takes as arguments a list and a number $n$ and
returns the $n^{\mathrm{th}}$ item of the list. It is customary to
number the elements of the list beginning with 0. The method for
computing `list/ref` is the following:

-   For $n = 0$, `list/ref` should return the `car` of the list.

-   Otherwise, `list/ref` should return the $(n - 1)$-st item of the
    `cdr` of the list.

::: scheme
(define (list-ref items n) (if (= n 0) (car items) (list-ref (cdr items)
(- n 1)))) (define squares (list 1 4 9 16 25)) (list-ref squares 3)
 *16*
:::

Often we `cdr` down the whole list. To aid in this, Scheme includes a
primitive predicate `null?`, which tests whether its argument is the
empty list. The procedure `length`, which returns the number of items in
a list, illustrates this typical pattern of use:

::: scheme
(define (length items) (if (null? items) 0 (+ 1 (length (cdr items)))))
(define odds (list 1 3 5 7)) (length odds)  *4*
:::

The `length` procedure implements a simple recursive plan. The reduction
step is:

-   The `length` of any list is 1 plus the `length` of the `cdr` of the
    list.

This is applied successively until we reach the base case:

-   The `length` of the empty list is 0.

We could also compute `length` in an iterative style:

::: scheme
(define (length items) (define (length-iter a count) (if (null? a) count
(length-iter (cdr a) (+ 1 count)))) (length-iter items 0))
:::

Another conventional programming technique is to "`cons` up" an answer
list while `cdr`ing down a list, as in the procedure `append`, which
takes two lists as arguments and combines their elements to make a new
list:

::: scheme
(append squares odds)  *(1 4 9 16 25 1 3 5 7)*  (append odds squares)
 *(1 3 5 7 1 4 9 16 25)*
:::

`append` is also implemented using a recursive plan. To `append` lists
`list1` and `list2`, do the following:

-   If `list1` is the empty list, then the result is just `list2`.

-   Otherwise, `append` the `cdr` of `list1` and `list2`, and `cons` the
    `car` of `list1` onto the result:

::: scheme
(define (append list1 list2) (if (null? list1) list2 (cons (car list1)
(append (cdr list1) list2))))
:::

> **[]{#Exercise 2.17 label="Exercise 2.17"}Exercise 2.17:** Define a
> procedure `last/pair` that returns the list that contains only the
> last element of a given (nonempty) list:
>
> ::: scheme
> (last-pair (list 23 72 149 34))  *(34)*
> :::

> **[]{#Exercise 2.18 label="Exercise 2.18"}Exercise 2.18:** Define a
> procedure `reverse` that takes a list as argument and returns a list
> of the same elements in reverse order:
>
> ::: scheme
> (reverse (list 1 4 9 16 25))  *(25 16 9 4 1)*
> :::

> **[]{#Exercise 2.19 label="Exercise 2.19"}Exercise 2.19:** Consider
> the change-counting program of [Section 1.2.2](#Section 1.2.2). It
> would be nice to be able to easily change the currency used by the
> program, so that we could compute the number of ways to change a
> British pound, for example. As the program is written, the knowledge
> of the currency is distributed partly into the procedure
> `first/denomination` and partly into the procedure `count/change`
> (which knows that there are five kinds of U.S. coins). It would be
> nicer to be able to supply a list of coins to be used for making
> change.
>
> We want to rewrite the procedure `cc` so that its second argument is a
> list of the values of the coins to use rather than an integer
> specifying which coins to use. We could then have lists that defined
> each kind of currency:
>
> ::: scheme
> (define us-coins (list 50 25 10 5 1)) (define uk-coins (list 100 50 20
> 10 5 2 1 0.5))
> :::
>
> We could then call `cc` as follows:
>
> ::: scheme
> (cc 100 us-coins)  *292*
> :::
>
> To do this will require changing the program `cc` somewhat. It will
> still have the same form, but it will access its second argument
> differently, as follows:
>
> ::: scheme
> (define (cc amount coin-values) (cond ((= amount 0) 1) ((or (\< amount
> 0) (no-more? coin-values)) 0) (else (+ (cc amount
> (except-first-denomination coin-values)) (cc (- amount
> (first-denomination coin-values)) coin-values)))))
> :::
>
> Define the procedures `first/denomination`,
> `except/first/denomination`, and `no/more?` in terms of primitive
> operations on list structures. Does the order of the list
> `coin/values` affect the answer produced by `cc`? Why or why not?

> **[]{#Exercise 2.20 label="Exercise 2.20"}Exercise 2.20:** The
> procedures `+`, `*`, and `list` take arbitrary numbers of arguments.
> One way to define such procedures is to use `define` with *dotted-tail
> notation*. In a procedure definition, a parameter list that has a dot
> before the last parameter name indicates that, when the procedure is
> called, the initial parameters (if any) will have as values the
> initial arguments, as usual, but the final parameter's value will be a
> *list* of any remaining arguments. For instance, given the definition
>
> ::: scheme
> (define (f x y . z)
>  $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
> :::
>
> the procedure `f` can be called with two or more arguments. If we
> evaluate
>
> ::: scheme
> (f 1 2 3 4 5 6)
> :::
>
> then in the body of `f`, `x` will be 1, `y` will be 2, and `z` will be
> the list `(3 4 5 6)`. Given the definition
>
> ::: scheme
> (define (g . w)
>  $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
> :::
>
> the procedure `g` can be called with zero or more arguments. If we
> evaluate
>
> ::: scheme
> (g 1 2 3 4 5 6)
> :::
>
> then in the body of `g`, `w` will be the list `(1 2 3 4 5 6)`.[^77]
>
> Use this notation to write a procedure `same/parity` that takes one or
> more integers and returns a list of all the arguments that have the
> same even-odd parity as the first argument. For example,
>
> ::: scheme
> (same-parity 1 2 3 4 5 6 7)  *(1 3 5 7)*  (same-parity 2 3 4 5 6 7)
>  *(2 4 6)*
> :::

#### Mapping over lists {#mapping-over-lists .unnumbered}

One extremely useful operation is to apply some transformation to each
element in a list and generate the list of results. For instance, the
following procedure scales each number in a list by a given factor:

::: scheme
(define (scale-list items factor) (if (null? items) nil (cons (\* (car
items) factor) (scale-list (cdr items) factor)))) (scale-list (list 1 2
3 4 5) 10)  *(10 20 30 40 50)*
:::

We can abstract this general idea and capture it as a common pattern
expressed as a higher-order procedure, just as in [Section
1.3](#Section 1.3). The higher-order procedure here is called `map`.
`map` takes as arguments a procedure of one argument and a list, and
returns a list of the results produced by applying the procedure to each
element in the list:[^78]

::: scheme
(define (map proc items) (if (null? items) nil (cons (proc (car items))
(map proc (cdr items))))) (map abs (list -10 2.5 -11.6 17))  *(10 2.5
11.6 17)*  (map (lambda (x) (\* x x)) (list 1 2 3 4))  *(1 4 9 16)*
:::

Now we can give a new definition of `scale/list` in terms of `map`:

::: scheme
(define (scale-list items factor) (map (lambda (x) (\* x factor))
items))
:::

`map` is an important construct, not only because it captures a common
pattern, but because it establishes a higher level of abstraction in
dealing with lists. In the original definition of `scale/list`, the
recursive structure of the program draws attention to the
element-by-element processing of the list. Defining `scale/list` in
terms of `map` suppresses that level of detail and emphasizes that
scaling transforms a list of elements to a list of results. The
difference between the two definitions is not that the computer is
performing a different process (it isn't) but that we think about the
process differently. In effect, `map` helps establish an abstraction
barrier that isolates the implementation of procedures that transform
lists from the details of how the elements of the list are extracted and
combined. Like the barriers shown in [Figure 2.1](#Figure 2.1), this
abstraction gives us the flexibility to change the low-level details of
how sequences are implemented, while preserving the conceptual framework
of operations that transform sequences to sequences. [Section
2.2.3](#Section 2.2.3) expands on this use of sequences as a framework
for organizing programs.

> **[]{#Exercise 2.21 label="Exercise 2.21"}Exercise 2.21:** The
> procedure `square/list` takes a list of numbers as argument and
> returns a list of the squares of those numbers.
>
> ::: scheme
> (square-list (list 1 2 3 4))  *(1 4 9 16)*
> :::
>
> Here are two different definitions of `square/list`. Complete both of
> them by filling in the missing expressions:
>
> ::: scheme
> (define (square-list items) (if (null? items) nil (cons
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ )))
> (define (square-list items) (map
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ ))
> :::

> **[]{#Exercise 2.22 label="Exercise 2.22"}Exercise 2.22:** Louis
> Reasoner tries to rewrite the first `square/list` procedure of
> [Exercise 2.21](#Exercise 2.21) so that it evolves an iterative
> process:
>
> ::: scheme
> (define (square-list items) (define (iter things answer) (if (null?
> things) answer (iter (cdr things) (cons (square (car things))
> answer)))) (iter items nil))
> :::
>
> Unfortunately, defining `square/list` this way produces the answer
> list in the reverse order of the one desired. Why?
>
> Louis then tries to fix his bug by interchanging the arguments to
> `cons`:
>
> ::: scheme
> (define (square-list items) (define (iter things answer) (if (null?
> things) answer (iter (cdr things) (cons answer (square (car
> things)))))) (iter items nil))
> :::
>
> This doesn't work either. Explain.

> **[]{#Exercise 2.23 label="Exercise 2.23"}Exercise 2.23:** The
> procedure `for/each` is similar to `map`. It takes as arguments a
> procedure and a list of elements. However, rather than forming a list
> of the results, `for/each` just applies the procedure to each of the
> elements in turn, from left to right. The values returned by applying
> the procedure to the elements are not used at all---`for/each` is used
> with procedures that perform an action, such as printing. For example,
>
> ::: scheme
> (for-each (lambda (x) (newline) (display x)) (list 57 321 88))  *57*
>  *321*   *88*
> :::
>
> The value returned by the call to `for/each` (not illustrated above)
> can be something arbitrary, such as true. Give an implementation of
> `for/each`.

### Hierarchical Structures {#Section 2.2.2}

The representation of sequences in terms of lists generalizes naturally
to represent sequences whose elements may themselves be sequences. For
example, we can regard the object `((1 2) 3 4)` constructed by

::: scheme
(cons (list 1 2) (list 3 4))
:::

as a list of three items, the first of which is itself a list, `(1 2)`.
Indeed, this is suggested by the form in which the result is printed by
the interpreter. [Figure 2.5](#Figure 2.5) shows the representation of
this structure in terms of pairs.

[]{#Figure 2.5 label="Figure 2.5"}

![image](fig/chap2/Fig2.5c.pdf){width="91mm"}

> **Figure 2.5:** Structure formed by `(cons (list 1 2) (list 3 4))`.

Another way to think of sequences whose elements are sequences is as
*trees*. The elements of the sequence are the branches of the tree, and
elements that are themselves sequences are subtrees. [Figure
2.6](#Figure 2.6) shows the structure in [Figure 2.5](#Figure 2.5)
viewed as a tree.

[]{#Figure 2.6 label="Figure 2.6"}

![image](fig/chap2/Fig2.6a.pdf){width="22mm"}

> **Figure 2.6:** The list structure in [Figure 2.5](#Figure 2.5) viewed
> as a tree.

Recursion is a natural tool for dealing with tree structures, since we
can often reduce operations on trees to operations on their branches,
which reduce in turn to operations on the branches of the branches, and
so on, until we reach the leaves of the tree. As an example, compare the
`length` procedure of [Section 2.2.1](#Section 2.2.1) with the
`count/leaves` procedure, which returns the total number of leaves of a
tree:

::: scheme
(define x (cons (list 1 2) (list 3 4))) (length x)  *3*  (count-leaves
x)  *4*  (list x x)  *(((1 2) 3 4) ((1 2) 3 4))*  (length (list x x))
 *2*  (count-leaves (list x x))  *8*
:::

To implement `count/leaves`, recall the recursive plan for computing
`length`:

-   `length` of a list `x` is 1 plus `length` of the `cdr` of `x`.

-   `length` of the empty list is 0.

`count/leaves` is similar. The value for the empty list is the same:

-   `count/leaves` of the empty list is 0.

But in the reduction step, where we strip off the `car` of the list, we
must take into account that the `car` may itself be a tree whose leaves
we need to count. Thus, the appropriate reduction step is

-   `count/leaves` of a tree `x` is `count/leaves` of the `car` of `x`
    plus `count/leaves` of the `cdr` of `x`.

Finally, by taking `car`s we reach actual leaves, so we need another
base case:

-   `count/leaves` of a leaf is 1.

To aid in writing recursive procedures on trees, Scheme provides the
primitive predicate `pair?`, which tests whether its argument is a pair.
Here is the complete procedure:[^79]

::: scheme
(define (count-leaves x) (cond ((null? x) 0) ((not (pair? x)) 1) (else
(+ (count-leaves (car x)) (count-leaves (cdr x))))))
:::

> **[]{#Exercise 2.24 label="Exercise 2.24"}Exercise 2.24:** Suppose we
> evaluate the expression `(list 1 (list 2 (list 3 4)))`. Give the
> result printed by the interpreter, the corresponding box-and-pointer
> structure, and the interpretation of this as a tree (as in [Figure
> 2.6](#Figure 2.6)).

> **[]{#Exercise 2.25 label="Exercise 2.25"}Exercise 2.25:** Give
> combinations of `car`s and `cdr`s that will pick 7 from each of the
> following lists:
>
> ::: scheme
> (1 3 (5 7) 9) ((7)) (1 (2 (3 (4 (5 (6 7))))))
> :::

> **[]{#Exercise 2.26 label="Exercise 2.26"}Exercise 2.26:** Suppose we
> define `x` and `y` to be two lists:
>
> ::: scheme
> (define x (list 1 2 3)) (define y (list 4 5 6))
> :::
>
> What result is printed by the interpreter in response to evaluating
> each of the following expressions:
>
> ::: scheme
> (append x y) (cons x y) (list x y)
> :::

> **[]{#Exercise 2.27 label="Exercise 2.27"}Exercise 2.27:** Modify your
> `reverse` procedure of [Exercise 2.18](#Exercise 2.18) to produce a
> `deep/reverse` procedure that takes a list as argument and returns as
> its value the list with its elements reversed and with all sublists
> deep-reversed as well. For example,
>
> ::: scheme
> (define x (list (list 1 2) (list 3 4))) x  *((1 2) (3 4))*  (reverse
> x)  *((3 4) (1 2))*  (deep-reverse x)  *((4 3) (2 1))*
> :::

> **[]{#Exercise 2.28 label="Exercise 2.28"}Exercise 2.28:** Write a
> procedure `fringe` that takes as argument a tree (represented as a
> list) and returns a list whose elements are all the leaves of the tree
> arranged in left-to-right order. For example,
>
> ::: scheme
> (define x (list (list 1 2) (list 3 4))) (fringe x)  *(1 2 3 4)*
> (fringe (list x x))  *(1 2 3 4 1 2 3 4)*
> :::

> **[]{#Exercise 2.29 label="Exercise 2.29"}Exercise 2.29:** A binary
> mobile consists of two branches, a left branch and a right branch.
> Each branch is a rod of a certain length, from which hangs either a
> weight or another binary mobile. We can represent a binary mobile
> using compound data by constructing it from two branches (for example,
> using `list`):
>
> ::: scheme
> (define (make-mobile left right) (list left right))
> :::
>
> A branch is constructed from a `length` (which must be a number)
> together with a `structure`, which may be either a number
> (representing a simple weight) or another mobile:
>
> ::: scheme
> (define (make-branch length structure) (list length structure))
> :::
>
> a.  Write the corresponding selectors `left/branch` and
>     `right/branch`, which return the branches of a mobile, and
>     `branch/length` and `branch/structure`, which return the
>     components of a branch.
>
> b.  Using your selectors, define a procedure `total/weight` that
>     returns the total weight of a mobile.
>
> c.  A mobile is said to be *balanced* if the torque applied by its
>     top-left branch is equal to that applied by its top-right branch
>     (that is, if the length of the left rod multiplied by the weight
>     hanging from that rod is equal to the corresponding product for
>     the right side) and if each of the submobiles hanging off its
>     branches is balanced. Design a predicate that tests whether a
>     binary mobile is balanced.
>
> d.  Suppose we change the representation of mobiles so that the
>     constructors are
>
>     ::: scheme
>     (define (make-mobile left right) (cons left right)) (define
>     (make-branch length structure) (cons length structure))
>     :::
>
>     How much do you need to change your programs to convert to the new
>     representation?

#### Mapping over trees {#mapping-over-trees .unnumbered}

Just as `map` is a powerful abstraction for dealing with sequences,
`map` together with recursion is a powerful abstraction for dealing with
trees. For instance, the `scale/tree` procedure, analogous to
`scale/list` of [Section 2.2.1](#Section 2.2.1), takes as arguments a
numeric factor and a tree whose leaves are numbers. It returns a tree of
the same shape, where each number is multiplied by the factor. The
recursive plan for `scale/tree` is similar to the one for
`count/leaves`:

::: scheme
(define (scale-tree tree factor) (cond ((null? tree) nil) ((not (pair?
tree)) (\* tree factor)) (else (cons (scale-tree (car tree) factor)
(scale-tree (cdr tree) factor))))) (scale-tree (list 1 (list 2 (list 3
4) 5) (list 6 7)) 10)  *(10 (20 (30 40) 50) (60 70))*
:::

Another way to implement `scale/tree` is to regard the tree as a
sequence of sub-trees and use `map`. We map over the sequence, scaling
each sub-tree in turn, and return the list of results. In the base case,
where the tree is a leaf, we simply multiply by the factor:

::: scheme
(define (scale-tree tree factor) (map (lambda (sub-tree) (if (pair?
sub-tree) (scale-tree sub-tree factor) (\* sub-tree factor))) tree))
:::

Many tree operations can be implemented by similar combinations of
sequence operations and recursion.

> **[]{#Exercise 2.30 label="Exercise 2.30"}Exercise 2.30:** Define a
> procedure `square/tree` analogous to the `square/list` procedure of
> [Exercise 2.21](#Exercise 2.21). That is, `square/tree` should behave
> as follows:
>
> ::: scheme
> (square-tree (list 1 (list 2 (list 3 4) 5) (list 6 7)))  *(1 (4 (9 16)
> 25) (36 49))*
> :::
>
> Define `square/tree` both directly (i.e., without using any
> higher-order procedures) and also by using `map` and recursion.

> **[]{#Exercise 2.31 label="Exercise 2.31"}Exercise 2.31:** Abstract
> your answer to [Exercise 2.30](#Exercise 2.30) to produce a procedure
> `tree/map` with the property that `square/tree` could be defined as
>
> ::: scheme
> (define (square-tree tree) (tree-map square tree))
> :::

> **[]{#Exercise 2.32 label="Exercise 2.32"}Exercise 2.32:** We can
> represent a set as a list of distinct elements, and we can represent
> the set of all subsets of the set as a list of lists. For example, if
> the set is `(1 2 3)`, then the set of all subsets is
> `(() (3) (2) (2 3) (1) (1 3) (1 2) (1 2 3))`. Complete the following
> definition of a procedure that generates the set of subsets of a set
> and give a clear explanation of why it works:
>
> ::: scheme
> (define (subsets s) (if (null? s) (list nil) (let ((rest (subsets (cdr
> s)))) (append rest (map
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$  rest)))))
> :::

### Sequences as Conventional Interfaces {#Section 2.2.3}

In working with compound data, we've stressed how data abstraction
permits us to design programs without becoming enmeshed in the details
of data representations, and how abstraction preserves for us the
flexibility to experiment with alternative representations. In this
section, we introduce another powerful design principle for working with
data structures---the use of *conventional interfaces*.

In [Section 1.3](#Section 1.3) we saw how program abstractions,
implemented as higher-order procedures, can capture common patterns in
programs that deal with numerical data. Our ability to formulate
analogous operations for working with compound data depends crucially on
the style in which we manipulate our data structures. Consider, for
example, the following procedure, analogous to the `count/leaves`
procedure of [Section 2.2.2](#Section 2.2.2), which takes a tree as
argument and computes the sum of the squares of the leaves that are odd:

::: scheme
(define (sum-odd-squares tree) (cond ((null? tree) 0) ((not (pair?
tree)) (if (odd? tree) (square tree) 0)) (else (+ (sum-odd-squares (car
tree)) (sum-odd-squares (cdr tree))))))
:::

On the surface, this procedure is very different from the following one,
which constructs a list of all the even Fibonacci numbers
${\rm Fib}(k)$, where $k$ is less than or equal to a given integer $n$:

::: scheme
(define (even-fibs n) (define (next k) (if (\> k n) nil (let ((f (fib
k))) (if (even? f) (cons f (next (+ k 1))) (next (+ k 1)))))) (next 0))
:::

Despite the fact that these two procedures are structurally very
different, a more abstract description of the two computations reveals a
great deal of similarity. The first program

-   enumerates the leaves of a tree;

-   filters them, selecting the odd ones;

-   squares each of the selected ones; and

-   accumulates the results using `+`, starting with 0.

The second program

-   enumerates the integers from 0 to $n$;

-   computes the Fibonacci number for each integer;

-   filters them, selecting the even ones; and

-   accumulates the results using `cons`, starting with the empty list.

[]{#Figure 2.7 label="Figure 2.7"}

![image](fig/chap2/Fig2.7d.pdf){width="111mm"}

> **Figure 2.7:** The signal-flow plans for the procedures
> `sum/odd/squares` (top) and `even/fibs` (bottom) reveal the
> commonality between the two programs.

A signal-processing engineer would find it natural to conceptualize
these processes in terms of signals flowing through a cascade of stages,
each of which implements part of the program plan, as shown in [Figure
2.7](#Figure 2.7). In `sum/odd/squares`, we begin with an *enumerator*,
which generates a "signal" consisting of the leaves of a given tree.
This signal is passed through a *filter*, which eliminates all but the
odd elements. The resulting signal is in turn passed through a *map*,
which is a "transducer" that applies the `square` procedure to each
element. The output of the map is then fed to an *accumulator*, which
combines the elements using `+`, starting from an initial 0. The plan
for `even/fibs` is analogous.

Unfortunately, the two procedure definitions above fail to exhibit this
signal-flow structure. For instance, if we examine the `sum/odd/squares`
procedure, we find that the enumeration is implemented partly by the
`null?` and `pair?` tests and partly by the tree-recursive structure of
the procedure. Similarly, the accumulation is found partly in the tests
and partly in the addition used in the recursion. In general, there are
no distinct parts of either procedure that correspond to the elements in
the signal-flow description. Our two procedures decompose the
computations in a different way, spreading the enumeration over the
program and mingling it with the map, the filter, and the accumulation.
If we could organize our programs to make the signal-flow structure
manifest in the procedures we write, this would increase the conceptual
clarity of the resulting code.

#### Sequence Operations {#sequence-operations .unnumbered}

The key to organizing programs so as to more clearly reflect the
signal-flow structure is to concentrate on the "signals" that flow from
one stage in the process to the next. If we represent these signals as
lists, then we can use list operations to implement the processing at
each of the stages. For instance, we can implement the mapping stages of
the signal-flow diagrams using the `map` procedure from [Section
2.2.1](#Section 2.2.1):

::: scheme
(map square (list 1 2 3 4 5))  *(1 4 9 16 25)*
:::

Filtering a sequence to select only those elements that satisfy a given
predicate is accomplished by

::: scheme
(define (filter predicate sequence) (cond ((null? sequence) nil)
((predicate (car sequence)) (cons (car sequence) (filter predicate (cdr
sequence)))) (else (filter predicate (cdr sequence)))))
:::

For example,

::: scheme
(filter odd? (list 1 2 3 4 5))  *(1 3 5)*
:::

Accumulations can be implemented by

::: scheme
(define (accumulate op initial sequence) (if (null? sequence) initial
(op (car sequence) (accumulate op initial (cdr sequence)))))
(accumulate + 0 (list 1 2 3 4 5))  *15*  (accumulate \* 1 (list 1 2 3 4
5))  *120*  (accumulate cons nil (list 1 2 3 4 5))  *(1 2 3 4 5)*
:::

All that remains to implement signal-flow diagrams is to enumerate the
sequence of elements to be processed. For `even/fibs`, we need to
generate the sequence of integers in a given range, which we can do as
follows:

::: scheme
(define (enumerate-interval low high) (if (\> low high) nil (cons low
(enumerate-interval (+ low 1) high)))) (enumerate-interval 2 7)  *(2 3 4
5 6 7)*
:::

To enumerate the leaves of a tree, we can use[^80]

::: scheme
(define (enumerate-tree tree) (cond ((null? tree) nil) ((not (pair?
tree)) (list tree)) (else (append (enumerate-tree (car tree))
(enumerate-tree (cdr tree)))))) (enumerate-tree (list 1 (list 2 (list 3
4)) 5))  *(1 2 3 4 5)*
:::

Now we can reformulate `sum/odd/squares` and `even/fibs` as in the
signal-flow diagrams. For `sum/odd/squares`, we enumerate the sequence
of leaves of the tree, filter this to keep only the odd numbers in the
sequence, square each element, and sum the results:

::: scheme
(define (sum-odd-squares tree) (accumulate + 0 (map square (filter odd?
(enumerate-tree tree)))))
:::

For `even/fibs`, we enumerate the integers from 0 to $n$, generate the
Fibonacci number for each of these integers, filter the resulting
sequence to keep only the even elements, and accumulate the results into
a list:

::: scheme
(define (even-fibs n) (accumulate cons nil (filter even? (map fib
(enumerate-interval 0 n)))))
:::

The value of expressing programs as sequence operations is that this
helps us make program designs that are modular, that is, designs that
are constructed by combining relatively independent pieces. We can
encourage modular design by providing a library of standard components
together with a conventional interface for connecting the components in
flexible ways.

Modular construction is a powerful strategy for controlling complexity
in engineering design. In real signal-processing applications, for
example, designers regularly build systems by cascading elements
selected from standardized families of filters and transducers.
Similarly, sequence operations provide a library of standard program
elements that we can mix and match. For instance, we can reuse pieces
from the `sum/odd/squares` and `even/fibs` procedures in a program that
constructs a list of the squares of the first $n + 1$ Fibonacci numbers:

::: scheme
(define (list-fib-squares n) (accumulate cons nil (map square (map fib
(enumerate-interval 0 n))))) (list-fib-squares 10)  *(0 1 1 4 9 25 64
169 441 1156 3025)*
:::

We can rearrange the pieces and use them in computing the product of the
squares of the odd integers in a sequence:

::: scheme
(define (product-of-squares-of-odd-elements sequence) (accumulate \* 1
(map square (filter odd? sequence))))
(product-of-squares-of-odd-elements (list 1 2 3 4 5))  *225*
:::

We can also formulate conventional data-processing applications in terms
of sequence operations. Suppose we have a sequence of personnel records
and we want to find the salary of the highest-paid programmer. Assume
that we have a selector `salary` that returns the salary of a record,
and a predicate `programmer?` that tests if a record is for a
programmer. Then we can write

::: scheme
(define (salary-of-highest-paid-programmer records) (accumulate max 0
(map salary (filter programmer? records))))
:::

These examples give just a hint of the vast range of operations that can
be expressed as sequence operations.[^81]

Sequences, implemented here as lists, serve as a conventional interface
that permits us to combine processing modules. Additionally, when we
uniformly represent structures as sequences, we have localized the
data-structure dependencies in our programs to a small number of
sequence operations. By changing these, we can experiment with
alternative representations of sequences, while leaving the overall
design of our programs intact. We will exploit this capability in
[Section 3.5](#Section 3.5), when we generalize the sequence-processing
paradigm to admit infinite sequences.

> **[]{#Exercise 2.33 label="Exercise 2.33"}Exercise 2.33:** Fill in the
> missing expressions to complete the following definitions of some
> basic list-manipulation operations as accumulations:
>
> ::: scheme
> (define (map p sequence) (accumulate (lambda (x y)
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ ) nil
> sequence)) (define (append seq1 seq2) (accumulate cons
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ )) (define
> (length sequence) (accumulate
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$  0
> sequence))
> :::

> **[]{#Exercise 2.34 label="Exercise 2.34"}Exercise 2.34:** Evaluating
> a polynomial in $x$ at a given value of $x$ can be formulated as an
> accumulation. We evaluate the polynomial
>
> $$a_n x^n + a_{n-1} x^{n-1} + \dots + a_1 x + a_0$$
>
> using a well-known algorithm called *Horner's rule*, which structures
> the computation as
>
> $$(\dots (a_n x + a_{n-1}) x + \dots + a_1) x + a_0.$$
>
> In other words, we start with $a_n$, multiply by $x$, add $a_{n-1}$,
> multiply by $x$, and so on, until we reach $a_0$.[^82]
>
> Fill in the following template to produce a procedure that evaluates a
> polynomial using Horner's rule. Assume that the coefficients of the
> polynomial are arranged in a sequence, from $a_0$ through $a_n$.
>
> ::: scheme
> (define (horner-eval x coefficient-sequence) (accumulate (lambda
> (this-coeff higher-terms)
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ ) 0
> coefficient-sequence))
> :::
>
> For example, to compute $1 + 3x + 5x^3 + x^5$ at $x = 2$ you would
> evaluate
>
> ::: scheme
> (horner-eval 2 (list 1 3 0 5 0 1))
> :::

> **[]{#Exercise 2.35 label="Exercise 2.35"}Exercise 2.35:** Redefine
> `count/leaves` from [Section 2.2.2](#Section 2.2.2) as an
> accumulation:
>
> ::: scheme
> (define (count-leaves t) (accumulate
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$  (map
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ )))
> :::

> **[]{#Exercise 2.36 label="Exercise 2.36"}Exercise 2.36:** The
> procedure `accumulate/n` is similar to `accumulate` except that it
> takes as its third argument a sequence of sequences, which are all
> assumed to have the same number of elements. It applies the designated
> accumulation procedure to combine all the first elements of the
> sequences, all the second elements of the sequences, and so on, and
> returns a sequence of the results. For instance, if `s` is a sequence
> containing four sequences, `((1 2 3) (4 5 6) (7 8 9) (10 11 12)),`
> then the value of `(accumulate/n + 0 s)` should be the sequence
> `(22 26 30)`. Fill in the missing expressions in the following
> definition of `accumulate/n`:
>
> ::: scheme
> (define (accumulate-n op init seqs) (if (null? (car seqs)) nil (cons
> (accumulate op init
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ )
> (accumulate-n op init
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ ))))
> :::

> **[]{#Exercise 2.37 label="Exercise 2.37"}Exercise 2.37:** Suppose we
> represent vectors $\hbox{\bf v} = (v_i)$ as sequences of numbers, and
> matrices $\hbox{\bf m} = (m_{i\!j})$ as sequences of vectors (the rows
> of the matrix). For example, the matrix
>
> $$\left(
> \begin{array}{cccc}
>   1 & 2 & 3 & 4 \\
>   4 & 5 & 6 & 6 \\
>   6 & 7 & 8 & 9
> \end{array}
> \right)$$
>
> is represented as the sequence `((1 2 3 4) (4 5 6 6) (6 7 8 9))`. With
> this representation, we can use sequence operations to concisely
> express the basic matrix and vector operations. These operations
> (which are described in any book on matrix algebra) are the following:
>
> $$\begin{array}{rl}
>     \hbox{\tt (dot-product v w)}        & {\rm returns\;the\;sum\;} \Sigma_i v_i w_i; \\
>     \hbox{\tt (matrix-*-vector m v)}    & {\rm returns\;the\;vector\;} \hbox{\bf t},  \\
>         & {\rm where\;} t_i = \Sigma_{\kern-0.1em j} m_{i\!j} v_{\kern-0.1em j};      \\
>     \hbox{\tt (matrix-*-matrix m n)}    & {\rm returns\;the\;matrix\;} \hbox{\bf p},  \\
>         & {\rm where\;} p_{i\!j} = \Sigma_k m_{ik} n_{k\!j};                          \\
>     \hbox{\tt (transpose m)}        & {\rm returns\;the\;matrix\;} \hbox{\bf n},  \\
>         & {\rm where\;} n_{i\!j} = m_{\kern-0.1em ji}.
> \end{array}$$
>
> We can define the dot product as[^83]
>
> ::: scheme
> (define (dot-product v w) (accumulate + 0 (map \* v w)))
> :::
>
> Fill in the missing expressions in the following procedures for
> computing the other matrix operations. (The procedure `accumulate/n`
> is defined in [Exercise 2.36](#Exercise 2.36).)
>
> ::: scheme
> (define (matrix-\*-vector m v) (map
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$  m))
> (define (transpose mat) (accumulate-n
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$  mat))
> (define (matrix-\*-matrix m n) (let ((cols (transpose n))) (map
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$  m)))
> :::

> **[]{#Exercise 2.38 label="Exercise 2.38"}Exercise 2.38:** The
> `accumulate` procedure is also known as `fold/right`, because it
> combines the first element of the sequence with the result of
> combining all the elements to the right. There is also a `fold/left`,
> which is similar to `fold/right`, except that it combines elements
> working in the opposite direction:
>
> ::: scheme
> (define (fold-left op initial sequence) (define (iter result rest) (if
> (null? rest) result (iter (op result (car rest)) (cdr rest)))) (iter
> initial sequence))
> :::
>
> What are the values of
>
> ::: scheme
> (fold-right / 1 (list 1 2 3)) (fold-left / 1 (list 1 2 3)) (fold-right
> list nil (list 1 2 3)) (fold-left list nil (list 1 2 3))
> :::
>
> Give a property that `op` should satisfy to guarantee that
> `fold/right` and `fold/left` will produce the same values for any
> sequence.

> **[]{#Exercise 2.39 label="Exercise 2.39"}Exercise 2.39:** Complete
> the following definitions of `reverse` ([Exercise
> 2.18](#Exercise 2.18)) in terms of `fold/right` and `fold/left` from
> [Exercise 2.38](#Exercise 2.38):
>
> ::: scheme
> (define (reverse sequence) (fold-right (lambda (x y)
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ ) nil
> sequence)) (define (reverse sequence) (fold-left (lambda (x y)
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ ) nil
> sequence))
> :::

#### Nested Mappings {#nested-mappings .unnumbered}

We can extend the sequence paradigm to include many computations that
are commonly expressed using nested loops.[^84] Consider this problem:
Given a positive integer $n$, find all ordered pairs of distinct
positive integers $i$ and $j$, where $1 \le j < i \le n$, such that
$i + j$ is prime. For example, if $n$ is 6, then the pairs are the
following:

$$\vbox{
\offinterlineskip
\halign{
\strut \hfil \quad #\quad \hfil & \vrule
    \hfil \quad #\quad \hfil &
    \hfil \quad #\quad \hfil &
    \hfil \quad #\quad \hfil &
    \hfil \quad #\quad \hfil &
    \hfil \quad #\quad \hfil &
    \hfil \quad #\quad \hfil &
    \hfil \quad #\quad \hfil \cr

$i$     & 2 & 3 & 4 & 4 & 5 & 6 & 6 \cr
$j$     & 1 & 2 & 1 & 3 & 2 & 1 & 5 \cr
\noalign{\hrule}
$i + j$ & 3 & 5 & 5 & 7 & 7 & 7 & 11 \cr}
}$$

A natural way to organize this computation is to generate the sequence
of all ordered pairs of positive integers less than or equal to $n$,
filter to select those pairs whose sum is prime, and then, for each pair
$(i, j)$ that passes through the filter, produce the triple
$(i, j, i + j)$.

Here is a way to generate the sequence of pairs: For each integer
$i \le n$, enumerate the integers $j < i$, and for each such $i$ and $j$
generate the pair $(i, j)$. In terms of sequence operations, we map
along the sequence `(enumerate/interval 1 n)`. For each $i$ in this
sequence, we map along the sequence `(enumerate/interval 1 (- i 1))`.
For each $j$ in this latter sequence, we generate the pair `(list i j)`.
This gives us a sequence of pairs for each $i$. Combining all the
sequences for all the $i$ (by accumulating with `append`) produces the
required sequence of pairs:[^85]

::: scheme
(accumulate append nil (map (lambda (i) (map (lambda (j) (list i j))
(enumerate-interval 1 (- i 1)))) (enumerate-interval 1 n)))
:::

The combination of mapping and accumulating with `append` is so common
in this sort of program that we will isolate it as a separate procedure:

::: scheme
(define (flatmap proc seq) (accumulate append nil (map proc seq)))
:::

Now filter this sequence of pairs to find those whose sum is prime. The
filter predicate is called for each element of the sequence; its
argument is a pair and it must extract the integers from the pair. Thus,
the predicate to apply to each element in the sequence is

::: scheme
(define (prime-sum? pair) (prime? (+ (car pair) (cadr pair))))
:::

Finally, generate the sequence of results by mapping over the filtered
pairs using the following procedure, which constructs a triple
consisting of the two elements of the pair along with their sum:

::: scheme
(define (make-pair-sum pair) (list (car pair) (cadr pair) (+ (car pair)
(cadr pair))))
:::

Combining all these steps yields the complete procedure:

::: smallscheme
(define (prime-sum-pairs n) (map make-pair-sum (filter prime-sum?
(flatmap (lambda (i) (map (lambda (j) (list i j)) (enumerate-interval 1
(- i 1)))) (enumerate-interval 1 n)))))
:::

Nested mappings are also useful for sequences other than those that
enumerate intervals. Suppose we wish to generate all the permutations of
a set $S;$ that is, all the ways of ordering the items in the set. For
instance, the permutations of $\{1, 2, 3\}$ are $\{1, 2, 3\}$,
$\{1, 3, 2\}$, $\{2, 1, 3\}$, $\{2, 3, 1\}$, $\{3, 1, 2\}$, and
$\{3, 2, 1\}$. Here is a plan for generating the permutations of $S$:
For each item $x$ in $S$, recursively generate the sequence of
permutations of $S - x$,[^86] and adjoin $x$ to the front of each one.
This yields, for each $x$ in $S$, the sequence of permutations of $S$
that begin with $x$. Combining these sequences for all $x$ gives all the
permutations of $S$:[^87]

::: scheme
(define (permutations s) (if (null? s)  [; empty set?]{.roman}  (list
nil)  [; sequence containing empty set]{.roman}  (flatmap (lambda (x)
(map (lambda (p) (cons x p)) (permutations (remove x s)))) s)))
:::

Notice how this strategy reduces the problem of generating permutations
of $S$ to the problem of generating the permutations of sets with fewer
elements than $S$. In the terminal case, we work our way down to the
empty list, which represents a set of no elements. For this, we generate
`(list nil)`, which is a sequence with one item, namely the set with no
elements. The `remove` procedure used in `permutations` returns all the
items in a given sequence except for a given item. This can be expressed
as a simple filter:

::: scheme
(define (remove item sequence) (filter (lambda (x) (not (= x item)))
sequence))
:::

> **[]{#Exercise 2.40 label="Exercise 2.40"}Exercise 2.40:** Define a
> procedure `unique/pairs` that, given an integer $n$, generates the
> sequence of pairs $(i, j)$ with $1 \le j < i \le n$. Use
> `unique/pairs` to simplify the definition of `prime/sum/pairs` given
> above.

> **[]{#Exercise 2.41 label="Exercise 2.41"}Exercise 2.41:** Write a
> procedure to find all ordered triples of distinct positive integers
> $i$, $j$, and $k$ less than or equal to a given integer $n$ that sum
> to a given integer $s$.

> **[]{#Exercise 2.42 label="Exercise 2.42"}Exercise 2.42:** The
> "eight-queens puzzle" asks how to place eight queens on a chessboard
> so that no queen is in check from any other (i.e., no two queens are
> in the same row, column, or diagonal). One possible solution is shown
> in [Figure 2.8](#Figure 2.8). One way to solve the puzzle is to work
> across the board, placing a queen in each column. Once we have placed
> $k - 1$ queens, we must place the $k^{\mathrm{th}}$ queen in a
> position where it does not check any of the queens already on the
> board. We can formulate this approach recursively: Assume that we have
> already generated the sequence of all possible ways to place $k - 1$
> queens in the first $k - 1$ columns of the board. For each of these
> ways, generate an extended set of positions by placing a queen in each
> row of the $k^{\mathrm{th}}$ column. Now filter these, keeping only
> the positions for which the queen in the $k^{\mathrm{th}}$ column is
> safe with respect to the other queens. This produces the sequence of
> all ways to place $k$ queens in the first $k$ columns. By continuing
> this process, we will produce not only one solution, but all solutions
> to the puzzle.
>
> []{#Figure 2.8 label="Figure 2.8"}
>
> ![image](fig/chap2/Fig2.8c.pdf){width="48mm"}
>
> **Figure 2.8:** A solution to the eight-queens puzzle.
>
> We implement this solution as a procedure `queens`, which returns a
> sequence of all solutions to the problem of placing $n$ queens on an
> $n \times n$ chessboard. `queens` has an internal procedure
> `queen/cols` that returns the sequence of all ways to place queens in
> the first $k$ columns of the board.
>
> ::: scheme
> (define (queens board-size) (define (queen-cols k) (if (= k 0) (list
> empty-board) (filter (lambda (positions) (safe? k positions)) (flatmap
> (lambda (rest-of-queens) (map (lambda (new-row) (adjoin-position
> new-row k rest-of-queens)) (enumerate-interval 1 board-size)))
> (queen-cols (- k 1)))))) (queen-cols board-size))
> :::
>
> In this procedure `rest/of/queens` is a way to place $k - 1$ queens in
> the first $k - 1$ columns, and `new/row` is a proposed row in which to
> place the queen for the $k^{\mathrm{th}}$ column. Complete the program
> by implementing the representation for sets of board positions,
> including the procedure `adjoin/position`, which adjoins a new
> row-column position to a set of positions, and `empty/board`, which
> represents an empty set of positions. You must also write the
> procedure `safe?`, which determines for a set of positions, whether
> the queen in the $k^{\mathrm{th}}$ column is safe with respect to the
> others. (Note that we need only check whether the new queen is
> safe---the other queens are already guaranteed safe with respect to
> each other.)

> **[]{#Exercise 2.43 label="Exercise 2.43"}Exercise 2.43:** Louis
> Reasoner is having a terrible time doing [Exercise
> 2.42](#Exercise 2.42). His `queens` procedure seems to work, but it
> runs extremely slowly. (Louis never does manage to wait long enough
> for it to solve even the $6\times6$ case.) When Louis asks Eva Lu Ator
> for help, she points out that he has interchanged the order of the
> nested mappings in the `flatmap`, writing it as
>
> ::: scheme
> (flatmap (lambda (new-row) (map (lambda (rest-of-queens)
> (adjoin-position new-row k rest-of-queens)) (queen-cols (- k 1))))
> (enumerate-interval 1 board-size))
> :::
>
> Explain why this interchange makes the program run slowly. Estimate
> how long it will take Louis's program to solve the eight-queens
> puzzle, assuming that the program in [Exercise 2.42](#Exercise 2.42)
> solves the puzzle in time $T$.

### Example: A Picture Language {#Section 2.2.4}

This section presents a simple language for drawing pictures that
illustrates the power of data abstraction and closure, and also exploits
higher-order procedures in an essential way. The language is designed to
make it easy to experiment with patterns such as the ones in [Figure
2.9](#Figure 2.9), which are composed of repeated elements that are
shifted and scaled.[^88] In this language, the data objects being
combined are represented as procedures rather than as list structure.
Just as `cons`, which satisfies the closure property, allowed us to
easily build arbitrarily complicated list structure, the operations in
this language, which also satisfy the closure property, allow us to
easily build arbitrarily complicated patterns.

#### The picture language {#the-picture-language .unnumbered}

When we began our study of programming in [Section 1.1](#Section 1.1),
we emphasized the importance of describing a language by focusing on the
language's primitives, its means of combination, and its means of
abstraction. We'll follow that framework here.

[]{#Figure 2.9 label="Figure 2.9"}

![image](fig/chap2/Fig2.9-bigger.png){width="111mm"}

**Figure 2.9:** Designs generated with the picture language.

[]{#Figure 2.10 label="Figure 2.10"}

![image](fig/chap2/Fig2.10.pdf){width="50mm"}

> **Figure 2.10:** Images produced by the `wave` painter, with respect
> to four different frames. The frames, shown with dotted lines, are not
> part of the images.

Part of the elegance of this picture language is that there is only one
kind of element, called a *painter*. A painter draws an image that is
shifted and scaled to fit within a designated parallelogram-shaped
frame. For example, there's a primitive painter we'll call `wave` that
makes a crude line drawing, as shown in [Figure 2.10](#Figure 2.10). The
actual shape of the drawing depends on the frame---all four images in
figure 2.10 are produced by the same `wave` painter, but with respect to
four different frames. Painters can be more elaborate than this: The
primitive painter called `rogers` paints a picture of
mit's founder, William Barton Rogers, as shown in [Figure
2.11](#Figure 2.11).[^89] The four images in figure 2.11 are drawn with
respect to the same four frames as the `wave` images in figure 2.10.

[]{#Figure 2.11 label="Figure 2.11"}

![image](fig/chap2/Fig2.11.pdf){width="48mm"}

> **Figure 2.11:** Images of William Barton Rogers, founder and first
> president of mit, painted with respect to the same four
> frames as in [Figure 2.10](#Figure 2.10) (original image from
> Wikimedia Commons).

To combine images, we use various operations that construct new painters
from given painters. For example, the `beside` operation takes two
painters and produces a new, compound painter that draws the first
painter's image in the left half of the frame and the second painter's
image in the right half of the frame. Similarly, `below` takes two
painters and produces a compound painter that draws the first painter's
image below the second painter's image. Some operations transform a
single painter to produce a new painter. For example, `flip/vert` takes
a painter and produces a painter that draws its image upside-down, and
`flip/horiz` produces a painter that draws the original painter's image
left-to-right reversed.

[Figure 2.12](#Figure 2.12) shows the drawing of a painter called
`wave4` that is built up in two stages starting from `wave`:

::: scheme
(define wave2 (beside wave (flip-vert wave))) (define wave4 (below wave2
wave2))
:::

[]{#Figure 2.12 label="Figure 2.12"}

![image](fig/chap2/Fig2.12.pdf){width="50mm"}

> **Figure 2.12:** Creating a complex figure, starting from the `wave`
> painter of [Figure 2.10](#Figure 2.10).

In building up a complex image in this manner we are exploiting the fact
that painters are closed under the language's means of combination. The
`beside` or `below` of two painters is itself a painter; therefore, we
can use it as an element in making more complex painters. As with
building up list structure using `cons`, the closure of our data under
the means of combination is crucial to the ability to create complex
structures while using only a few operations.

Once we can combine painters, we would like to be able to abstract
typical patterns of combining painters. We will implement the painter
operations as Scheme procedures. This means that we don't need a special
abstraction mechanism in the picture language: Since the means of
combination are ordinary Scheme procedures, we automatically have the
capability to do anything with painter operations that we can do with
procedures. For example, we can abstract the pattern in `wave4` as

::: scheme
(define (flipped-pairs painter) (let ((painter2 (beside painter
(flip-vert painter)))) (below painter2 painter2)))
:::

and define `wave4` as an instance of this pattern:

::: scheme
(define wave4 (flipped-pairs wave))
:::

[]{#Figure 2.13 label="Figure 2.13"}

![image](fig/chap2/Fig2.13a.pdf){width="111mm"}

**Figure 2.13:** Recursive plans for `right/split` and `corner/split`.

We can also define recursive operations. Here's one that makes painters
split and branch towards the right as shown in [Figure
2.13](#Figure 2.13) and [Figure 2.14](#Figure 2.14):

::: scheme
(define (right-split painter n) (if (= n 0) painter (let ((smaller
(right-split painter (- n 1)))) (beside painter (below smaller
smaller)))))
:::

We can produce balanced patterns by branching upwards as well as towards
the right (see exercise [Exercise 2.44](#Exercise 2.44) and figures
[Figure 2.13](#Figure 2.13) and [Figure 2.14](#Figure 2.14)):

::: scheme
(define (corner-split painter n) (if (= n 0) painter (let ((up (up-split
painter (- n 1))) (right (right-split painter (- n 1)))) (let ((top-left
(beside up up)) (bottom-right (below right right)) (corner (corner-split
painter (- n 1)))) (beside (below painter top-left) (below bottom-right
corner))))))
:::

By placing four copies of a `corner/split` appropriately, we obtain a
pattern called `square/limit`, whose application to `wave` and `rogers`
is shown in [Figure 2.9](#Figure 2.9):

::: scheme
(define (square-limit painter n) (let ((quarter (corner-split painter
n))) (let ((half (beside (flip-horiz quarter) quarter))) (below
(flip-vert half) half))))
:::

> **[]{#Exercise 2.44 label="Exercise 2.44"}Exercise 2.44:** Define the
> procedure `up/split` used by `corner/split`. It is similar to
> `right/split`, except that it switches the roles of `below` and
> `beside`.

[]{#Figure 2.14 label="Figure 2.14"}

![image](fig/chap2/Fig2.14b.pdf){width="91mm"}

> **Figure 2.14:** The recursive operations `right/split` and
> `corner/split` applied to the painters `wave` and `rogers`. Combining
> four `corner/split` figures produces symmetric `square/limit` designs
> as shown in [Figure 2.9](#Figure 2.9).

#### Higher-order operations {#higher-order-operations .unnumbered}

In addition to abstracting patterns of combining painters, we can work
at a higher level, abstracting patterns of combining painter operations.
That is, we can view the painter operations as elements to manipulate
and can write means of combination for these elements---procedures that
take painter operations as arguments and create new painter operations.

For example, `flipped/pairs` and `square/limit` each arrange four copies
of a painter's image in a square pattern; they differ only in how they
orient the copies. One way to abstract this pattern of painter
combination is with the following procedure, which takes four
one-argument painter operations and produces a painter operation that
transforms a given painter with those four operations and arranges the
results in a square. `tl`, `tr`, `bl`, and `br` are the transformations
to apply to the top left copy, the top right copy, the bottom left copy,
and the bottom right copy, respectively.

::: scheme
(define (square-of-four tl tr bl br) (lambda (painter) (let ((top
(beside (tl painter) (tr painter))) (bottom (beside (bl painter) (br
painter)))) (below bottom top))))
:::

Then `flipped/pairs` can be defined in terms of `square/of/four` as
follows:[^90]

::: scheme
(define (flipped-pairs painter) (let ((combine4 (square-of-four identity
flip-vert identity flip-vert))) (combine4 painter)))
:::

and `square/limit` can be expressed as[^91]

::: scheme
(define (square-limit painter n) (let ((combine4 (square-of-four
flip-horiz identity rotate180 flip-vert))) (combine4 (corner-split
painter n))))
:::

> **[]{#Exercise 2.45 label="Exercise 2.45"}Exercise 2.45:**
> `right/split` and `up/split` can be expressed as instances of a
> general splitting operation. Define a procedure `split` with the
> property that evaluating
>
> ::: scheme
> (define right-split (split beside below)) (define up-split (split
> below beside))
> :::
>
> produces procedures `right/split` and `up/split` with the same
> behaviors as the ones already defined.

#### Frames {#frames .unnumbered}

Before we can show how to implement painters and their means of
combination, we must first consider frames. A frame can be described by
three vectors---an origin vector and two edge vectors. The origin vector
specifies the offset of the frame's origin from some absolute origin in
the plane, and the edge vectors specify the offsets of the frame's
corners from its origin. If the edges are perpendicular, the frame will
be rectangular. Otherwise the frame will be a more general
parallelogram.

[Figure 2.15](#Figure 2.15) shows a frame and its associated vectors. In
accordance with data abstraction, we need not be specific yet about how
frames are represented, other than to say that there is a constructor
`make/frame`, which takes three vectors and produces a frame, and three
corresponding selectors `origin/frame`, `edge1/frame`, and `edge2/frame`
(see [Exercise 2.47](#Exercise 2.47)).

[]{#Figure 2.15 label="Figure 2.15"}

![image](fig/chap2/Fig2.15a.pdf){width="51mm"}

> **Figure 2.15:** A frame is described by three vectors --- an origin
> and two edges.

We will use coordinates in the unit square $(0 \le x, y \le 1)$ to
specify images. With each frame, we associate a *frame coordinate map*,
which will be used to shift and scale images to fit the frame. The map
transforms the unit square into the frame by mapping the vector
$\hbox{\bf v} = (x, y)$ to the vector sum

$${\rm Origin(Frame)} + x \cdot {\rm Edge_1(Frame)} + y \cdot {\rm Edge_2(Frame)}.$$

For example, (0, 0) is mapped to the origin of the frame, (1, 1) to the
vertex diagonally opposite the origin, and (0.5, 0.5) to the center of
the frame. We can create a frame's coordinate map with the following
procedure:[^92]

::: scheme
(define (frame-coord-map frame) (lambda (v) (add-vect (origin-frame
frame) (add-vect (scale-vect (xcor-vect v) (edge1-frame frame))
(scale-vect (ycor-vect v) (edge2-frame frame))))))
:::

Observe that applying `frame/coord/map` to a frame returns a procedure
that, given a vector, returns a vector. If the argument vector is in the
unit square, the result vector will be in the frame. For example,

::: scheme
((frame-coord-map a-frame) (make-vect 0 0))
:::

returns the same vector as

::: scheme
(origin-frame a-frame)
:::

> **[]{#Exercise 2.46 label="Exercise 2.46"}Exercise 2.46:** A
> two-dimensional vector $\hbox{\bf v}$ running from the origin to a
> point can be represented as a pair consisting of an $x$-coordinate and
> a $y$-coordinate. Implement a data abstraction for vectors by giving a
> constructor `make/vect` and corresponding selectors `xcor/vect` and
> `ycor/vect`. In terms of your selectors and constructor, implement
> procedures `add/vect`, `sub/vect`, and `scale/vect` that perform the
> operations vector addition, vector subtraction, and multiplying a
> vector by a scalar:
>
> $$\begin{array}{r@{{}={}}l}
>     (x_1, y_1) + (x_2, y_2)     & (x_1 + x_2, y_1 + y_2), \\
>     (x_1, y_1) - (x_2, y_2)     & (x_1 - x_2, y_1 - y_2), \\
>     s \cdot (x, y)          & (sx, sy).
> \end{array}$$

> **[]{#Exercise 2.47 label="Exercise 2.47"}Exercise 2.47:** Here are
> two possible constructors for frames:
>
> ::: scheme
> (define (make-frame origin edge1 edge2) (list origin edge1 edge2))
> (define (make-frame origin edge1 edge2) (cons origin (cons edge1
> edge2)))
> :::
>
> For each constructor supply the appropriate selectors to produce an
> implementation for frames.

#### Painters {#painters .unnumbered}

A painter is represented as a procedure that, given a frame as argument,
draws a particular image shifted and scaled to fit the frame. That is to
say, if `p` is a painter and `f` is a frame, then we produce `p`'s image
in `f` by calling `p` with `f` as argument.

The details of how primitive painters are implemented depend on the
particular characteristics of the graphics system and the type of image
to be drawn. For instance, suppose we have a procedure `draw/line` that
draws a line on the screen between two specified points. Then we can
create painters for line drawings, such as the `wave` painter in [Figure
2.10](#Figure 2.10), from lists of line segments as follows:[^93]

::: scheme
(define (segments-\>painter segment-list) (lambda (frame) (for-each
(lambda (segment) (draw-line ((frame-coord-map frame) (start-segment
segment)) ((frame-coord-map frame) (end-segment segment))))
segment-list)))
:::

The segments are given using coordinates with respect to the unit
square. For each segment in the list, the painter transforms the segment
endpoints with the frame coordinate map and draws a line between the
transformed points.

Representing painters as procedures erects a powerful abstraction
barrier in the picture language. We can create and intermix all sorts of
primitive painters, based on a variety of graphics capabilities. The
details of their implementation do not matter. Any procedure can serve
as a painter, provided that it takes a frame as argument and draws
something scaled to fit the frame.[^94]

> **[]{#Exercise 2.48 label="Exercise 2.48"}Exercise 2.48:** A directed
> line segment in the plane can be represented as a pair of
> vectors---the vector running from the origin to the start-point of the
> segment, and the vector running from the origin to the end-point of
> the segment. Use your vector representation from [Exercise
> 2.46](#Exercise 2.46) to define a representation for segments with a
> constructor `make/segment` and selectors `start/segment` and
> `end/segment`.

> **[]{#Exercise 2.49 label="Exercise 2.49"}Exercise 2.49:** Use
> `segments/>painter` to define the following primitive painters:
>
> a.  The painter that draws the outline of the designated frame.
>
> b.  The painter that draws an "X" by connecting opposite corners of
>     the frame.
>
> c.  The painter that draws a diamond shape by connecting the midpoints
>     of the sides of the frame.
>
> d.  The `wave` painter.

#### Transforming and combining painters {#transforming-and-combining-painters .unnumbered}

An operation on painters (such as `flip/vert` or `beside`) works by
creating a painter that invokes the original painters with respect to
frames derived from the argument frame. Thus, for example, `flip/vert`
doesn't have to know how a painter works in order to flip it---it just
has to know how to turn a frame upside down: The flipped painter just
uses the original painter, but in the inverted frame.

Painter operations are based on the procedure `transform/painter`, which
takes as arguments a painter and information on how to transform a frame
and produces a new painter. The transformed painter, when called on a
frame, transforms the frame and calls the original painter on the
transformed frame. The arguments to `transform/painter` are points
(represented as vectors) that specify the corners of the new frame: When
mapped into the frame, the first point specifies the new frame's origin
and the other two specify the ends of its edge vectors. Thus, arguments
within the unit square specify a frame contained within the original
frame.

::: scheme
(define (transform-painter painter origin corner1 corner2) (lambda
(frame) (let ((m (frame-coord-map frame))) (let ((new-origin (m
origin))) (painter (make-frame new-origin (sub-vect (m corner1)
new-origin) (sub-vect (m corner2) new-origin)))))))
:::

Here's how to flip painter images vertically:

::: scheme
(define (flip-vert painter) (transform-painter painter (make-vect 0.0
1.0)  [; new `origin`]{.roman}  (make-vect 1.0 1.0)  [; new end of
`edge1`]{.roman}  (make-vect 0.0 0.0)))  [; new end of `edge2`]{.roman}
:::

Using `transform/painter`, we can easily define new transformations. For
example, we can define a painter that shrinks its image to the
upper-right quarter of the frame it is given:

::: scheme
(define (shrink-to-upper-right painter) (transform-painter painter
(make-vect 0.5 0.5) (make-vect 1.0 0.5) (make-vect 0.5 1.0)))
:::

Other transformations rotate images counterclockwise by 90 degrees[^95]

::: scheme
(define (rotate90 painter) (transform-painter painter (make-vect 1.0
0.0) (make-vect 1.0 1.0) (make-vect 0.0 0.0)))
:::

or squash images towards the center of the frame:[^96]

::: scheme
(define (squash-inwards painter) (transform-painter painter (make-vect
0.0 0.0) (make-vect 0.65 0.35) (make-vect 0.35 0.65)))
:::

Frame transformation is also the key to defining means of combining two
or more painters. The `beside` procedure, for example, takes two
painters, transforms them to paint in the left and right halves of an
argument frame respectively, and produces a new, compound painter. When
the compound painter is given a frame, it calls the first transformed
painter to paint in the left half of the frame and calls the second
transformed painter to paint in the right half of the frame:

::: scheme
(define (beside painter1 painter2) (let ((split-point (make-vect 0.5
0.0))) (let ((paint-left (transform-painter painter1 (make-vect 0.0 0.0)
split-point (make-vect 0.0 1.0))) (paint-right (transform-painter
painter2 split-point (make-vect 1.0 0.0) (make-vect 0.5 1.0)))) (lambda
(frame) (paint-left frame) (paint-right frame)))))
:::

Observe how the painter data abstraction, and in particular the
representation of painters as procedures, makes `beside` easy to
implement. The `beside` procedure need not know anything about the
details of the component painters other than that each painter will draw
something in its designated frame.

> **[]{#Exercise 2.50 label="Exercise 2.50"}Exercise 2.50:** Define the
> transformation `flip/horiz`, which flips painters horizontally, and
> transformations that rotate painters counterclockwise by 180 degrees
> and 270 degrees.

> **[]{#Exercise 2.51 label="Exercise 2.51"}Exercise 2.51:** Define the
> `below` operation for painters. `below` takes two painters as
> arguments. The resulting painter, given a frame, draws with the first
> painter in the bottom of the frame and with the second painter in the
> top. Define `below` in two different ways---first by writing a
> procedure that is analogous to the `beside` procedure given above, and
> again in terms of `beside` and suitable rotation operations (from
> [Exercise 2.50](#Exercise 2.50)).

#### Levels of language for robust design {#levels-of-language-for-robust-design .unnumbered}

The picture language exercises some of the critical ideas we've
introduced about abstraction with procedures and data. The fundamental
data abstractions, painters, are implemented using procedural
representations, which enables the language to handle different basic
drawing capabilities in a uniform way. The means of combination satisfy
the closure property, which permits us to easily build up complex
designs. Finally, all the tools for abstracting procedures are available
to us for abstracting means of combination for painters.

We have also obtained a glimpse of another crucial idea about languages
and program design. This is the approach of *stratified design*, the
notion that a complex system should be structured as a sequence of
levels that are described using a sequence of languages. Each level is
constructed by combining parts that are regarded as primitive at that
level, and the parts constructed at each level are used as primitives at
the next level. The language used at each level of a stratified design
has primitives, means of combination, and means of abstraction
appropriate to that level of detail.

Stratified design pervades the engineering of complex systems. For
example, in computer engineering, resistors and transistors are combined
(and described using a language of analog circuits) to produce parts
such as and-gates and or-gates, which form the primitives of a language
for digital-circuit design.[^97] These parts are combined to build
processors, bus structures, and memory systems, which are in turn
combined to form computers, using languages appropriate to computer
architecture. Computers are combined to form distributed systems, using
languages appropriate for describing network interconnections, and so
on.

As a tiny example of stratification, our picture language uses primitive
elements (primitive painters) that are created using a language that
specifies points and lines to provide the lists of line segments for
`segments/>painter`, or the shading details for a painter like `rogers`.
The bulk of our description of the picture language focused on combining
these primitives, using geometric combiners such as `beside` and
`below`. We also worked at a higher level, regarding `beside` and
`below` as primitives to be manipulated in a language whose operations,
such as `square/of/four`, capture common patterns of combining geometric
combiners.

Stratified design helps make programs *robust*, that is, it makes it
likely that small changes in a specification will require
correspondingly small changes in the program. For instance, suppose we
wanted to change the image based on `wave` shown in [Figure
2.9](#Figure 2.9). We could work at the lowest level to change the
detailed appearance of the `wave` element; we could work at the middle
level to change the way `corner/split` replicates the `wave`; we could
work at the highest level to change how `square/limit` arranges the four
copies of the corner. In general, each level of a stratified design
provides a different vocabulary for expressing the characteristics of
the system, and a different kind of ability to change it.

> **[]{#Exercise 2.52 label="Exercise 2.52"}Exercise 2.52:** Make
> changes to the square limit of `wave` shown in [Figure
> 2.9](#Figure 2.9) by working at each of the levels described above. In
> particular:
>
> a.  Add some segments to the primitive `wave` painter of [Exercise
>     2.49](#Exercise 2.49) (to add a smile, for example).
>
> b.  Change the pattern constructed by `corner/split` (for example, by
>     using only one copy of the `up/split` and `right/split` images
>     instead of two).
>
> c.  Modify the version of `square/limit` that uses `square/of/four` so
>     as to assemble the corners in a different pattern. (For example,
>     you might make the big Mr. Rogers look outward from each corner of
>     the square.)

## Symbolic Data {#Section 2.3}

All the compound data objects we have used so far were constructed
ultimately from numbers. In this section we extend the representational
capability of our language by introducing the ability to work with
arbitrary symbols as data.

### Quotation {#Section 2.3.1}

If we can form compound data using symbols, we can have lists such as

::: scheme
(a b c d) (23 45 17) ((Norah 12) (Molly 9) (Anna 7) (Lauren 6)
(Charlotte 4))
:::

Lists containing symbols can look just like the expressions of our
language:

::: scheme
(\* (+ 23 45) (+ x 9)) (define (fact n) (if (= n 1) 1 (\* n (fact (- n
1)))))
:::

In order to manipulate symbols we need a new element in our language:
the ability to *quote* a data object. Suppose we want to construct the
list `(a b)`. We can't accomplish this with `(list a b)`, because this
expression constructs a list of the *values* of `a` and `b` rather than
the symbols themselves. This issue is well known in the context of
natural languages, where words and sentences may be regarded either as
semantic entities or as character strings (syntactic entities). The
common practice in natural languages is to use quotation marks to
indicate that a word or a sentence is to be treated literally as a
string of characters. For instance, the first letter of "John" is
clearly "J." If we tell somebody "say your name aloud," we expect to
hear that person's name. However, if we tell somebody "say 'your name'
aloud," we expect to hear the words "your name." Note that we are forced
to nest quotation marks to describe what somebody else might say.[^98]

We can follow this same practice to identify lists and symbols that are
to be treated as data objects rather than as expressions to be
evaluated. However, our format for quoting differs from that of natural
languages in that we place a quotation mark (traditionally, the single
quote symbol `’`) only at the beginning of the object to be quoted. We
can get away with this in Scheme syntax because we rely on blanks and
parentheses to delimit objects. Thus, the meaning of the single quote
character is to quote the next object.[^99]

Now we can distinguish between symbols and their values:

::: scheme
(define a 1) (define b 2) (list a b)  *(1 2)*  (list 'a 'b)  *(a b)*
(list 'a b)  *(a 2)*
:::

Quotation also allows us to type in compound objects, using the
conventional printed representation for lists:[^100]

::: scheme
(car '(a b c))  *a*  (cdr '(a b c))  *(b c)*
:::

In keeping with this, we can obtain the empty list by evaluating `’()`,
and thus dispense with the variable `nil`.

One additional primitive used in manipulating symbols is `eq?`, which
takes two symbols as arguments and tests whether they are the
same.[^101] Using `eq?`, we can implement a useful procedure called
`memq`. This takes two arguments, a symbol and a list. If the symbol is
not contained in the list (i.e., is not `eq?` to any item in the list),
then `memq` returns false. Otherwise, it returns the sublist of the list
beginning with the first occurrence of the symbol:

::: scheme
(define (memq item x) (cond ((null? x) false) ((eq? item (car x)) x)
(else (memq item (cdr x)))))
:::

For example, the value of

::: scheme
(memq 'apple '(pear banana prune))
:::

is false, whereas the value of

::: scheme
(memq 'apple '(x (apple sauce) y apple pear))
:::

is `(apple pear)`.

> **[]{#Exercise 2.53 label="Exercise 2.53"}Exercise 2.53:** What would
> the interpreter print in response to evaluating each of the following
> expressions?
>
> ::: scheme
> (list 'a 'b 'c) (list (list 'george)) (cdr '((x1 x2) (y1 y2))) (cadr
> '((x1 x2) (y1 y2))) (pair? (car '(a short list))) (memq 'red '((red
> shoes) (blue socks))) (memq 'red '(red shoes blue socks))
> :::

> **[]{#Exercise 2.54 label="Exercise 2.54"}Exercise 2.54:** Two lists
> are said to be `equal?` if they contain equal elements arranged in the
> same order. For example,
>
> ::: scheme
> (equal? '(this is a list) '(this is a list))
> :::
>
> is true, but
>
> ::: scheme
> (equal? '(this is a list) '(this (is a) list))
> :::
>
> is false. To be more precise, we can define `equal?` recursively in
> terms of the basic `eq?` equality of symbols by saying that `a` and
> `b` are `equal?` if they are both symbols and the symbols are `eq?`,
> or if they are both lists such that `(car a)` is `equal?` to `(car b)`
> and `(cdr a)` is `equal?` to `(cdr b)`. Using this idea, implement
> `equal?` as a procedure.[^102]

> **[]{#Exercise 2.55 label="Exercise 2.55"}Exercise 2.55:** Eva Lu Ator
> types to the interpreter the expression
>
> ::: scheme
> (car "abracadabra)
> :::
>
> To her surprise, the interpreter prints back `quote`. Explain.

### Example: Symbolic Differentiation {#Section 2.3.2}

As an illustration of symbol manipulation and a further illustration of
data abstraction, consider the design of a procedure that performs
symbolic differentiation of algebraic expressions. We would like the
procedure to take as arguments an algebraic expression and a variable
and to return the derivative of the expression with respect to the
variable. For example, if the arguments to the procedure are
$ax^2 + bx + c$ and $x$, the procedure should return $2ax + b$. Symbolic
differentiation is of special historical significance in Lisp. It was
one of the motivating examples behind the development of a computer
language for symbol manipulation. Furthermore, it marked the beginning
of the line of research that led to the development of powerful systems
for symbolic mathematical work, which are currently being used by a
growing number of applied mathematicians and physicists.

In developing the symbolic-differentiation program, we will follow the
same strategy of data abstraction that we followed in developing the
rational-number system of [Section 2.1.1](#Section 2.1.1). That is, we
will first define a differentiation algorithm that operates on abstract
objects such as "sums," "products," and "variables" without worrying
about how these are to be represented. Only afterward will we address
the representation problem.

#### The differentiation program with abstract data {#the-differentiation-program-with-abstract-data .unnumbered}

In order to keep things simple, we will consider a very simple
symbolic-differentiation program that handles expressions that are built
up using only the operations of addition and multiplication with two
arguments. Differentiation of any such expression can be carried out by
applying the following reduction rules:

$${{\it dc} \over {\it dx}}       = 0,
    \quad {\rm for\ } c\ {\rm a\ constant\ or\ a\ variable\ different\ from\ } x,$$

$${{\it dx} \over {\it dx}}       = 1,$$

$${{\it d\,(u + v\,)} \over {\it dx}}     = {{\it du} \over {\it dx}} + {{\it dv} \over {\it dx}},$$

$${{\it d\,(uv\,)} \over {\it dx}}    = u {{\it dv} \over {\it dx}} + v {{\it du} \over {\it dx}}.$$

Observe that the latter two rules are recursive in nature. That is, to
obtain the derivative of a sum we first find the derivatives of the
terms and add them. Each of the terms may in turn be an expression that
needs to be decomposed. Decomposing into smaller and smaller pieces will
eventually produce pieces that are either constants or variables, whose
derivatives will be either 0 or 1.

To embody these rules in a procedure we indulge in a little wishful
thinking, as we did in designing the rational-number implementation. If
we had a means for representing algebraic expressions, we should be able
to tell whether an expression is a sum, a product, a constant, or a
variable. We should be able to extract the parts of an expression. For a
sum, for example we want to be able to extract the addend (first term)
and the augend (second term). We should also be able to construct
expressions from parts. Let us assume that we already have procedures to
implement the following selectors, constructors, and predicates:

::: scheme
(variable? e)  [Is `e` a variable?]{.roman}  (same-variable? v1 v2)
 [Are `v1` and `v2` the same variable?]{.roman}  (sum? e)  [Is `e` a
sum?]{.roman}  (addend e)  [Addend of the sum `e`.]{.roman}  (augend e)
 [Augend of the sum `e`.]{.roman}  (make-sum a1 a2)  [Construct the sum
of `a1` and `a2`.]{.roman}  (product? e)  [Is `e` a product?]{.roman}
(multiplier e)  [Multiplier of the product `e`.]{.roman}  (multiplicand
e)  [Multiplicand of the product `e`.]{.roman}  (make-product m1 m2)
 [Construct the product of `m1` and `m2`.]{.roman}
:::

Using these, and the primitive predicate `number?`, which identifies
numbers, we can express the differentiation rules as the following
procedure:

::: scheme
(define (deriv exp var) (cond ((number? exp) 0) ((variable? exp) (if
(same-variable? exp var) 1 0)) ((sum? exp) (make-sum (deriv (addend exp)
var) (deriv (augend exp) var))) ((product? exp) (make-sum (make-product
(multiplier exp) (deriv (multiplicand exp) var)) (make-product (deriv
(multiplier exp) var) (multiplicand exp)))) (else (error \"unknown
expression type: DERIV\" exp))))
:::

This `deriv` procedure incorporates the complete differentiation
algorithm. Since it is expressed in terms of abstract data, it will work
no matter how we choose to represent algebraic expressions, as long as
we design a proper set of selectors and constructors. This is the issue
we must address next.

#### Representing algebraic expressions {#representing-algebraic-expressions .unnumbered}

We can imagine many ways to use list structure to represent algebraic
expressions. For example, we could use lists of symbols that mirror the
usual algebraic notation, representing $ax + b$ as the list
`(a * x + b)`. However, one especially straightforward choice is to use
the same parenthesized prefix notation that Lisp uses for combinations;
that is, to represent $ax + b$ as `(+ (* a x) b)`. Then our data
representation for the differentiation problem is as follows:

-   The variables are symbols. They are identified by the primitive
    predicate `symbol?`:

    ::: scheme
    (define (variable? x) (symbol? x))
    :::

-   Two variables are the same if the symbols representing them are
    `eq?`:

    ::: scheme
    (define (same-variable? v1 v2) (and (variable? v1) (variable? v2)
    (eq? v1 v2)))
    :::

-   Sums and products are constructed as lists:

    ::: scheme
    (define (make-sum a1 a2) (list '+ a1 a2)) (define (make-product m1
    m2) (list '\* m1 m2))
    :::

-   A sum is a list whose first element is the symbol `+`:

    ::: scheme
    (define (sum? x) (and (pair? x) (eq? (car x) '+)))
    :::

-   The addend is the second item of the sum list:

    ::: scheme
    (define (addend s) (cadr s))
    :::

-   The augend is the third item of the sum list:

    ::: scheme
    (define (augend s) (caddr s))
    :::

-   A product is a list whose first element is the symbol `*`:

    ::: scheme
    (define (product? x) (and (pair? x) (eq? (car x) '\*)))
    :::

-   The multiplier is the second item of the product list:

    ::: scheme
    (define (multiplier p) (cadr p))
    :::

-   The multiplicand is the third item of the product list:

    ::: scheme
    (define (multiplicand p) (caddr p))
    :::

Thus, we need only combine these with the algorithm as embodied by
`deriv` in order to have a working symbolic-differentiation program. Let
us look at some examples of its behavior:

::: scheme
(deriv '(+ x 3) 'x)  *(+ 1 0)*  (deriv '(\* x y) 'x)  *(+ (\* x 0) (\* 1
y))*  (deriv '(\* (\* x y) (+ x 3)) 'x)  *(+ (\* (\* x y) (+ 1 0))*
 *(\* (+ (\* x 0) (\* 1 y))*   *(+ x 3)))*
:::

The program produces answers that are correct; however, they are
unsimplified. It is true that

$${{\it d\,(xy\,)} \over {\it dx}} = x \cdot 0 + 1 \cdot y,$$

but we would like the program to know that $x \cdot 0 = 0$,
$1 \cdot y = y$, and $0 + y = y$. The answer for the second example
should have been simply `y`. As the third example shows, this becomes a
serious issue when the expressions are complex.

Our difficulty is much like the one we encountered with the
rational-number implementation: we haven't reduced answers to simplest
form. To accomplish the rational-number reduction, we needed to change
only the constructors and the selectors of the implementation. We can
adopt a similar strategy here. We won't change `deriv` at all. Instead,
we will change `make/sum` so that if both summands are numbers,
`make/sum` will add them and return their sum. Also, if one of the
summands is 0, then `make/sum` will return the other summand.

::: scheme
(define (make-sum a1 a2) (cond ((=number? a1 0) a2) ((=number? a2 0) a1)
((and (number? a1) (number? a2)) (+ a1 a2)) (else (list '+ a1 a2))))
:::

This uses the procedure `=number?`, which checks whether an expression
is equal to a given number:

::: scheme
(define (=number? exp num) (and (number? exp) (= exp num)))
:::

Similarly, we will change `make/product` to build in the rules that 0
times anything is 0 and 1 times anything is the thing itself:

::: scheme
(define (make-product m1 m2) (cond ((or (=number? m1 0) (=number? m2 0))
0) ((=number? m1 1) m2) ((=number? m2 1) m1) ((and (number? m1) (number?
m2)) (\* m1 m2)) (else (list '\* m1 m2))))
:::

Here is how this version works on our three examples:

::: scheme
(deriv '(+ x 3) 'x)  *1*  (deriv '(\* x y) 'x)  *y*  (deriv '(\* (\* x
y) (+ x 3)) 'x)  *(+ (\* x y) (\* y (+ x 3)))*
:::

Although this is quite an improvement, the third example shows that
there is still a long way to go before we get a program that puts
expressions into a form that we might agree is "simplest." The problem
of algebraic simplification is complex because, among other reasons, a
form that may be simplest for one purpose may not be for another.

> **[]{#Exercise 2.56 label="Exercise 2.56"}Exercise 2.56:** Show how to
> extend the basic differentiator to handle more kinds of expressions.
> For instance, implement the differentiation rule
>
> $${{\it d\,(u^n\,)} \over {\it dx}} = nu^{n-1} {{\it du} \over {\it dx}}$$
>
> by adding a new clause to the `deriv` program and defining appropriate
> procedures `exponentiation?`, `base`, `exponent`, and
> `make/exponentiation`. (You may use the symbol `**` to denote
> exponentiation.) Build in the rules that anything raised to the power
> 0 is 1 and anything raised to the power 1 is the thing itself.

> **[]{#Exercise 2.57 label="Exercise 2.57"}Exercise 2.57:** Extend the
> differentiation program to handle sums and products of arbitrary
> numbers of (two or more) terms. Then the last example above could be
> expressed as
>
> ::: scheme
> (deriv '(\* x y (+ x 3)) 'x)
> :::
>
> Try to do this by changing only the representation for sums and
> products, without changing the `deriv` procedure at all. For example,
> the `addend` of a sum would be the first term, and the `augend` would
> be the sum of the rest of the terms.

> **[]{#Exercise 2.58 label="Exercise 2.58"}Exercise 2.58:** Suppose we
> want to modify the differentiation program so that it works with
> ordinary mathematical notation, in which `+` and `*` are infix rather
> than prefix operators. Since the differentiation program is defined in
> terms of abstract data, we can modify it to work with different
> representations of expressions solely by changing the predicates,
> selectors, and constructors that define the representation of the
> algebraic expressions on which the differentiator is to operate.
>
> a.  Show how to do this in order to differentiate algebraic
>     expressions presented in infix form, such as
>     `(x + (3 * (x + (y + 2))))`. To simplify the task, assume that `+`
>     and `*` always take two arguments and that expressions are fully
>     parenthesized.
>
> b.  The problem becomes substantially harder if we allow standard
>     algebraic notation, such as `(x + 3 * (x + y + 2))`, which drops
>     unnecessary parentheses and assumes that multiplication is done
>     before addition. Can you design appropriate predicates, selectors,
>     and constructors for this notation such that our derivative
>     program still works?

### Example: Representing Sets {#Section 2.3.3}

In the previous examples we built representations for two kinds of
compound data objects: rational numbers and algebraic expressions. In
one of these examples we had the choice of simplifying (reducing) the
expressions at either construction time or selection time, but other
than that the choice of a representation for these structures in terms
of lists was straightforward. When we turn to the representation of
sets, the choice of a representation is not so obvious. Indeed, there
are a number of possible representations, and they differ significantly
from one another in several ways.

Informally, a set is simply a collection of distinct objects. To give a
more precise definition we can employ the method of data abstraction.
That is, we define "set" by specifying the operations that are to be
used on sets. These are `union/set`, `intersection/set`,
`element/of/set?`, and `adjoin/set`. `element/of/set?` is a predicate
that determines whether a given element is a member of a set.
`adjoin/set` takes an object and a set as arguments and returns a set
that contains the elements of the original set and also the adjoined
element. `union/set` computes the union of two sets, which is the set
containing each element that appears in either argument.
`intersection/set` computes the intersection of two sets, which is the
set containing only elements that appear in both arguments. From the
viewpoint of data abstraction, we are free to design any representation
that implements these operations in a way consistent with the
interpretations given above.[^103]

#### Sets as unordered lists {#sets-as-unordered-lists .unnumbered}

One way to represent a set is as a list of its elements in which no
element appears more than once. The empty set is represented by the
empty list. In this representation, `element/of/set?` is similar to the
procedure `memq` of [Section 2.3.1](#Section 2.3.1). It uses `equal?`
instead of `eq?` so that the set elements need not be symbols:

::: scheme
(define (element-of-set? x set) (cond ((null? set) false) ((equal? x
(car set)) true) (else (element-of-set? x (cdr set)))))
:::

Using this, we can write `adjoin/set`. If the object to be adjoined is
already in the set, we just return the set. Otherwise, we use `cons` to
add the object to the list that represents the set:

::: scheme
(define (adjoin-set x set) (if (element-of-set? x set) set (cons x
set)))
:::

For `intersection/set` we can use a recursive strategy. If we know how
to form the intersection of `set2` and the `cdr` of `set1`, we only need
to decide whether to include the `car` of `set1` in this. But this
depends on whether `(car set1)` is also in `set2`. Here is the resulting
procedure:

::: scheme
(define (intersection-set set1 set2) (cond ((or (null? set1) (null?
set2)) '()) ((element-of-set? (car set1) set2) (cons (car set1)
(intersection-set (cdr set1) set2))) (else (intersection-set (cdr set1)
set2))))
:::

In designing a representation, one of the issues we should be concerned
with is efficiency. Consider the number of steps required by our set
operations. Since they all use `element/of/set?`, the speed of this
operation has a major impact on the efficiency of the set implementation
as a whole. Now, in order to check whether an object is a member of a
set, `element/of/set?` may have to scan the entire set. (In the worst
case, the object turns out not to be in the set.) Hence, if the set has
$n$ elements, `element/of/set?` might take up to $n$ steps. Thus, the
number of steps required grows as $\Theta(n)$. The number of steps
required by `adjoin/set`, which uses this operation, also grows as
$\Theta(n)$. For `intersection/set`, which does an `element/of/set?`
check for each element of `set1`, the number of steps required grows as
the product of the sizes of the sets involved, or $\Theta(n^2)$ for two
sets of size $n$. The same will be true of `union/set`.

> **[]{#Exercise 2.59 label="Exercise 2.59"}Exercise 2.59:** Implement
> the `union/set` operation for the unordered-list representation of
> sets.

> **[]{#Exercise 2.60 label="Exercise 2.60"}Exercise 2.60:** We
> specified that a set would be represented as a list with no
> duplicates. Now suppose we allow duplicates. For instance, the set
> $\{1, 2, 3\}$ could be represented as the list `(2 3 2 1 3 2 2)`.
> Design procedures `element/of/set?`, `adjoin/set`, `union/set`, and
> `intersection/set` that operate on this representation. How does the
> efficiency of each compare with the corresponding procedure for the
> non-duplicate representation? Are there applications for which you
> would use this representation in preference to the non-duplicate one?

#### Sets as ordered lists {#sets-as-ordered-lists .unnumbered}

One way to speed up our set operations is to change the representation
so that the set elements are listed in increasing order. To do this, we
need some way to compare two objects so that we can say which is bigger.
For example, we could compare symbols lexicographically, or we could
agree on some method for assigning a unique number to an object and then
compare the elements by comparing the corresponding numbers. To keep our
discussion simple, we will consider only the case where the set elements
are numbers, so that we can compare elements using `>` and `<`. We will
represent a set of numbers by listing its elements in increasing order.
Whereas our first representation above allowed us to represent the set
$\{1, 3, 6, 10\}$ by listing the elements in any order, our new
representation allows only the list `(1 3 6 10)`.

One advantage of ordering shows up in `element/of/set?`: In checking for
the presence of an item, we no longer have to scan the entire set. If we
reach a set element that is larger than the item we are looking for,
then we know that the item is not in the set:

::: scheme
(define (element-of-set? x set) (cond ((null? set) false) ((= x (car
set)) true) ((\< x (car set)) false) (else (element-of-set? x (cdr
set)))))
:::

How many steps does this save? In the worst case, the item we are
looking for may be the largest one in the set, so the number of steps is
the same as for the unordered representation. On the other hand, if we
search for items of many different sizes we can expect that sometimes we
will be able to stop searching at a point near the beginning of the list
and that other times we will still need to examine most of the list. On
the average we should expect to have to examine about half of the items
in the set. Thus, the average number of steps required will be about
$n / 2$. This is still $\Theta(n)$ growth, but it does save us, on the
average, a factor of 2 in number of steps over the previous
implementation.

We obtain a more impressive speedup with `intersection/set`. In the
unordered representation this operation required $\Theta(n^2)$ steps,
because we performed a complete scan of `set2` for each element of
`set1`. But with the ordered representation, we can use a more clever
method. Begin by comparing the initial elements, `x1` and `x2`, of the
two sets. If `x1` equals `x2`, then that gives an element of the
intersection, and the rest of the intersection is the intersection of
the `cdr`-s of the two sets. Suppose, however, that `x1` is less than
`x2`. Since `x2` is the smallest element in `set2`, we can immediately
conclude that `x1` cannot appear anywhere in `set2` and hence is not in
the intersection. Hence, the intersection is equal to the intersection
of `set2` with the `cdr` of `set1`. Similarly, if `x2` is less than
`x1`, then the intersection is given by the intersection of `set1` with
the `cdr` of `set2`. Here is the procedure:

::: scheme
(define (intersection-set set1 set2) (if (or (null? set1) (null? set2))
'() (let ((x1 (car set1)) (x2 (car set2))) (cond ((= x1 x2) (cons x1
(intersection-set (cdr set1) (cdr set2)))) ((\< x1 x2) (intersection-set
(cdr set1) set2)) ((\< x2 x1) (intersection-set set1 (cdr set2)))))))
:::

To estimate the number of steps required by this process, observe that
at each step we reduce the intersection problem to computing
intersections of smaller sets---removing the first element from `set1`
or `set2` or both. Thus, the number of steps required is at most the sum
of the sizes of `set1` and `set2`, rather than the product of the sizes
as with the unordered representation. This is $\Theta(n)$ growth rather
than $\Theta(n^2)$---a considerable speedup, even for sets of moderate
size.

> **[]{#Exercise 2.61 label="Exercise 2.61"}Exercise 2.61:** Give an
> implementation of `adjoin/set` using the ordered representation. By
> analogy with `element/of/set?` show how to take advantage of the
> ordering to produce a procedure that requires on the average about
> half as many steps as with the unordered representation.

> **[]{#Exercise 2.62 label="Exercise 2.62"}Exercise 2.62:** Give a
> $\Theta(n)$ implementation of `union/set` for sets represented as
> ordered lists.

#### Sets as binary trees {#sets-as-binary-trees .unnumbered}

We can do better than the ordered-list representation by arranging the
set elements in the form of a tree. Each node of the tree holds one
element of the set, called the "entry" at that node, and a link to each
of two other (possibly empty) nodes. The "left" link points to elements
smaller than the one at the node, and the "right" link to elements
greater than the one at the node. [Figure 2.16](#Figure 2.16) shows some
trees that represent the set $\{1, 3, 5, 7, 9, 11\}$. The same set may
be represented by a tree in a number of different ways. The only thing
we require for a valid representation is that all elements in the left
subtree be smaller than the node entry and that all elements in the
right subtree be larger.

[]{#Figure 2.16 label="Figure 2.16"}

![image](fig/chap2/Fig2.16b.pdf){width="70mm"}

> **Figure 2.16:** Various binary trees that represent the set
> $\{1, 3, 5, 7, 9, 11\}$.

The advantage of the tree representation is this: Suppose we want to
check whether a number $x$ is contained in a set. We begin by comparing
$x$ with the entry in the top node. If $x$ is less than this, we know
that we need only search the left subtree; if $x$ is greater, we need
only search the right subtree. Now, if the tree is "balanced," each of
these subtrees will be about half the size of the original. Thus, in one
step we have reduced the problem of searching a tree of size $n$ to
searching a tree of size $n / 2$. Since the size of the tree is halved
at each step, we should expect that the number of steps needed to search
a tree of size $n$ grows as $\Theta(\log n)$.[^104] For large sets, this
will be a significant speedup over the previous representations.

We can represent trees by using lists. Each node will be a list of three
items: the entry at the node, the left subtree, and the right subtree. A
left or a right subtree of the empty list will indicate that there is no
subtree connected there. We can describe this representation by the
following procedures:[^105]

::: scheme
(define (entry tree) (car tree)) (define (left-branch tree) (cadr tree))
(define (right-branch tree) (caddr tree)) (define (make-tree entry left
right) (list entry left right))
:::

Now we can write the `element/of/set?` procedure using the strategy
described above:

::: scheme
(define (element-of-set? x set) (cond ((null? set) false) ((= x (entry
set)) true) ((\< x (entry set)) (element-of-set? x (left-branch set)))
((\> x (entry set)) (element-of-set? x (right-branch set)))))
:::

Adjoining an item to a set is implemented similarly and also requires
$\Theta(\log n)$ steps. To adjoin an item `x`, we compare `x` with the
node entry to determine whether `x` should be added to the right or to
the left branch, and having adjoined `x` to the appropriate branch we
piece this newly constructed branch together with the original entry and
the other branch. If `x` is equal to the entry, we just return the node.
If we are asked to adjoin `x` to an empty tree, we generate a tree that
has `x` as the entry and empty right and left branches. Here is the
procedure:

::: scheme
(define (adjoin-set x set) (cond ((null? set) (make-tree x '() '())) ((=
x (entry set)) set) ((\< x (entry set)) (make-tree (entry set)
(adjoin-set x (left-branch set)) (right-branch set))) ((\> x (entry
set)) (make-tree (entry set) (left-branch set) (adjoin-set x
(right-branch set))))))
:::

The above claim that searching the tree can be performed in a
logarithmic number of steps rests on the assumption that the tree is
"balanced," i.e., that the left and the right subtree of every tree have
approximately the same number of elements, so that each subtree contains
about half the elements of its parent. But how can we be certain that
the trees we construct will be balanced? Even if we start with a
balanced tree, adding elements with `adjoin/set` may produce an
unbalanced result. Since the position of a newly adjoined element
depends on how the element compares with the items already in the set,
we can expect that if we add elements "randomly" the tree will tend to
be balanced on the average. But this is not a guarantee. For example, if
we start with an empty set and adjoin the numbers 1 through 7 in
sequence we end up with the highly unbalanced tree shown in [Figure
2.17](#Figure 2.17). In this tree all the left subtrees are empty, so it
has no advantage over a simple ordered list. One way to solve this
problem is to define an operation that transforms an arbitrary tree into
a balanced tree with the same elements. Then we can perform this
transformation after every few `adjoin/set` operations to keep our set
in balance. There are also other ways to solve this problem, most of
which involve designing new data structures for which searching and
insertion both can be done in $\Theta(\log n)$ steps.[^106]

[]{#Figure 2.17 label="Figure 2.17"}

![image](fig/chap2/Fig2.17a.pdf){width="40mm"}

> **Figure 2.17:** Unbalanced tree produced by adjoining 1 through 7 in
> sequence.

> **[]{#Exercise 2.63 label="Exercise 2.63"}Exercise 2.63:** Each of the
> following two procedures converts a binary tree to a list.
>
> ::: scheme
> (define (tree-\>list-1 tree) (if (null? tree) '() (append
> (tree-\>list-1 (left-branch tree)) (cons (entry tree) (tree-\>list-1
> (right-branch tree)))))) (define (tree-\>list-2 tree) (define
> (copy-to-list tree result-list) (if (null? tree) result-list
> (copy-to-list (left-branch tree) (cons (entry tree) (copy-to-list
> (right-branch tree) result-list))))) (copy-to-list tree '()))
> :::
>
> a.  Do the two procedures produce the same result for every tree? If
>     not, how do the results differ? What lists do the two procedures
>     produce for the trees in [Figure 2.16](#Figure 2.16)?
>
> b.  Do the two procedures have the same order of growth in the number
>     of steps required to convert a balanced tree with $n$ elements to
>     a list? If not, which one grows more slowly?

> **[]{#Exercise 2.64 label="Exercise 2.64"}Exercise 2.64:** The
> following procedure `list/>tree` converts an ordered list to a
> balanced binary tree. The helper procedure `partial/tree` takes as
> arguments an integer $n$ and list of at least $n$ elements and
> constructs a balanced tree containing the first $n$ elements of the
> list. The result returned by `partial/tree` is a pair (formed with
> `cons`) whose `car` is the constructed tree and whose `cdr` is the
> list of elements not included in the tree.
>
> ::: scheme
> (define (list-\>tree elements) (car (partial-tree elements (length
> elements)))) (define (partial-tree elts n) (if (= n 0) (cons '() elts)
> (let ((left-size (quotient (- n 1) 2))) (let ((left-result
> (partial-tree elts left-size))) (let ((left-tree (car left-result))
> (non-left-elts (cdr left-result)) (right-size (- n (+ left-size 1))))
> (let ((this-entry (car non-left-elts)) (right-result (partial-tree
> (cdr non-left-elts) right-size))) (let ((right-tree (car
> right-result)) (remaining-elts (cdr right-result))) (cons (make-tree
> this-entry left-tree right-tree) remaining-elts))))))))
> :::
>
> a.  Write a short paragraph explaining as clearly as you can how
>     `partial/tree` works. Draw the tree produced by `list/>tree` for
>     the list `(1 3 5 7 9 11)`.
>
> b.  What is the order of growth in the number of steps required by
>     `list/>tree` to convert a list of $n$ elements?

> **[]{#Exercise 2.65 label="Exercise 2.65"}Exercise 2.65:** Use the
> results of [Exercise 2.63](#Exercise 2.63) and [Exercise
> 2.64](#Exercise 2.64) to give $\Theta(n)$ implementations of
> `union/set` and `intersection/set` for sets implemented as (balanced)
> binary trees.[^107]

#### Sets and information retrieval {#sets-and-information-retrieval .unnumbered}

We have examined options for using lists to represent sets and have seen
how the choice of representation for a data object can have a large
impact on the performance of the programs that use the data. Another
reason for concentrating on sets is that the techniques discussed here
appear again and again in applications involving information retrieval.

Consider a data base containing a large number of individual records,
such as the personnel files for a company or the transactions in an
accounting system. A typical data-management system spends a large
amount of time accessing or modifying the data in the records and
therefore requires an efficient method for accessing records. This is
done by identifying a part of each record to serve as an identifying
*key*. A key can be anything that uniquely identifies the record. For a
personnel file, it might be an employee's id number. For
an accounting system, it might be a transaction number. Whatever the key
is, when we define the record as a data structure we should include a
`key` selector procedure that retrieves the key associated with a given
record.

Now we represent the data base as a set of records. To locate the record
with a given key we use a procedure `lookup`, which takes as arguments a
key and a data base and which returns the record that has that key, or
false if there is no such record. `lookup` is implemented in almost the
same way as `element/of/set?`. For example, if the set of records is
implemented as an unordered list, we could use

::: scheme
(define (lookup given-key set-of-records) (cond ((null? set-of-records)
false) ((equal? given-key (key (car set-of-records))) (car
set-of-records)) (else (lookup given-key (cdr set-of-records)))))
:::

Of course, there are better ways to represent large sets than as
unordered lists. Information-retrieval systems in which records have to
be "randomly accessed" are typically implemented by a tree-based method,
such as the binary-tree representation discussed previously. In
designing such a system the methodology of data abstraction can be a
great help. The designer can create an initial implementation using a
simple, straightforward representation such as unordered lists. This
will be unsuitable for the eventual system, but it can be useful in
providing a "quick and dirty" data base with which to test the rest of
the system. Later on, the data representation can be modified to be more
sophisticated. If the data base is accessed in terms of abstract
selectors and constructors, this change in representation will not
require any changes to the rest of the system.

> **[]{#Exercise 2.66 label="Exercise 2.66"}Exercise 2.66:** Implement
> the `lookup` procedure for the case where the set of records is
> structured as a binary tree, ordered by the numerical values of the
> keys.

### Example: Huffman Encoding Trees {#Section 2.3.4}

This section provides practice in the use of list structure and data
abstraction to manipulate sets and trees. The application is to methods
for representing data as sequences of ones and zeros (bits). For
example, the ascii standard code used to represent text in
computers encodes each character as a sequence of seven bits. Using
seven bits allows us to distinguish $2^7$, or 128, possible different
characters. In general, if we want to distinguish $n$ different symbols,
we will need to use $\log_2\!n$ bits per symbol. If all our messages are
made up of the eight symbols A, B, C, D, E, F, G, and H, we can choose a
code with three bits per character, for example

A 000 C 010 E 100 G 110 B 001 D 011 F 101 H 111

With this code, the message

BACADAEAFABBAAAGAH

is encoded as the string of 54 bits

001000010000011000100000101000001001000000000110000111

Codes such as ascii and the A-through-H code above are
known as *fixed-length* codes, because they represent each symbol in the
message with the same number of bits. It is sometimes advantageous to
use *variable-length* codes, in which different symbols may be
represented by different numbers of bits. For example, Morse code does
not use the same number of dots and dashes for each letter of the
alphabet. In particular, E, the most frequent letter, is represented by
a single dot. In general, if our messages are such that some symbols
appear very frequently and some very rarely, we can encode data more
efficiently (i.e., using fewer bits per message) if we assign shorter
codes to the frequent symbols. Consider the following alternative code
for the letters A through H:

A 0 C 1010 E 1100 G 1110 B 100 D 1011 F 1101 H 1111

With this code, the same message as above is encoded as the string

100010100101101100011010100100000111001111

This string contains 42 bits, so it saves more than 20% in space in
comparison with the fixed-length code shown above.

One of the difficulties of using a variable-length code is knowing when
you have reached the end of a symbol in reading a sequence of zeros and
ones. Morse code solves this problem by using a special *separator code*
(in this case, a pause) after the sequence of dots and dashes for each
letter. Another solution is to design the code in such a way that no
complete code for any symbol is the beginning (or *prefix*) of the code
for another symbol. Such a code is called a *prefix code*. In the
example above, A is encoded by 0 and B is encoded by 100, so no other
symbol can have a code that begins with 0 or with 100.

In general, we can attain significant savings if we use variable-length
prefix codes that take advantage of the relative frequencies of the
symbols in the messages to be encoded. One particular scheme for doing
this is called the Huffman encoding method, after its discoverer, David
Huffman. A Huffman code can be represented as a binary tree whose leaves
are the symbols that are encoded. At each non-leaf node of the tree
there is a set containing all the symbols in the leaves that lie below
the node. In addition, each symbol at a leaf is assigned a weight (which
is its relative frequency), and each non-leaf node contains a weight
that is the sum of all the weights of the leaves lying below it. The
weights are not used in the encoding or the decoding process. We will
see below how they are used to help construct the tree.

[Figure 2.18](#Figure 2.18) shows the Huffman tree for the A-through-H
code given above. The weights at the leaves indicate that the tree was
designed for messages in which A appears with relative frequency 8, B
with relative frequency 3, and the other letters each with relative
frequency 1.

[]{#Figure 2.18 label="Figure 2.18"}

![image](fig/chap2/Fig2.18a.pdf){width="81mm"}

**Figure 2.18:** A Huffman encoding tree.

Given a Huffman tree, we can find the encoding of any symbol by starting
at the root and moving down until we reach the leaf that holds the
symbol. Each time we move down a left branch we add a 0 to the code, and
each time we move down a right branch we add a 1. (We decide which
branch to follow by testing to see which branch either is the leaf node
for the symbol or contains the symbol in its set.) For example, starting
from the root of the tree in [Figure 2.18](#Figure 2.18), we arrive at
the leaf for D by following a right branch, then a left branch, then a
right branch, then a right branch; hence, the code for D is 1011.

To decode a bit sequence using a Huffman tree, we begin at the root and
use the successive zeros and ones of the bit sequence to determine
whether to move down the left or the right branch. Each time we come to
a leaf, we have generated a new symbol in the message, at which point we
start over from the root of the tree to find the next symbol. For
example, suppose we are given the tree above and the sequence 10001010.
Starting at the root, we move down the right branch, (since the first
bit of the string is 1), then down the left branch (since the second bit
is 0), then down the left branch (since the third bit is also 0). This
brings us to the leaf for B, so the first symbol of the decoded message
is B. Now we start again at the root, and we make a left move because
the next bit in the string is 0. This brings us to the leaf for A. Then
we start again at the root with the rest of the string 1010, so we move
right, left, right, left and reach C. Thus, the entire message is BAC.

#### Generating Huffman trees {#generating-huffman-trees .unnumbered}

Given an "alphabet" of symbols and their relative frequencies, how do we
construct the "best" code? (In other words, which tree will encode
messages with the fewest bits?) Huffman gave an algorithm for doing this
and showed that the resulting code is indeed the best variable-length
code for messages where the relative frequency of the symbols matches
the frequencies with which the code was constructed. We will not prove
this optimality of Huffman codes here, but we will show how Huffman
trees are constructed.[^108]

The algorithm for generating a Huffman tree is very simple. The idea is
to arrange the tree so that the symbols with the lowest frequency appear
farthest away from the root. Begin with the set of leaf nodes,
containing symbols and their frequencies, as determined by the initial
data from which the code is to be constructed. Now find two leaves with
the lowest weights and merge them to produce a node that has these two
nodes as its left and right branches. The weight of the new node is the
sum of the two weights. Remove the two leaves from the original set and
replace them by this new node. Now continue this process. At each step,
merge two nodes with the smallest weights, removing them from the set
and replacing them with a node that has these two as its left and right
branches. The process stops when there is only one node left, which is
the root of the entire tree. Here is how the Huffman tree of [Figure
2.18](#Figure 2.18) was generated:

Initial leaves (A 8) (B 3) (C 1) (D 1) (E 1) (F 1) (G 1) (H 1) Merge (A
8) (B 3) (C D 2) (E 1) (F 1) (G 1) (H 1) Merge (A 8) (B 3) (C D 2) (E F
2) (G 1) (H 1) Merge (A 8) (B 3) (C D 2) (E F 2) (G H 2) Merge (A 8) (B
3) (C D 2) (E F G H 4) Merge (A 8) (B C D 5) (E F G H 4) Merge (A 8) (B
C D E F G H 9) Final merge (A B C D E F G H 17)

The algorithm does not always specify a unique tree, because there may
not be unique smallest-weight nodes at each step. Also, the choice of
the order in which the two nodes are merged (i.e., which will be the
right branch and which will be the left branch) is arbitrary.

#### Representing Huffman trees {#representing-huffman-trees .unnumbered}

In the exercises below we will work with a system that uses Huffman
trees to encode and decode messages and generates Huffman trees
according to the algorithm outlined above. We will begin by discussing
how trees are represented.

Leaves of the tree are represented by a list consisting of the symbol
`leaf`, the symbol at the leaf, and the weight:

::: scheme
(define (make-leaf symbol weight) (list 'leaf symbol weight)) (define
(leaf? object) (eq? (car object) 'leaf)) (define (symbol-leaf x) (cadr
x)) (define (weight-leaf x) (caddr x))
:::

A general tree will be a list of a left branch, a right branch, a set of
symbols, and a weight. The set of symbols will be simply a list of the
symbols, rather than some more sophisticated set representation. When we
make a tree by merging two nodes, we obtain the weight of the tree as
the sum of the weights of the nodes, and the set of symbols as the union
of the sets of symbols for the nodes. Since our symbol sets are
represented as lists, we can form the union by using the `append`
procedure we defined in [Section 2.2.1](#Section 2.2.1):

::: scheme
(define (make-code-tree left right) (list left right (append (symbols
left) (symbols right)) (+ (weight left) (weight right))))
:::

If we make a tree in this way, we have the following selectors:

::: scheme
(define (left-branch tree) (car tree)) (define (right-branch tree) (cadr
tree)) (define (symbols tree) (if (leaf? tree) (list (symbol-leaf tree))
(caddr tree))) (define (weight tree) (if (leaf? tree) (weight-leaf tree)
(cadddr tree)))
:::

The procedures `symbols` and `weight` must do something slightly
different depending on whether they are called with a leaf or a general
tree. These are simple examples of *generic procedures* (procedures that
can handle more than one kind of data), which we will have much more to
say about in [Section 2.4](#Section 2.4) and [Section
2.5](#Section 2.5).

#### The decoding procedure {#the-decoding-procedure .unnumbered}

The following procedure implements the decoding algorithm. It takes as
arguments a list of zeros and ones, together with a Huffman tree.

::: scheme
(define (decode bits tree) (define (decode-1 bits current-branch) (if
(null? bits) '() (let ((next-branch (choose-branch (car bits)
current-branch))) (if (leaf? next-branch) (cons (symbol-leaf
next-branch) (decode-1 (cdr bits) tree)) (decode-1 (cdr bits)
next-branch))))) (decode-1 bits tree)) (define (choose-branch bit
branch) (cond ((= bit 0) (left-branch branch)) ((= bit 1) (right-branch
branch)) (else (error \"bad bit: CHOOSE-BRANCH\" bit))))
:::

The procedure `decode/1` takes two arguments: the list of remaining bits
and the current position in the tree. It keeps moving "down" the tree,
choosing a left or a right branch according to whether the next bit in
the list is a zero or a one. (This is done with the procedure
`choose/branch`.) When it reaches a leaf, it returns the symbol at that
leaf as the next symbol in the message by `cons`ing it onto the result
of decoding the rest of the message, starting at the root of the tree.
Note the error check in the final clause of `choose/branch`, which
complains if the procedure finds something other than a zero or a one in
the input data.

#### Sets of weighted elements {#sets-of-weighted-elements .unnumbered}

In our representation of trees, each non-leaf node contains a set of
symbols, which we have represented as a simple list. However, the
tree-generating algorithm discussed above requires that we also work
with sets of leaves and trees, successively merging the two smallest
items. Since we will be required to repeatedly find the smallest item in
a set, it is convenient to use an ordered representation for this kind
of set.

We will represent a set of leaves and trees as a list of elements,
arranged in increasing order of weight. The following `adjoin/set`
procedure for constructing sets is similar to the one described in
[Exercise 2.61](#Exercise 2.61); however, items are compared by their
weights, and the element being added to the set is never already in it.

::: scheme
(define (adjoin-set x set) (cond ((null? set) (list x)) ((\< (weight x)
(weight (car set))) (cons x set)) (else (cons (car set) (adjoin-set x
(cdr set))))))
:::

The following procedure takes a list of symbol-frequency pairs such as
`((A 4) (B 2) (C 1) (D 1))` and constructs an initial ordered set of
leaves, ready to be merged according to the Huffman algorithm:

::: scheme
(define (make-leaf-set pairs) (if (null? pairs) '() (let ((pair (car
pairs))) (adjoin-set (make-leaf (car pair)  [; symbol]{.roman}  (cadr
pair))  [; frequency]{.roman}  (make-leaf-set (cdr pairs))))))
:::

> **[]{#Exercise 2.67 label="Exercise 2.67"}Exercise 2.67:** Define an
> encoding tree and a sample message:
>
> ::: scheme
> (define sample-tree (make-code-tree (make-leaf 'A 4) (make-code-tree
> (make-leaf 'B 2) (make-code-tree (make-leaf 'D 1) (make-leaf 'C 1)))))
> (define sample-message '(0 1 1 0 0 1 0 1 0 1 1 1 0))
> :::
>
> Use the `decode` procedure to decode the message, and give the result.

> **[]{#Exercise 2.68 label="Exercise 2.68"}Exercise 2.68:** The
> `encode` procedure takes as arguments a message and a tree and
> produces the list of bits that gives the encoded message.
>
> ::: scheme
> (define (encode message tree) (if (null? message) '() (append
> (encode-symbol (car message) tree) (encode (cdr message) tree))))
> :::
>
> `encode/symbol` is a procedure, which you must write, that returns the
> list of bits that encodes a given symbol according to a given tree.
> You should design `encode/symbol` so that it signals an error if the
> symbol is not in the tree at all. Test your procedure by encoding the
> result you obtained in [Exercise 2.67](#Exercise 2.67) with the sample
> tree and seeing whether it is the same as the original sample message.

> **[]{#Exercise 2.69 label="Exercise 2.69"}Exercise 2.69:** The
> following procedure takes as its argument a list of symbol-frequency
> pairs (where no symbol appears in more than one pair) and generates a
> Huffman encoding tree according to the Huffman algorithm.
>
> ::: scheme
> (define (generate-huffman-tree pairs) (successive-merge (make-leaf-set
> pairs)))
> :::
>
> `make/leaf/set` is the procedure given above that transforms the list
> of pairs into an ordered set of leaves. `successive/merge` is the
> procedure you must write, using `make/code/tree` to successively merge
> the smallest-weight elements of the set until there is only one
> element left, which is the desired Huffman tree. (This procedure is
> slightly tricky, but not really complicated. If you find yourself
> designing a complex procedure, then you are almost certainly doing
> something wrong. You can take significant advantage of the fact that
> we are using an ordered set representation.)

> **[]{#Exercise 2.70 label="Exercise 2.70"}Exercise 2.70:** The
> following eight-symbol alphabet with associated relative frequencies
> was designed to efficiently encode the lyrics of 1950s rock songs.
> (Note that the "symbols" of an "alphabet" need not be individual
> letters.)
>
> A 2 GET 2 SHA 3 WAH 1 BOOM 1 JOB 2 NA 16 YIP 9
>
> Use `generate/huffman/tree` ([Exercise 2.69](#Exercise 2.69)) to
> generate a corresponding Huffman tree, and use `encode` ([Exercise
> 2.68](#Exercise 2.68)) to encode the following message:
>
> Get a job Sha na na na na na na na na Get a job Sha na na na na na na
> na na Wah yip yip yip yip yip yip yip yip yip Sha boom
>
> How many bits are required for the encoding? What is the smallest
> number of bits that would be needed to encode this song if we used a
> fixed-length code for the eight-symbol alphabet?

> **[]{#Exercise 2.71 label="Exercise 2.71"}Exercise 2.71:** Suppose we
> have a Huffman tree for an alphabet of $n$ symbols, and that the
> relative frequencies of the symbols are $1, 2, 4, \dots, 2^{n-1}$.
> Sketch the tree for $n=5$; for $n=10$. In such a tree (for general
> $n$) how many bits are required to encode the most frequent symbol?
> The least frequent symbol?

> **[]{#Exercise 2.72 label="Exercise 2.72"}Exercise 2.72:** Consider
> the encoding procedure that you designed in [Exercise
> 2.68](#Exercise 2.68). What is the order of growth in the number of
> steps needed to encode a symbol? Be sure to include the number of
> steps needed to search the symbol list at each node encountered. To
> answer this question in general is difficult. Consider the special
> case where the relative frequencies of the $n$ symbols are as
> described in [Exercise 2.71](#Exercise 2.71), and give the order of
> growth (as a function of $n$) of the number of steps needed to encode
> the most frequent and least frequent symbols in the alphabet.

## Multiple Representations for Abstract Data {#Section 2.4}

We have introduced data abstraction, a methodology for structuring
systems in such a way that much of a program can be specified
independent of the choices involved in implementing the data objects
that the program manipulates. For example, we saw in [Section
2.1.1](#Section 2.1.1) how to separate the task of designing a program
that uses rational numbers from the task of implementing rational
numbers in terms of the computer language's primitive mechanisms for
constructing compound data. The key idea was to erect an abstraction
barrier---in this case, the selectors and constructors for rational
numbers (`make/rat`, `numer`, `denom`)---that isolates the way rational
numbers are used from their underlying representation in terms of list
structure. A similar abstraction barrier isolates the details of the
procedures that perform rational arithmetic (`add/rat`, `sub/rat`,
`mul/rat`, and `div/rat`) from the "higher-level" procedures that use
rational numbers. The resulting program has the structure shown in
[Figure 2.1](#Figure 2.1).

These data-abstraction barriers are powerful tools for controlling
complexity. By isolating the underlying representations of data objects,
we can divide the task of designing a large program into smaller tasks
that can be performed separately. But this kind of data abstraction is
not yet powerful enough, because it may not always make sense to speak
of "the underlying representation" for a data object.

For one thing, there might be more than one useful representation for a
data object, and we might like to design systems that can deal with
multiple representations. To take a simple example, complex numbers may
be represented in two almost equivalent ways: in rectangular form (real
and imaginary parts) and in polar form (magnitude and angle). Sometimes
rectangular form is more appropriate and sometimes polar form is more
appropriate. Indeed, it is perfectly plausible to imagine a system in
which complex numbers are represented in both ways, and in which the
procedures for manipulating complex numbers work with either
representation.

More importantly, programming systems are often designed by many people
working over extended periods of time, subject to requirements that
change over time. In such an environment, it is simply not possible for
everyone to agree in advance on choices of data representation. So in
addition to the data-abstraction barriers that isolate representation
from use, we need abstraction barriers that isolate different design
choices from each other and permit different choices to coexist in a
single program. Furthermore, since large programs are often created by
combining pre-existing modules that were designed in isolation, we need
conventions that permit programmers to incorporate modules into larger
systems *additively*, that is, without having to redesign or reimplement
these modules.

In this section, we will learn how to cope with data that may be
represented in different ways by different parts of a program. This
requires constructing *generic procedures*---procedures that can operate
on data that may be represented in more than one way. Our main technique
for building generic procedures will be to work in terms of data objects
that have *type tags*, that is, data objects that include explicit
information about how they are to be processed. We will also discuss
*data-directed* programming, a powerful and convenient implementation
strategy for additively assembling systems with generic operations.

We begin with the simple complex-number example. We will see how type
tags and data-directed style enable us to design separate rectangular
and polar representations for complex numbers while maintaining the
notion of an abstract "complex-number" data object. We will accomplish
this by defining arithmetic procedures for complex numbers
(`add/complex`, `sub/complex`, `mul/complex`, and `div/complex`) in
terms of generic selectors that access parts of a complex number
independent of how the number is represented. The resulting
complex-number system, as shown in [Figure 2.19](#Figure 2.19), contains
two different kinds of abstraction barriers. The "horizontal"
abstraction barriers play the same role as the ones in [Figure
2.1](#Figure 2.1). They isolate "higher-level" operations from
"lower-level" representations. In addition, there is a "vertical"
barrier that gives us the ability to separately design and install
alternative representations.

[]{#Figure 2.19 label="Figure 2.19"}

![image](fig/chap2/Fig2.19a.pdf){width="108mm"}

> **Figure 2.19:** Data-abstraction barriers in the complex-number
> system.

In [Section 2.5](#Section 2.5) we will show how to use type tags and
data-directed style to develop a generic arithmetic package. This
provides procedures (`add`, `mul`, and so on) that can be used to
manipulate all sorts of "numbers" and can be easily extended when a new
kind of number is needed. In [Section 2.5.3](#Section 2.5.3), we'll show
how to use generic arithmetic in a system that performs symbolic
algebra.

### Representations for Complex Numbers {#Section 2.4.1}

We will develop a system that performs arithmetic operations on complex
numbers as a simple but unrealistic example of a program that uses
generic operations. We begin by discussing two plausible representations
for complex numbers as ordered pairs: rectangular form (real part and
imaginary part) and polar form (magnitude and angle).[^109] [Section
2.4.2](#Section 2.4.2) will show how both representations can be made to
coexist in a single system through the use of type tags and generic
operations.

Like rational numbers, complex numbers are naturally represented as
ordered pairs. The set of complex numbers can be thought of as a
two-dimensional space with two orthogonal axes, the "real" axis and the
"imaginary" axis. (See [Figure 2.20](#Figure 2.20).) From this point of
view, the complex number $z = x + iy$ (where $i^2 = -1$) can be thought
of as the point in the plane whose real coordinate is $x$ and whose
imaginary coordinate is $y$. Addition of complex numbers reduces in this
representation to addition of coordinates:

$$\begin{array}{r@{{}={}}l}
  \hbox{Real-part} (z_1 + z_2)\;    &
    \hbox{ Real-part} (z_1)\; + \hbox{ Real-part} (z_2), \\
  \hbox{Imaginary-part} (z_1 + z_2)\;   &
    \hbox{ Imaginary-part} (z_1)\; + \hbox{ Imaginary-part} (z_2).
\end{array}$$

[]{#Figure 2.20 label="Figure 2.20"}

![image](fig/chap2/Fig2.20.pdf){width="79mm"}

**Figure 2.20:** Complex numbers as points in the plane.

When multiplying complex numbers, it is more natural to think in terms
of representing a complex number in polar form, as a magnitude and an
angle ($r$ and $A$ in [Figure 2.20](#Figure 2.20)). The product of two
complex numbers is the vector obtained by stretching one complex number
by the length of the other and then rotating it through the angle of the
other:

$$\begin{array}{r@{{}={}}l}
    \hbox{Magnitude} (z_1 \cdot z_2)\;  &
        \hbox{ Magnitude} (z_1)\; \cdot \hbox{ Magnitude} (z_2), \\
    \hbox{Angle} (z_1 \cdot z_2)\;      &
        \hbox{ Angle} (z_1)\; + \hbox{ Angle} (z_2).
\end{array}$$

Thus, there are two different representations for complex numbers, which
are appropriate for different operations. Yet, from the viewpoint of
someone writing a program that uses complex numbers, the principle of
data abstraction suggests that all the operations for manipulating
complex numbers should be available regardless of which representation
is used by the computer. For example, it is often useful to be able to
find the magnitude of a complex number that is specified by rectangular
coordinates. Similarly, it is often useful to be able to determine the
real part of a complex number that is specified by polar coordinates.

To design such a system, we can follow the same data-abstraction
strategy we followed in designing the rational-number package in
[Section 2.1.1](#Section 2.1.1). Assume that the operations on complex
numbers are implemented in terms of four selectors: `real/part`,
`imag/part`, `magnitude` and `angle`. Also assume that we have two
procedures for constructing complex numbers: `make/from/real/imag`
returns a complex number with specified real and imaginary parts, and
`make/from/mag/ang` returns a complex number with specified magnitude
and angle. These procedures have the property that, for any complex
number `z`, both

::: scheme
(make-from-real-imag (real-part z) (imag-part z))
:::

and

::: scheme
(make-from-mag-ang (magnitude z) (angle z))
:::

produce complex numbers that are equal to `z`.

Using these constructors and selectors, we can implement arithmetic on
complex numbers using the "abstract data" specified by the constructors
and selectors, just as we did for rational numbers in [Section
2.1.1](#Section 2.1.1). As shown in the formulas above, we can add and
subtract complex numbers in terms of real and imaginary parts while
multiplying and dividing complex numbers in terms of magnitudes and
angles:

::: scheme
(define (add-complex z1 z2) (make-from-real-imag (+ (real-part z1)
(real-part z2)) (+ (imag-part z1) (imag-part z2)))) (define (sub-complex
z1 z2) (make-from-real-imag (- (real-part z1) (real-part z2)) (-
(imag-part z1) (imag-part z2)))) (define (mul-complex z1 z2)
(make-from-mag-ang (\* (magnitude z1) (magnitude z2)) (+ (angle z1)
(angle z2)))) (define (div-complex z1 z2) (make-from-mag-ang (/
(magnitude z1) (magnitude z2)) (- (angle z1) (angle z2))))
:::

To complete the complex-number package, we must choose a representation
and we must implement the constructors and selectors in terms of
primitive numbers and primitive list structure. There are two obvious
ways to do this: We can represent a complex number in "rectangular form"
as a pair (real part, imaginary part) or in "polar form" as a pair
(magnitude, angle). Which shall we choose?

In order to make the different choices concrete, imagine that there are
two programmers, Ben Bitdiddle and Alyssa P. Hacker, who are
independently designing representations for the complex-number system.
Ben chooses to represent complex numbers in rectangular form. With this
choice, selecting the real and imaginary parts of a complex number is
straightforward, as is constructing a complex number with given real and
imaginary parts. To find the magnitude and the angle, or to construct a
complex number with a given magnitude and angle, he uses the
trigonometric relations

$$\begin{array}{r@{{}={}}lr@{{}={}}l}
    x & r \cos A, \qquad    & r & \sqrt{x^2 + y^2}, \\
    y & r \sin A, \qquad    & A & \arctan(y, x),
\end{array}$$

which relate the real and imaginary parts $(x, y)$ to the magnitude and
the angle $(r, A)$.[^110] Ben's representation is therefore given by the
following selectors and constructors:

::: scheme
(define (real-part z) (car z)) (define (imag-part z) (cdr z)) (define
(magnitude z) (sqrt (+ (square (real-part z)) (square (imag-part z)))))
(define (angle z) (atan (imag-part z) (real-part z))) (define
(make-from-real-imag x y) (cons x y)) (define (make-from-mag-ang r a)
(cons (\* r (cos a)) (\* r (sin a))))
:::

Alyssa, in contrast, chooses to represent complex numbers in polar form.
For her, selecting the magnitude and angle is straightforward, but she
has to use the trigonometric relations to obtain the real and imaginary
parts. Alyssa's representation is:

::: scheme
(define (real-part z) (\* (magnitude z) (cos (angle z)))) (define
(imag-part z) (\* (magnitude z) (sin (angle z)))) (define (magnitude z)
(car z)) (define (angle z) (cdr z)) (define (make-from-real-imag x y)
(cons (sqrt (+ (square x) (square y))) (atan y x))) (define
(make-from-mag-ang r a) (cons r a))
:::

The discipline of data abstraction ensures that the same implementation
of `add/complex`, `sub/complex`, `mul/complex`, and `div/complex` will
work with either Ben's representation or Alyssa's representation.

### Tagged data {#Section 2.4.2}

One way to view data abstraction is as an application of the "principle
of least commitment." In implementing the complex-number system in
[Section 2.4.1](#Section 2.4.1), we can use either Ben's rectangular
representation or Alyssa's polar representation. The abstraction barrier
formed by the selectors and constructors permits us to defer to the last
possible moment the choice of a concrete representation for our data
objects and thus retain maximum flexibility in our system design.

The principle of least commitment can be carried to even further
extremes. If we desire, we can maintain the ambiguity of representation
even *after* we have designed the selectors and constructors, and elect
to use both Ben's representation *and* Alyssa's representation. If both
representations are included in a single system, however, we will need
some way to distinguish data in polar form from data in rectangular
form. Otherwise, if we were asked, for instance, to find the `magnitude`
of the pair (3, 4), we wouldn't know whether to answer 5 (interpreting
the number in rectangular form) or 3 (interpreting the number in polar
form). A straightforward way to accomplish this distinction is to
include a *type tag*---the symbol `rectangular` or `polar`---as part of
each complex number. Then when we need to manipulate a complex number we
can use the tag to decide which selector to apply.

In order to manipulate tagged data, we will assume that we have
procedures `type/tag` and `contents` that extract from a data object the
tag and the actual contents (the polar or rectangular coordinates, in
the case of a complex number). We will also postulate a procedure
`attach/tag` that takes a tag and contents and produces a tagged data
object. A straightforward way to implement this is to use ordinary list
structure:

::: scheme
(define (attach-tag type-tag contents) (cons type-tag contents)) (define
(type-tag datum) (if (pair? datum) (car datum) (error \"Bad tagged
datum: TYPE-TAG\" datum))) (define (contents datum) (if (pair? datum)
(cdr datum) (error \"Bad tagged datum: CONTENTS\" datum)))
:::

Using these procedures, we can define predicates `rectangular?` and
`polar?`, which recognize rectangular and polar numbers, respectively:

::: scheme
(define (rectangular? z) (eq? (type-tag z) 'rectangular)) (define
(polar? z) (eq? (type-tag z) 'polar))
:::

With type tags, Ben and Alyssa can now modify their code so that their
two different representations can coexist in the same system. Whenever
Ben constructs a complex number, he tags it as rectangular. Whenever
Alyssa constructs a complex number, she tags it as polar. In addition,
Ben and Alyssa must make sure that the names of their procedures do not
conflict. One way to do this is for Ben to append the suffix
`rectangular` to the name of each of his representation procedures and
for Alyssa to append `polar` to the names of hers. Here is Ben's revised
rectangular representation from [Section 2.4.1](#Section 2.4.1):

::: scheme
(define (real-part-rectangular z) (car z)) (define
(imag-part-rectangular z) (cdr z)) (define (magnitude-rectangular z)
(sqrt (+ (square (real-part-rectangular z)) (square
(imag-part-rectangular z))))) (define (angle-rectangular z) (atan
(imag-part-rectangular z) (real-part-rectangular z))) (define
(make-from-real-imag-rectangular x y) (attach-tag 'rectangular (cons x
y))) (define (make-from-mag-ang-rectangular r a) (attach-tag
'rectangular (cons (\* r (cos a)) (\* r (sin a)))))
:::

and here is Alyssa's revised polar representation:

::: scheme
(define (real-part-polar z) (\* (magnitude-polar z) (cos (angle-polar
z)))) (define (imag-part-polar z) (\* (magnitude-polar z) (sin
(angle-polar z)))) (define (magnitude-polar z) (car z)) (define
(angle-polar z) (cdr z)) (define (make-from-real-imag-polar x y)
(attach-tag 'polar (cons (sqrt (+ (square x) (square y))) (atan y x))))
(define (make-from-mag-ang-polar r a) (attach-tag 'polar (cons r a)))
:::

Each generic selector is implemented as a procedure that checks the tag
of its argument and calls the appropriate procedure for handling data of
that type. For example, to obtain the real part of a complex number,
`real/part` examines the tag to determine whether to use Ben's
`real/part/rectangular` or Alyssa's `real/part/polar`. In either case,
we use `contents` to extract the bare, untagged datum and send this to
the rectangular or polar procedure as required:

::: scheme
(define (real-part z) (cond ((rectangular? z) (real-part-rectangular
(contents z))) ((polar? z) (real-part-polar (contents z))) (else (error
\"Unknown type: REAL-PART\" z)))) (define (imag-part z) (cond
((rectangular? z) (imag-part-rectangular (contents z))) ((polar? z)
(imag-part-polar (contents z))) (else (error \"Unknown type: IMAG-PART\"
z)))) (define (magnitude z) (cond ((rectangular? z)
(magnitude-rectangular (contents z))) ((polar? z) (magnitude-polar
(contents z))) (else (error \"Unknown type: MAGNITUDE\" z)))) (define
(angle z) (cond ((rectangular? z) (angle-rectangular (contents z)))
((polar? z) (angle-polar (contents z))) (else (error \"Unknown type:
ANGLE\" z))))
:::

To implement the complex-number arithmetic operations, we can use the
same procedures `add/complex`, `sub/complex`, `mul/complex`, and
`div/complex` from [Section 2.4.1](#Section 2.4.1), because the
selectors they call are generic, and so will work with either
representation. For example, the procedure `add/complex` is still

::: scheme
(define (add-complex z1 z2) (make-from-real-imag (+ (real-part z1)
(real-part z2)) (+ (imag-part z1) (imag-part z2))))
:::

Finally, we must choose whether to construct complex numbers using Ben's
representation or Alyssa's representation. One reasonable choice is to
construct rectangular numbers whenever we have real and imaginary parts
and to construct polar numbers whenever we have magnitudes and angles:

::: scheme
(define (make-from-real-imag x y) (make-from-real-imag-rectangular x y))
(define (make-from-mag-ang r a) (make-from-mag-ang-polar r a))
:::

The resulting complex-number system has the structure shown in [Figure
2.21](#Figure 2.21). The system has been decomposed into three
relatively independent parts: the complex-number-arithmetic operations,
Alyssa's polar implementation, and Ben's rectangular implementation. The
polar and rectangular implementations could have been written by Ben and
Alyssa working separately, and both of these can be used as underlying
representations by a third programmer implementing the
complex-arithmetic procedures in terms of the abstract
constructor/selector interface.

[]{#Figure 2.21 label="Figure 2.21"}

![image](fig/chap2/Fig2.21a.pdf){width="108mm"}

**Figure 2.21:** Structure of the generic complex-arithmetic system.

Since each data object is tagged with its type, the selectors operate on
the data in a generic manner. That is, each selector is defined to have
a behavior that depends upon the particular type of data it is applied
to. Notice the general mechanism for interfacing the separate
representations: Within a given representation implementation (say,
Alyssa's polar package) a complex number is an untyped pair (magnitude,
angle). When a generic selector operates on a number of `polar` type, it
strips off the tag and passes the contents on to Alyssa's code.
Conversely, when Alyssa constructs a number for general use, she tags it
with a type so that it can be appropriately recognized by the
higher-level procedures. This discipline of stripping off and attaching
tags as data objects are passed from level to level can be an important
organizational strategy, as we shall see in [Section 2.5](#Section 2.5).

### Data-Directed Programming and Additivity {#Section 2.4.3}

The general strategy of checking the type of a datum and calling an
appropriate procedure is called *dispatching on type*. This is a
powerful strategy for obtaining modularity in system design. On the
other hand, implementing the dispatch as in [Section
2.4.2](#Section 2.4.2) has two significant weaknesses. One weakness is
that the generic interface procedures (`real/part`, `imag/part`,
`magnitude`, and `angle`) must know about all the different
representations. For instance, suppose we wanted to incorporate a new
representation for complex numbers into our complex-number system. We
would need to identify this new representation with a type, and then add
a clause to each of the generic interface procedures to check for the
new type and apply the appropriate selector for that representation.

Another weakness of the technique is that even though the individual
representations can be designed separately, we must guarantee that no
two procedures in the entire system have the same name. This is why Ben
and Alyssa had to change the names of their original procedures from
[Section 2.4.1](#Section 2.4.1).

The issue underlying both of these weaknesses is that the technique for
implementing generic interfaces is not *additive*. The person
implementing the generic selector procedures must modify those
procedures each time a new representation is installed, and the people
interfacing the individual representations must modify their code to
avoid name conflicts. In each of these cases, the changes that must be
made to the code are straightforward, but they must be made nonetheless,
and this is a source of inconvenience and error. This is not much of a
problem for the complex-number system as it stands, but suppose there
were not two but hundreds of different representations for complex
numbers. And suppose that there were many generic selectors to be
maintained in the abstract-data interface. Suppose, in fact, that no one
programmer knew all the interface procedures or all the representations.
The problem is real and must be addressed in such programs as
large-scale data-base-management systems.

What we need is a means for modularizing the system design even further.
This is provided by the programming technique known as *data-directed
programming*. To understand how data-directed programming works, begin
with the observation that whenever we deal with a set of generic
operations that are common to a set of different types we are, in
effect, dealing with a two-dimensional table that contains the possible
operations on one axis and the possible types on the other axis. The
entries in the table are the procedures that implement each operation
for each type of argument presented. In the complex-number system
developed in the previous section, the correspondence between operation
name, data type, and actual procedure was spread out among the various
conditional clauses in the generic interface procedures. But the same
information could have been organized in a table, as shown in [Figure
2.22](#Figure 2.22).

Data-directed programming is the technique of designing programs to work
with such a table directly. Previously, we implemented the mechanism
that interfaces the complex-arithmetic code with the two representation
packages as a set of procedures that each perform an explicit dispatch
on type. Here we will implement the interface as a single procedure that
looks up the combination of the operation name and argument type in the
table to find the correct procedure to apply, and then applies it to the
contents of the argument. If we do this, then to add a new
representation package to the system we need not change any existing
procedures; we need only add new entries to the table.

[]{#Figure 2.22 label="Figure 2.22"}

![image](fig/chap2/Fig2.22.pdf){width="102mm"}

**Figure 2.22:** Table of operations for the complex-number system.

To implement this plan, assume that we have two procedures, `put` and
`get`, for manipulating the operation-and-type table:

-   $\hbox{\tt(put}\;\langle$*op*$\kern0.1em\rangle\;\langle$*type*$\kern0.08em\rangle\;\langle$*item*$\kern0.08em\rangle\hbox{\tt)}$
    installs the $\langle$*item*$\kern0.08em\rangle$ in the table,
    indexed by the $\langle$*op*$\kern0.1em\rangle$ and the
    $\langle$*type*$\kern0.08em\rangle$.

-   $\hbox{\tt(get}\;\langle$*op*$\kern0.1em\rangle\;\langle$*type*$\kern0.08em\rangle\hbox{\tt)}$
    looks up the $\langle$*op*$\kern0.08em\rangle$,
    $\langle$*type*$\kern0.08em\rangle$ entry in the table and returns
    the item found there. If no item is found, `get` returns false.

For now, we can assume that `put` and `get` are included in our
language. In [Chapter 3](#Chapter 3) ([Section 3.3.3](#Section 3.3.3))
we will see how to implement these and other operations for manipulating
tables.

Here is how data-directed programming can be used in the complex-number
system. Ben, who developed the rectangular representation, implements
his code just as he did originally. He defines a collection of
procedures, or a *package*, and interfaces these to the rest of the
system by adding entries to the table that tell the system how to
operate on rectangular numbers. This is accomplished by calling the
following procedure:

::: scheme
(define (install-rectangular-package)  [;; internal procedures]{.roman}
(define (real-part z) (car z)) (define (imag-part z) (cdr z)) (define
(make-from-real-imag x y) (cons x y)) (define (magnitude z) (sqrt (+
(square (real-part z)) (square (imag-part z))))) (define (angle z) (atan
(imag-part z) (real-part z))) (define (make-from-mag-ang r a) (cons (\*
r (cos a)) (\* r (sin a))))

 [;; interface to the rest of the system]{.roman}  (define (tag x)
(attach-tag 'rectangular x)) (put 'real-part '(rectangular) real-part)
(put 'imag-part '(rectangular) imag-part) (put 'magnitude '(rectangular)
magnitude) (put 'angle '(rectangular) angle) (put 'make-from-real-imag
'rectangular (lambda (x y) (tag (make-from-real-imag x y)))) (put
'make-from-mag-ang 'rectangular (lambda (r a) (tag (make-from-mag-ang r
a)))) 'done)
:::

Notice that the internal procedures here are the same procedures from
[Section 2.4.1](#Section 2.4.1) that Ben wrote when he was working in
isolation. No changes are necessary in order to interface them to the
rest of the system. Moreover, since these procedure definitions are
internal to the installation procedure, Ben needn't worry about name
conflicts with other procedures outside the rectangular package. To
interface these to the rest of the system, Ben installs his `real/part`
procedure under the operation name `real/part` and the type
`(rectangular)`, and similarly for the other selectors.[^111] The
interface also defines the constructors to be used by the external
system.[^112] These are identical to Ben's internally defined
constructors, except that they attach the tag.

Alyssa's polar package is analogous:

::: scheme
(define (install-polar-package)  [;; internal procedures]{.roman}
(define (magnitude z) (car z)) (define (angle z) (cdr z)) (define
(make-from-mag-ang r a) (cons r a)) (define (real-part z) (\* (magnitude
z) (cos (angle z)))) (define (imag-part z) (\* (magnitude z) (sin (angle
z)))) (define (make-from-real-imag x y) (cons (sqrt (+ (square x)
(square y))) (atan y x)))  [;; interface to the rest of the
system]{.roman}  (define (tag x) (attach-tag 'polar x)) (put 'real-part
'(polar) real-part) (put 'imag-part '(polar) imag-part) (put 'magnitude
'(polar) magnitude) (put 'angle '(polar) angle) (put
'make-from-real-imag 'polar (lambda (x y) (tag (make-from-real-imag x
y)))) (put 'make-from-mag-ang 'polar (lambda (r a) (tag
(make-from-mag-ang r a)))) 'done)
:::

Even though Ben and Alyssa both still use their original procedures
defined with the same names as each other's (e.g., `real/part`), these
definitions are now internal to different procedures (see [Section
1.1.8](#Section 1.1.8)), so there is no name conflict.

The complex-arithmetic selectors access the table by means of a general
"operation" procedure called `apply/generic`, which applies a generic
operation to some arguments. `apply/generic` looks in the table under
the name of the operation and the types of the arguments and applies the
resulting procedure if one is present:[^113]

::: scheme
(define (apply-generic op . args) (let ((type-tags (map type-tag args)))
(let ((proc (get op type-tags))) (if proc (apply proc (map contents
args)) (error \"No method for these types: APPLY-GENERIC\" (list op
type-tags))))))
:::

Using `apply/generic`, we can define our generic selectors as follows:

::: scheme
(define (real-part z) (apply-generic 'real-part z)) (define (imag-part
z) (apply-generic 'imag-part z)) (define (magnitude z) (apply-generic
'magnitude z)) (define (angle z) (apply-generic 'angle z))
:::

Observe that these do not change at all if a new representation is added
to the system.

We can also extract from the table the constructors to be used by the
programs external to the packages in making complex numbers from real
and imaginary parts and from magnitudes and angles. As in [Section
2.4.2](#Section 2.4.2), we construct rectangular numbers whenever we
have real and imaginary parts, and polar numbers whenever we have
magnitudes and angles:

::: scheme
(define (make-from-real-imag x y) ((get 'make-from-real-imag
'rectangular) x y)) (define (make-from-mag-ang r a) ((get
'make-from-mag-ang 'polar) r a))
:::

> **[]{#Exercise 2.73 label="Exercise 2.73"}Exercise 2.73:** [Section
> 2.3.2](#Section 2.3.2) described a program that performs symbolic
> differentiation:
>
> ::: scheme
> (define (deriv exp var) (cond ((number? exp) 0) ((variable? exp) (if
> (same-variable? exp var) 1 0)) ((sum? exp) (make-sum (deriv (addend
> exp) var) (deriv (augend exp) var))) ((product? exp) (make-sum
> (make-product (multiplier exp) (deriv (multiplicand exp) var))
> (make-product (deriv (multiplier exp) var) (multiplicand exp))))
>  $\color{SchemeDark}\langle$  *more rules can be added
> here*  $\color{SchemeDark}\rangle$  (else (error \"unknown expression
> type: DERIV\" exp))))
> :::
>
> We can regard this program as performing a dispatch on the type of the
> expression to be differentiated. In this situation the "type tag" of
> the datum is the algebraic operator symbol (such as `+`) and the
> operation being performed is `deriv`. We can transform this program
> into data-directed style by rewriting the basic derivative procedure
> as
>
> ::: scheme
> (define (deriv exp var) (cond ((number? exp) 0) ((variable? exp) (if
> (same-variable? exp var) 1 0)) (else ((get 'deriv (operator exp))
> (operands exp) var)))) (define (operator exp) (car exp)) (define
> (operands exp) (cdr exp))
> :::
>
> a.  Explain what was done above. Why can't we assimilate the
>     predicates `number?` and `variable?` into the data-directed
>     dispatch?
>
> b.  Write the procedures for derivatives of sums and products, and the
>     auxiliary code required to install them in the table used by the
>     program above.
>
> c.  Choose any additional differentiation rule that you like, such as
>     the one for exponents ([Exercise 2.56](#Exercise 2.56)), and
>     install it in this data-directed system.
>
> d.  In this simple algebraic manipulator the type of an expression is
>     the algebraic operator that binds it together. Suppose, however,
>     we indexed the procedures in the opposite way, so that the
>     dispatch line in `deriv` looked like
>
>     ::: scheme
>     ((get (operator exp) 'deriv) (operands exp) var)
>     :::
>
>     What corresponding changes to the derivative system are required?

> **[]{#Exercise 2.74 label="Exercise 2.74"}Exercise 2.74:** Insatiable
> Enterprises, Inc., is a highly decentralized conglomerate company
> consisting of a large number of independent divisions located all over
> the world. The company's computer facilities have just been
> interconnected by means of a clever network-interfacing scheme that
> makes the entire network appear to any user to be a single computer.
> Insatiable's president, in her first attempt to exploit the ability of
> the network to extract administrative information from division files,
> is dismayed to discover that, although all the division files have
> been implemented as data structures in Scheme, the particular data
> structure used varies from division to division. A meeting of division
> managers is hastily called to search for a strategy to integrate the
> files that will satisfy headquarters' needs while preserving the
> existing autonomy of the divisions.
>
> Show how such a strategy can be implemented with data-directed
> programming. As an example, suppose that each division's personnel
> records consist of a single file, which contains a set of records
> keyed on employees' names. The structure of the set varies from
> division to division. Furthermore, each employee's record is itself a
> set (structured differently from division to division) that contains
> information keyed under identifiers such as `address` and `salary`. In
> particular:
>
> a.  Implement for headquarters a `get/record` procedure that retrieves
>     a specified employee's record from a specified personnel file. The
>     procedure should be applicable to any division's file. Explain how
>     the individual divisions' files should be structured. In
>     particular, what type information must be supplied?
>
> b.  Implement for headquarters a `get/salary` procedure that returns
>     the salary information from a given employee's record from any
>     division's personnel file. How should the record be structured in
>     order to make this operation work?
>
> c.  Implement for headquarters a `find/employee/record` procedure.
>     This should search all the divisions' files for the record of a
>     given employee and return the record. Assume that this procedure
>     takes as arguments an employee's name and a list of all the
>     divisions' files.
>
> d.  When Insatiable takes over a new company, what changes must be
>     made in order to incorporate the new personnel information into
>     the central system?

#### Message passing {#message-passing .unnumbered}

The key idea of data-directed programming is to handle generic
operations in programs by dealing explicitly with operation-and-type
tables, such as the table in [Figure 2.22](#Figure 2.22). The style of
programming we used in [Section 2.4.2](#Section 2.4.2) organized the
required dispatching on type by having each operation take care of its
own dispatching. In effect, this decomposes the operation-and-type table
into rows, with each generic operation procedure representing a row of
the table.

An alternative implementation strategy is to decompose the table into
columns and, instead of using "intelligent operations" that dispatch on
data types, to work with "intelligent data objects" that dispatch on
operation names. We can do this by arranging things so that a data
object, such as a rectangular number, is represented as a procedure that
takes as input the required operation name and performs the operation
indicated. In such a discipline, `make/from/real/imag` could be written
as

::: scheme
(define (make-from-real-imag x y) (define (dispatch op) (cond ((eq? op
'real-part) x) ((eq? op 'imag-part) y) ((eq? op 'magnitude) (sqrt (+
(square x) (square y)))) ((eq? op 'angle) (atan y x)) (else (error
\"Unknown op: MAKE-FROM-REAL-IMAG\" op)))) dispatch)
:::

The corresponding `apply/generic` procedure, which applies a generic
operation to an argument, now simply feeds the operation's name to the
data object and lets the object do the work:[^114]

::: scheme
(define (apply-generic op arg) (arg op))
:::

Note that the value returned by `make/from/real/imag` is a
procedure---the internal `dispatch` procedure. This is the procedure
that is invoked when `apply/generic` requests an operation to be
performed.

This style of programming is called *message passing*. The name comes
from the image that a data object is an entity that receives the
requested operation name as a "message." We have already seen an example
of message passing in [Section 2.1.3](#Section 2.1.3), where we saw how
`cons`, `car`, and `cdr` could be defined with no data objects but only
procedures. Here we see that message passing is not a mathematical trick
but a useful technique for organizing systems with generic operations.
In the remainder of this chapter we will continue to use data-directed
programming, rather than message passing, to discuss generic arithmetic
operations. In [Chapter 3](#Chapter 3) we will return to message
passing, and we will see that it can be a powerful tool for structuring
simulation programs.

> **[]{#Exercise 2.75 label="Exercise 2.75"}Exercise 2.75:** Implement
> the constructor `make/from/mag/ang` in message-passing style. This
> procedure should be analogous to the `make/from/real/imag` procedure
> given above.

> **[]{#Exercise 2.76 label="Exercise 2.76"}Exercise 2.76:** As a large
> system with generic operations evolves, new types of data objects or
> new operations may be needed. For each of the three
> strategies---generic operations with explicit dispatch, data-directed
> style, and message-passing-style---describe the changes that must be
> made to a system in order to add new types or new operations. Which
> organization would be most appropriate for a system in which new types
> must often be added? Which would be most appropriate for a system in
> which new operations must often be added?

## Systems with Generic Operations {#Section 2.5}

In the previous section, we saw how to design systems in which data
objects can be represented in more than one way. The key idea is to link
the code that specifies the data operations to the several
representations by means of generic interface procedures. Now we will
see how to use this same idea not only to define operations that are
generic over different representations but also to define operations
that are generic over different kinds of arguments. We have already seen
several different packages of arithmetic operations: the primitive
arithmetic (`+`, `-`, `*`, `/`) built into our language, the
rational-number arithmetic (`add/rat`, `sub/rat`, `mul/rat`, `div/rat`)
of [Section 2.1.1](#Section 2.1.1), and the complex-number arithmetic
that we implemented in [Section 2.4.3](#Section 2.4.3). We will now use
data-directed techniques to construct a package of arithmetic operations
that incorporates all the arithmetic packages we have already
constructed.

[]{#Figure 2.23 label="Figure 2.23"}

![image](fig/chap2/Fig2.23a.pdf){width="111mm"}

**Figure 2.23:** Generic arithmetic system.

[Figure 2.23](#Figure 2.23) shows the structure of the system we shall
build. Notice the abstraction barriers. From the perspective of someone
using "numbers," there is a single procedure `add` that operates on
whatever numbers are supplied. `add` is part of a generic interface that
allows the separate ordinary-arithmetic, rational-arithmetic, and
complex-arithmetic packages to be accessed uniformly by programs that
use numbers. Any individual arithmetic package (such as the complex
package) may itself be accessed through generic procedures (such as
`add/complex`) that combine packages designed for different
representations (such as rectangular and polar). Moreover, the structure
of the system is additive, so that one can design the individual
arithmetic packages separately and combine them to produce a generic
arithmetic system.

### Generic Arithmetic Operations {#Section 2.5.1}

The task of designing generic arithmetic operations is analogous to that
of designing the generic complex-number operations. We would like, for
instance, to have a generic addition procedure `add` that acts like
ordinary primitive addition `+` on ordinary numbers, like `add/rat` on
rational numbers, and like `add/complex` on complex numbers. We can
implement `add`, and the other generic arithmetic operations, by
following the same strategy we used in [Section 2.4.3](#Section 2.4.3)
to implement the generic selectors for complex numbers. We will attach a
type tag to each kind of number and cause the generic procedure to
dispatch to an appropriate package according to the data type of its
arguments.

The generic arithmetic procedures are defined as follows:

::: scheme
(define (add x y) (apply-generic 'add x y)) (define (sub x y)
(apply-generic 'sub x y)) (define (mul x y) (apply-generic 'mul x y))
(define (div x y) (apply-generic 'div x y))
:::

We begin by installing a package for handling *ordinary* numbers, that
is, the primitive numbers of our language. We will tag these with the
symbol `scheme/number`. The arithmetic operations in this package are
the primitive arithmetic procedures (so there is no need to define extra
procedures to handle the untagged numbers). Since these operations each
take two arguments, they are installed in the table keyed by the list
`(scheme/number scheme/number)`:

::: scheme
(define (install-scheme-number-package) (define (tag x) (attach-tag
'scheme-number x)) (put 'add '(scheme-number scheme-number) (lambda (x
y) (tag (+ x y)))) (put 'sub '(scheme-number scheme-number) (lambda (x
y) (tag (- x y)))) (put 'mul '(scheme-number scheme-number) (lambda (x
y) (tag (\* x y)))) (put 'div '(scheme-number scheme-number) (lambda (x
y) (tag (/ x y)))) (put 'make 'scheme-number (lambda (x) (tag x)))
'done)
:::

Users of the Scheme-number package will create (tagged) ordinary numbers
by means of the procedure:

::: scheme
(define (make-scheme-number n) ((get 'make 'scheme-number) n))
:::

Now that the framework of the generic arithmetic system is in place, we
can readily include new kinds of numbers. Here is a package that
performs rational arithmetic. Notice that, as a benefit of additivity,
we can use without modification the rational-number code from [Section
2.1.1](#Section 2.1.1) as the internal procedures in the package:

::: scheme
(define (install-rational-package)  [;; internal procedures]{.roman}
(define (numer x) (car x)) (define (denom x) (cdr x)) (define (make-rat
n d) (let ((g (gcd n d))) (cons (/ n g) (/ d g)))) (define (add-rat x y)
(make-rat (+ (\* (numer x) (denom y)) (\* (numer y) (denom x))) (\*
(denom x) (denom y)))) (define (sub-rat x y) (make-rat (- (\* (numer x)
(denom y)) (\* (numer y) (denom x))) (\* (denom x) (denom y)))) (define
(mul-rat x y) (make-rat (\* (numer x) (numer y)) (\* (denom x) (denom
y)))) (define (div-rat x y) (make-rat (\* (numer x) (denom y)) (\*
(denom x) (numer y))))  [;; interface to rest of the system]{.roman}
(define (tag x) (attach-tag 'rational x)) (put 'add '(rational rational)
(lambda (x y) (tag (add-rat x y)))) (put 'sub '(rational rational)
(lambda (x y) (tag (sub-rat x y)))) (put 'mul '(rational rational)
(lambda (x y) (tag (mul-rat x y)))) (put 'div '(rational rational)
(lambda (x y) (tag (div-rat x y)))) (put 'make 'rational (lambda (n d)
(tag (make-rat n d)))) 'done) (define (make-rational n d) ((get 'make
'rational) n d))
:::

We can install a similar package to handle complex numbers, using the
tag `complex`. In creating the package, we extract from the table the
operations `make/from/real/imag` and `make/from/mag/ang` that were
defined by the rectangular and polar packages. Additivity permits us to
use, as the internal operations, the same `add/complex`, `sub/complex`,
`mul/complex`, and `div/complex` procedures from [Section
2.4.1](#Section 2.4.1).

::: scheme
(define (install-complex-package)  [;; imported procedures from
rectangular and polar packages]{.roman}  (define (make-from-real-imag x
y) ((get 'make-from-real-imag 'rectangular) x y)) (define
(make-from-mag-ang r a) ((get 'make-from-mag-ang 'polar) r a))  [;;
internal procedures]{.roman}  (define (add-complex z1 z2)
(make-from-real-imag (+ (real-part z1) (real-part z2)) (+ (imag-part z1)
(imag-part z2)))) (define (sub-complex z1 z2) (make-from-real-imag (-
(real-part z1) (real-part z2)) (- (imag-part z1) (imag-part z2))))
(define (mul-complex z1 z2) (make-from-mag-ang (\* (magnitude z1)
(magnitude z2)) (+ (angle z1) (angle z2)))) (define (div-complex z1 z2)
(make-from-mag-ang (/ (magnitude z1) (magnitude z2)) (- (angle z1)
(angle z2))))  [;; interface to rest of the system]{.roman}  (define
(tag z) (attach-tag 'complex z)) (put 'add '(complex complex) (lambda
(z1 z2) (tag (add-complex z1 z2)))) (put 'sub '(complex complex) (lambda
(z1 z2) (tag (sub-complex z1 z2)))) (put 'mul '(complex complex) (lambda
(z1 z2) (tag (mul-complex z1 z2)))) (put 'div '(complex complex) (lambda
(z1 z2) (tag (div-complex z1 z2)))) (put 'make-from-real-imag 'complex
(lambda (x y) (tag (make-from-real-imag x y)))) (put 'make-from-mag-ang
'complex (lambda (r a) (tag (make-from-mag-ang r a)))) 'done)
:::

Programs outside the complex-number package can construct complex
numbers either from real and imaginary parts or from magnitudes and
angles. Notice how the underlying procedures, originally defined in the
rectangular and polar packages, are exported to the complex package, and
exported from there to the outside world.

::: scheme
(define (make-complex-from-real-imag x y) ((get 'make-from-real-imag
'complex) x y)) (define (make-complex-from-mag-ang r a) ((get
'make-from-mag-ang 'complex) r a))
:::

What we have here is a two-level tag system. A typical complex number,
such as $3 + 4i$ in rectangular form, would be represented as shown in
[Figure 2.24](#Figure 2.24). The outer tag (`complex`) is used to direct
the number to the complex package. Once within the complex package, the
next tag (`rectangular`) is used to direct the number to the rectangular
package. In a large and complicated system there might be many levels,
each interfaced with the next by means of generic operations. As a data
object is passed "downward," the outer tag that is used to direct it to
the appropriate package is stripped off (by applying `contents`) and the
next level of tag (if any) becomes visible to be used for further
dispatching.

[]{#Figure 2.24 label="Figure 2.24"}

![image](fig/chap2/Fig2.24c.pdf){width="64mm"}

> **Figure 2.24:** Representation of $3 + 4i$ in rectangular form.

In the above packages, we used `add/rat`, `add/complex`, and the other
arithmetic procedures exactly as originally written. Once these
definitions are internal to different installation procedures, however,
they no longer need names that are distinct from each other: we could
simply name them `add`, `sub`, `mul`, and `div` in both packages.

> **[]{#Exercise 2.77 label="Exercise 2.77"}Exercise 2.77:** Louis
> Reasoner tries to evaluate the expression `(magnitude z)` where `z` is
> the object shown in [Figure 2.24](#Figure 2.24). To his surprise,
> instead of the answer 5 he gets an error message from `apply/generic`,
> saying there is no method for the operation `magnitude` on the types
> `(complex)`. He shows this interaction to Alyssa P. Hacker, who says
> "The problem is that the complex-number selectors were never defined
> for `complex` numbers, just for `polar` and `rectangular` numbers. All
> you have to do to make this work is add the following to the `complex`
> package:"
>
> ::: scheme
> (put 'real-part '(complex) real-part) (put 'imag-part '(complex)
> imag-part) (put 'magnitude '(complex) magnitude) (put 'angle
> '(complex) angle)
> :::
>
> Describe in detail why this works. As an example, trace through all
> the procedures called in evaluating the expression `(magnitude z)`
> where `z` is the object shown in [Figure 2.24](#Figure 2.24). In
> particular, how many times is `apply/generic` invoked? What procedure
> is dispatched to in each case?

> **[]{#Exercise 2.78 label="Exercise 2.78"}Exercise 2.78:** The
> internal procedures in the `scheme/number` package are essentially
> nothing more than calls to the primitive procedures `+`, `-`, etc. It
> was not possible to use the primitives of the language directly
> because our type-tag system requires that each data object have a type
> attached to it. In fact, however, all Lisp implementations do have a
> type system, which they use internally. Primitive predicates such as
> `symbol?` and `number?` determine whether data objects have particular
> types. Modify the definitions of `type/tag`, `contents`, and
> `attach/tag` from [Section 2.4.2](#Section 2.4.2) so that our generic
> system takes advantage of Scheme's internal type system. That is to
> say, the system should work as before except that ordinary numbers
> should be represented simply as Scheme numbers rather than as pairs
> whose `car` is the symbol `scheme/number`.

> **[]{#Exercise 2.79 label="Exercise 2.79"}Exercise 2.79:** Define a
> generic equality predicate `equ?` that tests the equality of two
> numbers, and install it in the generic arithmetic package. This
> operation should work for ordinary numbers, rational numbers, and
> complex numbers.

> **[]{#Exercise 2.80 label="Exercise 2.80"}Exercise 2.80:** Define a
> generic predicate `=zero?` that tests if its argument is zero, and
> install it in the generic arithmetic package. This operation should
> work for ordinary numbers, rational numbers, and complex numbers.

### Combining Data of Different Types {#Section 2.5.2}

We have seen how to define a unified arithmetic system that encompasses
ordinary numbers, complex numbers, rational numbers, and any other type
of number we might decide to invent, but we have ignored an important
issue. The operations we have defined so far treat the different data
types as being completely independent. Thus, there are separate packages
for adding, say, two ordinary numbers, or two complex numbers. What we
have not yet considered is the fact that it is meaningful to define
operations that cross the type boundaries, such as the addition of a
complex number to an ordinary number. We have gone to great pains to
introduce barriers between parts of our programs so that they can be
developed and understood separately. We would like to introduce the
cross-type operations in some carefully controlled way, so that we can
support them without seriously violating our module boundaries.

One way to handle cross-type operations is to design a different
procedure for each possible combination of types for which the operation
is valid. For example, we could extend the complex-number package so
that it provides a procedure for adding complex numbers to ordinary
numbers and installs this in the table using the tag
`(complex scheme/number)`:[^115]

::: scheme
 [;; to be included in the complex package]{.roman}  (define
(add-complex-to-schemenum z x) (make-from-real-imag (+ (real-part z) x)
(imag-part z))) (put 'add '(complex scheme-number) (lambda (z x) (tag
(add-complex-to-schemenum z x))))
:::

This technique works, but it is cumbersome. With such a system, the cost
of introducing a new type is not just the construction of the package of
procedures for that type but also the construction and installation of
the procedures that implement the cross-type operations. This can easily
be much more code than is needed to define the operations on the type
itself. The method also undermines our ability to combine separate
packages additively, or at least to limit the extent to which the
implementors of the individual packages need to take account of other
packages. For instance, in the example above, it seems reasonable that
handling mixed operations on complex numbers and ordinary numbers should
be the responsibility of the complex-number package. Combining rational
numbers and complex numbers, however, might be done by the complex
package, by the rational package, or by some third package that uses
operations extracted from these two packages. Formulating coherent
policies on the division of responsibility among packages can be an
overwhelming task in designing systems with many packages and many
cross-type operations.

#### Coercion {#coercion .unnumbered}

In the general situation of completely unrelated operations acting on
completely unrelated types, implementing explicit cross-type operations,
cumbersome though it may be, is the best that one can hope for.
Fortunately, we can usually do better by taking advantage of additional
structure that may be latent in our type system. Often the different
data types are not completely independent, and there may be ways by
which objects of one type may be viewed as being of another type. This
process is called *coercion*. For example, if we are asked to
arithmetically combine an ordinary number with a complex number, we can
view the ordinary number as a complex number whose imaginary part is
zero. This transforms the problem to that of combining two complex
numbers, which can be handled in the ordinary way by the
complex-arithmetic package.

In general, we can implement this idea by designing coercion procedures
that transform an object of one type into an equivalent object of
another type. Here is a typical coercion procedure, which transforms a
given ordinary number to a complex number with that real part and zero
imaginary part:

::: scheme
(define (scheme-number-\>complex n) (make-complex-from-real-imag
(contents n) 0))
:::

We install these coercion procedures in a special coercion table,
indexed under the names of the two types:

::: scheme
(put-coercion 'scheme-number 'complex scheme-number-\>complex)
:::

(We assume that there are `put/coercion` and `get/coercion` procedures
available for manipulating this table.) Generally some of the slots in
the table will be empty, because it is not generally possible to coerce
an arbitrary data object of each type into all other types. For example,
there is no way to coerce an arbitrary complex number to an ordinary
number, so there will be no general `complex/>scheme/number` procedure
included in the table.

Once the coercion table has been set up, we can handle coercion in a
uniform manner by modifying the `apply/generic` procedure of [Section
2.4.3](#Section 2.4.3). When asked to apply an operation, we first check
whether the operation is defined for the arguments' types, just as
before. If so, we dispatch to the procedure found in the
operation-and-type table. Otherwise, we try coercion. For simplicity, we
consider only the case where there are two arguments.[^116] We check the
coercion table to see if objects of the first type can be coerced to the
second type. If so, we coerce the first argument and try the operation
again. If objects of the first type cannot in general be coerced to the
second type, we try the coercion the other way around to see if there is
a way to coerce the second argument to the type of the first argument.
Finally, if there is no known way to coerce either type to the other
type, we give up. Here is the procedure:

::: scheme
(define (apply-generic op . args) (let ((type-tags (map type-tag args)))
(let ((proc (get op type-tags))) (if proc (apply proc (map contents
args)) (if (= (length args) 2) (let ((type1 (car type-tags)) (type2
(cadr type-tags)) (a1 (car args)) (a2 (cadr args))) (let ((t1-\>t2
(get-coercion type1 type2)) (t2-\>t1 (get-coercion type2 type1))) (cond
(t1-\>t2 (apply-generic op (t1-\>t2 a1) a2)) (t2-\>t1 (apply-generic op
a1 (t2-\>t1 a2))) (else (error \"No method for these types\" (list op
type-tags)))))) (error \"No method for these types\" (list op
type-tags)))))))
:::

This coercion scheme has many advantages over the method of defining
explicit cross-type operations, as outlined above. Although we still
need to write coercion procedures to relate the types (possibly $n^2$
procedures for a system with $n$ types), we need to write only one
procedure for each pair of types rather than a different procedure for
each collection of types and each generic operation.[^117] What we are
counting on here is the fact that the appropriate transformation between
types depends only on the types themselves, not on the operation to be
applied.

On the other hand, there may be applications for which our coercion
scheme is not general enough. Even when neither of the objects to be
combined can be converted to the type of the other it may still be
possible to perform the operation by converting both objects to a third
type. In order to deal with such complexity and still preserve
modularity in our programs, it is usually necessary to build systems
that take advantage of still further structure in the relations among
types, as we discuss next.

#### Hierarchies of types {#hierarchies-of-types .unnumbered}

The coercion scheme presented above relied on the existence of natural
relations between pairs of types. Often there is more "global" structure
in how the different types relate to each other. For instance, suppose
we are building a generic arithmetic system to handle integers, rational
numbers, real numbers, and complex numbers. In such a system, it is
quite natural to regard an integer as a special kind of rational number,
which is in turn a special kind of real number, which is in turn a
special kind of complex number. What we actually have is a so-called
*hierarchy of types*, in which, for example, integers are a *subtype* of
rational numbers (i.e., any operation that can be applied to a rational
number can automatically be applied to an integer). Conversely, we say
that rational numbers form a *supertype* of integers. The particular
hierarchy we have here is of a very simple kind, in which each type has
at most one supertype and at most one subtype. Such a structure, called
a *tower*, is illustrated in [Figure 2.25](#Figure 2.25).

[]{#Figure 2.25 label="Figure 2.25"}

![image](fig/chap2/Fig2.25.pdf){width="11mm"}

**Figure 2.25:** A tower of types.

If we have a tower structure, then we can greatly simplify the problem
of adding a new type to the hierarchy, for we need only specify how the
new type is embedded in the next supertype above it and how it is the
supertype of the type below it. For example, if we want to add an
integer to a complex number, we need not explicitly define a special
coercion procedure `integer/>complex`. Instead, we define how an integer
can be transformed into a rational number, how a rational number is
transformed into a real number, and how a real number is transformed
into a complex number. We then allow the system to transform the integer
into a complex number through these steps and then add the two complex
numbers.

We can redesign our `apply/generic` procedure in the following way: For
each type, we need to supply a `raise` procedure, which "raises" objects
of that type one level in the tower. Then when the system is required to
operate on objects of different types it can successively raise the
lower types until all the objects are at the same level in the tower.
([Exercise 2.83](#Exercise 2.83) and [Exercise 2.84](#Exercise 2.84)
concern the details of implementing such a strategy.)

Another advantage of a tower is that we can easily implement the notion
that every type "inherits" all operations defined on a supertype. For
instance, if we do not supply a special procedure for finding the real
part of an integer, we should nevertheless expect that `real/part` will
be defined for integers by virtue of the fact that integers are a
subtype of complex numbers. In a tower, we can arrange for this to
happen in a uniform way by modifying `apply/generic`. If the required
operation is not directly defined for the type of the object given, we
raise the object to its supertype and try again. We thus crawl up the
tower, transforming our argument as we go, until we either find a level
at which the desired operation can be performed or hit the top (in which
case we give up).

Yet another advantage of a tower over a more general hierarchy is that
it gives us a simple way to "lower" a data object to the simplest
representation. For example, if we add $2 + 3i$ to $4 - 3i$, it would be
nice to obtain the answer as the integer 6 rather than as the complex
number $6 + 0i$. [Exercise 2.85](#Exercise 2.85) discusses a way to
implement such a lowering operation. (The trick is that we need a
general way to distinguish those objects that can be lowered, such as
$6 + 0i$, from those that cannot, such as $6 + 2i$.)

[]{#Figure 2.26 label="Figure 2.26"}

![image](fig/chap2/Fig2.26e.pdf){width="96mm"}

**Figure 2.26:** Relations among types of geometric figures.

#### Inadequacies of hierarchies {#inadequacies-of-hierarchies .unnumbered}

If the data types in our system can be naturally arranged in a tower,
this greatly simplifies the problems of dealing with generic operations
on different types, as we have seen. Unfortunately, this is usually not
the case. [Figure 2.26](#Figure 2.26) illustrates a more complex
arrangement of mixed types, this one showing relations among different
types of geometric figures. We see that, in general, a type may have
more than one subtype. Triangles and quadrilaterals, for instance, are
both subtypes of polygons. In addition, a type may have more than one
supertype. For example, an isosceles right triangle may be regarded
either as an isosceles triangle or as a right triangle. This
multiple-supertypes issue is particularly thorny, since it means that
there is no unique way to "raise" a type in the hierarchy. Finding the
"correct" supertype in which to apply an operation to an object may
involve considerable searching through the entire type network on the
part of a procedure such as `apply/generic`. Since there generally are
multiple subtypes for a type, there is a similar problem in coercing a
value "down" the type hierarchy. Dealing with large numbers of
interrelated types while still preserving modularity in the design of
large systems is very difficult, and is an area of much current
research.[^118]

> **[]{#Exercise 2.81 label="Exercise 2.81"}Exercise 2.81:** Louis
> Reasoner has noticed that `apply/generic` may try to coerce the
> arguments to each other's type even if they already have the same
> type. Therefore, he reasons, we need to put procedures in the coercion
> table to *coerce* arguments of each type to their own type. For
> example, in addition to the `scheme/number/>complex` coercion shown
> above, he would do:
>
> ::: scheme
> (define (scheme-number-\>scheme-number n) n) (define
> (complex-\>complex z) z) (put-coercion 'scheme-number 'scheme-number
> scheme-number-\>scheme-number) (put-coercion 'complex 'complex
> complex-\>complex)
> :::
>
> a.  With Louis's coercion procedures installed, what happens if
>     `apply/generic` is called with two arguments of type
>     `scheme/number` or two arguments of type `complex` for an
>     operation that is not found in the table for those types? For
>     example, assume that we've defined a generic exponentiation
>     operation:
>
>     ::: scheme
>     (define (exp x y) (apply-generic 'exp x y))
>     :::
>
>     and have put a procedure for exponentiation in the Scheme-number
>     package but not in any other package:
>
>     ::: scheme
>      [;; following added to Scheme-number package]{.roman}  (put 'exp
>     '(scheme-number scheme-number) (lambda (x y) (tag (expt x y))))
>      [; using primitive `expt`]{.roman}
>     :::
>
>     What happens if we call `exp` with two complex numbers as
>     arguments?
>
> b.  Is Louis correct that something had to be done about coercion with
>     arguments of the same type, or does `apply/generic` work correctly
>     as is?
>
> c.  Modify `apply/generic` so that it doesn't try coercion if the two
>     arguments have the same type.

> **[]{#Exercise 2.82 label="Exercise 2.82"}Exercise 2.82:** Show how to
> generalize `apply/generic` to handle coercion in the general case of
> multiple arguments. One strategy is to attempt to coerce all the
> arguments to the type of the first argument, then to the type of the
> second argument, and so on. Give an example of a situation where this
> strategy (and likewise the two-argument version given above) is not
> sufficiently general. (Hint: Consider the case where there are some
> suitable mixed-type operations present in the table that will not be
> tried.)

> **[]{#Exercise 2.83 label="Exercise 2.83"}Exercise 2.83:** Suppose you
> are designing a generic arithmetic system for dealing with the tower
> of types shown in [Figure 2.25](#Figure 2.25): integer, rational,
> real, complex. For each type (except complex), design a procedure that
> raises objects of that type one level in the tower. Show how to
> install a generic `raise` operation that will work for each type
> (except complex).

> **[]{#Exercise 2.84 label="Exercise 2.84"}Exercise 2.84:** Using the
> `raise` operation of [Exercise 2.83](#Exercise 2.83), modify the
> `apply/generic` procedure so that it coerces its arguments to have the
> same type by the method of successive raising, as discussed in this
> section. You will need to devise a way to test which of two types is
> higher in the tower. Do this in a manner that is "compatible" with the
> rest of the system and will not lead to problems in adding new levels
> to the tower.

> **[]{#Exercise 2.85 label="Exercise 2.85"}Exercise 2.85:** This
> section mentioned a method for "simplifying" a data object by lowering
> it in the tower of types as far as possible. Design a procedure `drop`
> that accomplishes this for the tower described in [Exercise
> 2.83](#Exercise 2.83). The key is to decide, in some general way,
> whether an object can be lowered. For example, the complex number
> $1.5 + 0i$ can be lowered as far as `real`, the complex number
> $1 + 0i$ can be lowered as far as `integer`, and the complex number
> $2 + 3i$ cannot be lowered at all. Here is a plan for determining
> whether an object can be lowered: Begin by defining a generic
> operation `project` that "pushes" an object down in the tower. For
> example, projecting a complex number would involve throwing away the
> imaginary part. Then a number can be dropped if, when we `project` it
> and `raise` the result back to the type we started with, we end up
> with something equal to what we started with. Show how to implement
> this idea in detail, by writing a `drop` procedure that drops an
> object as far as possible. You will need to design the various
> projection operations[^119] and install `project` as a generic
> operation in the system. You will also need to make use of a generic
> equality predicate, such as described in [Exercise
> 2.79](#Exercise 2.79). Finally, use `drop` to rewrite `apply/generic`
> from [Exercise 2.84](#Exercise 2.84) so that it "simplifies" its
> answers.

> **[]{#Exercise 2.86 label="Exercise 2.86"}Exercise 2.86:** Suppose we
> want to handle complex numbers whose real parts, imaginary parts,
> magnitudes, and angles can be either ordinary numbers, rational
> numbers, or other numbers we might wish to add to the system. Describe
> and implement the changes to the system needed to accommodate this.
> You will have to define operations such as `sine` and `cosine` that
> are generic over ordinary numbers and rational numbers.

### Example: Symbolic Algebra {#Section 2.5.3}

The manipulation of symbolic algebraic expressions is a complex process
that illustrates many of the hardest problems that occur in the design
of large-scale systems. An algebraic expression, in general, can be
viewed as a hierarchical structure, a tree of operators applied to
operands. We can construct algebraic expressions by starting with a set
of primitive objects, such as constants and variables, and combining
these by means of algebraic operators, such as addition and
multiplication. As in other languages, we form abstractions that enable
us to refer to compound objects in simple terms. Typical abstractions in
symbolic algebra are ideas such as linear combination, polynomial,
rational function, or trigonometric function. We can regard these as
compound "types," which are often useful for directing the processing of
expressions. For example, we could describe the expression

$$x^2 \sin (y^2 + 1) + x \cos 2y + \cos(y^3 - 2y^2)$$

as a polynomial in $x$ with coefficients that are trigonometric
functions of polynomials in $y$ whose coefficients are integers.

We will not attempt to develop a complete algebraic-manipulation system
here. Such systems are exceedingly complex programs, embodying deep
algebraic knowledge and elegant algorithms. What we will do is look at a
simple but important part of algebraic manipulation: the arithmetic of
polynomials. We will illustrate the kinds of decisions the designer of
such a system faces, and how to apply the ideas of abstract data and
generic operations to help organize this effort.

#### Arithmetic on polynomials {#arithmetic-on-polynomials .unnumbered}

Our first task in designing a system for performing arithmetic on
polynomials is to decide just what a polynomial is. Polynomials are
normally defined relative to certain variables (the *indeterminates* of
the polynomial). For simplicity, we will restrict ourselves to
polynomials having just one indeterminate (*univariate
polynomials*).[^120] We will define a polynomial to be a sum of terms,
each of which is either a coefficient, a power of the indeterminate, or
a product of a coefficient and a power of the indeterminate. A
coefficient is defined as an algebraic expression that is not dependent
upon the indeterminate of the polynomial. For example,

$$5x^2 + 3x + 7$$

is a simple polynomial in $x$, and

$$(y^2 + 1)x^3 + (2y)x + 1$$

is a polynomial in $x$ whose coefficients are polynomials in $y$.

Already we are skirting some thorny issues. Is the first of these
polynomials the same as the polynomial $5y^2 + 3y + 7$, or not? A
reasonable answer might be "yes, if we are considering a polynomial
purely as a mathematical function, but no, if we are considering a
polynomial to be a syntactic form." The second polynomial is
algebraically equivalent to a polynomial in $y$ whose coefficients are
polynomials in $x$. Should our system recognize this, or not?
Furthermore, there are other ways to represent a polynomial---for
example, as a product of factors, or (for a univariate polynomial) as
the set of roots, or as a listing of the values of the polynomial at a
specified set of points.[^121] We can finesse these questions by
deciding that in our algebraic-manipulation system a "polynomial" will
be a particular syntactic form, not its underlying mathematical meaning.

Now we must consider how to go about doing arithmetic on polynomials. In
this simple system, we will consider only addition and multiplication.
Moreover, we will insist that two polynomials to be combined must have
the same indeterminate.

We will approach the design of our system by following the familiar
discipline of data abstraction. We will represent polynomials using a
data structure called a *poly*, which consists of a variable and a
collection of terms. We assume that we have selectors `variable` and
`term/list` that extract those parts from a poly and a constructor
`make/poly` that assembles a poly from a given variable and a term list.
A variable will be just a symbol, so we can use the `same/variable?`
procedure of [Section 2.3.2](#Section 2.3.2) to compare variables. The
following procedures define addition and multiplication of polys:

::: scheme
(define (add-poly p1 p2) (if (same-variable? (variable p1) (variable
p2)) (make-poly (variable p1) (add-terms (term-list p1) (term-list p2)))
(error \"Polys not in same var: ADD-POLY\" (list p1 p2)))) (define
(mul-poly p1 p2) (if (same-variable? (variable p1) (variable p2))
(make-poly (variable p1) (mul-terms (term-list p1) (term-list p2)))
(error \"Polys not in same var: MUL-POLY\" (list p1 p2))))
:::

To incorporate polynomials into our generic arithmetic system, we need
to supply them with type tags. We'll use the tag `polynomial`, and
install appropriate operations on tagged polynomials in the operation
table. We'll embed all our code in an installation procedure for the
polynomial package, similar to the ones in [Section
2.5.1](#Section 2.5.1):

::: scheme
(define (install-polynomial-package)  [;; internal procedures]{.roman}
 [;; representation of poly]{.roman}  (define (make-poly variable
term-list) (cons variable term-list)) (define (variable p) (car p))
(define (term-list p) (cdr p))  $\color{SchemeDark}\langle$  *procedures
*same-variable?* and *variable?* from section
2.3.2*  $\color{SchemeDark}\rangle$   [;; representation of terms and
term lists]{.roman}   $\color{SchemeDark}\langle$  *procedures
*adjoin-term* $\dots$ *coeff* from text
below*  $\color{SchemeDark}\rangle$  (define (add-poly p1 p2)  $\dots$ )
 $\color{SchemeDark}\langle$  *procedures used by
*add-poly**  $\color{SchemeDark}\rangle$  (define (mul-poly p1 p2)
 $\dots$ )  $\color{SchemeDark}\langle$  *procedures used by
*mul-poly**  $\color{SchemeDark}\rangle$   [;; interface to rest of the
system]{.roman}  (define (tag p) (attach-tag 'polynomial p)) (put 'add
'(polynomial polynomial) (lambda (p1 p2) (tag (add-poly p1 p2)))) (put
'mul '(polynomial polynomial) (lambda (p1 p2) (tag (mul-poly p1 p2))))
(put 'make 'polynomial (lambda (var terms) (tag (make-poly var terms))))
'done)
:::

Polynomial addition is performed termwise. Terms of the same order
(i.e., with the same power of the indeterminate) must be combined. This
is done by forming a new term of the same order whose coefficient is the
sum of the coefficients of the addends. Terms in one addend for which
there are no terms of the same order in the other addend are simply
accumulated into the sum polynomial being constructed.

In order to manipulate term lists, we will assume that we have a
constructor `the/empty/termlist` that returns an empty term list and a
constructor `adjoin/term` that adjoins a new term to a term list. We
will also assume that we have a predicate `empty/termlist?` that tells
if a given term list is empty, a selector `first/term` that extracts the
highest-order term from a term list, and a selector `rest/terms` that
returns all but the highest-order term. To manipulate terms, we will
suppose that we have a constructor `make/term` that constructs a term
with given order and coefficient, and selectors `order` and `coeff` that
return, respectively, the order and the coefficient of the term. These
operations allow us to consider both terms and term lists as data
abstractions, whose concrete representations we can worry about
separately.

Here is the procedure that constructs the term list for the sum of two
polynomials:[^122]

::: scheme
(define (add-terms L1 L2) (cond ((empty-termlist? L1) L2)
((empty-termlist? L2) L1) (else (let ((t1 (first-term L1)) (t2
(first-term L2))) (cond ((\> (order t1) (order t2)) (adjoin-term t1
(add-terms (rest-terms L1) L2))) ((\< (order t1) (order t2))
(adjoin-term t2 (add-terms L1 (rest-terms L2)))) (else (adjoin-term
(make-term (order t1) (add (coeff t1) (coeff t2))) (add-terms
(rest-terms L1) (rest-terms L2)))))))))
:::

The most important point to note here is that we used the generic
addition procedure `add` to add together the coefficients of the terms
being combined. This has powerful consequences, as we will see below.

In order to multiply two term lists, we multiply each term of the first
list by all the terms of the other list, repeatedly using
`mul/term/by/all/terms`, which multiplies a given term by all terms in a
given term list. The resulting term lists (one for each term of the
first list) are accumulated into a sum. Multiplying two terms forms a
term whose order is the sum of the orders of the factors and whose
coefficient is the product of the coefficients of the factors:

::: scheme
(define (mul-terms L1 L2) (if (empty-termlist? L1) (the-empty-termlist)
(add-terms (mul-term-by-all-terms (first-term L1) L2) (mul-terms
(rest-terms L1) L2)))) (define (mul-term-by-all-terms t1 L) (if
(empty-termlist? L) (the-empty-termlist) (let ((t2 (first-term L)))
(adjoin-term (make-term (+ (order t1) (order t2)) (mul (coeff t1) (coeff
t2))) (mul-term-by-all-terms t1 (rest-terms L))))))
:::

This is really all there is to polynomial addition and multiplication.
Notice that, since we operate on terms using the generic procedures
`add` and `mul`, our polynomial package is automatically able to handle
any type of coefficient that is known about by the generic arithmetic
package. If we include a coercion mechanism such as one of those
discussed in [Section 2.5.2](#Section 2.5.2), then we also are
automatically able to handle operations on polynomials of different
coefficient types, such as

$$[3x^2 + (2 + 3i)x + 7] \cdot \! \left[ x^4 + {2\over3} x^2 + (5 + 3i) \right]\!.$$

Because we installed the polynomial addition and multiplication
procedures `add/poly` and `mul/poly` in the generic arithmetic system as
the `add` and `mul` operations for type `polynomial`, our system is also
automatically able to handle polynomial operations such as

$$\Big[(y + 1)x^2 + (y^2 + 1)x + (y - 1)\Big] \cdot \Big[(y - 2)x + (y^3 + 7)\Big]\!.$$

The reason is that when the system tries to combine coefficients, it
will dispatch through `add` and `mul`. Since the coefficients are
themselves polynomials (in $y$), these will be combined using `add/poly`
and `mul/poly`. The result is a kind of "data-directed recursion" in
which, for example, a call to `mul/poly` will result in recursive calls
to `mul/poly` in order to multiply the coefficients. If the coefficients
of the coefficients were themselves polynomials (as might be used to
represent polynomials in three variables), the data direction would
ensure that the system would follow through another level of recursive
calls, and so on through as many levels as the structure of the data
dictates.[^123]

#### Representing term lists {#representing-term-lists .unnumbered}

Finally, we must confront the job of implementing a good representation
for term lists. A term list is, in effect, a set of coefficients keyed
by the order of the term. Hence, any of the methods for representing
sets, as discussed in [Section 2.3.3](#Section 2.3.3), can be applied to
this task. On the other hand, our procedures `add/terms` and `mul/terms`
always access term lists sequentially from highest to lowest order.
Thus, we will use some kind of ordered list representation.

How should we structure the list that represents a term list? One
consideration is the "density" of the polynomials we intend to
manipulate. A polynomial is said to be *dense* if it has nonzero
coefficients in terms of most orders. If it has many zero terms it is
said to be *sparse*. For example,

$$A: \quad x^5 + 2x^4 + 3x^2 - 2x - 5$$

is a dense polynomial, whereas

$$B: \quad x^{100} + 2x^2 + 1$$

is sparse.

The term lists of dense polynomials are most efficiently represented as
lists of the coefficients. For example, $A$ above would be nicely
represented as `(1 2 0 3 -2 -5)`. The order of a term in this
representation is the length of the sublist beginning with that term's
coefficient, decremented by 1.[^124] This would be a terrible
representation for a sparse polynomial such as $B$: There would be a
giant list of zeros punctuated by a few lonely nonzero terms. A more
reasonable representation of the term list of a sparse polynomial is as
a list of the nonzero terms, where each term is a list containing the
order of the term and the coefficient for that order. In such a scheme,
polynomial $B$ is efficiently represented as `((100 1) (2 2) (0 1))`. As
most polynomial manipulations are performed on sparse polynomials, we
will use this method. We will assume that term lists are represented as
lists of terms, arranged from highest-order to lowest-order term. Once
we have made this decision, implementing the selectors and constructors
for terms and term lists is straightforward:[^125]

::: scheme
(define (adjoin-term term term-list) (if (=zero? (coeff term)) term-list
(cons term term-list))) (define (the-empty-termlist) '()) (define
(first-term term-list) (car term-list)) (define (rest-terms term-list)
(cdr term-list)) (define (empty-termlist? term-list) (null? term-list))
(define (make-term order coeff) (list order coeff)) (define (order term)
(car term)) (define (coeff term) (cadr term))
:::

where `=zero?` is as defined in [Exercise 2.80](#Exercise 2.80). (See
also [Exercise 2.87](#Exercise 2.87) below.)

Users of the polynomial package will create (tagged) polynomials by
means of the procedure:

::: scheme
(define (make-polynomial var terms) ((get 'make 'polynomial) var terms))
:::

> **[]{#Exercise 2.87 label="Exercise 2.87"}Exercise 2.87:** Install
> `=zero?` for polynomials in the generic arithmetic package. This will
> allow `adjoin/term` to work for polynomials with coefficients that are
> themselves polynomials.

> **[]{#Exercise 2.88 label="Exercise 2.88"}Exercise 2.88:** Extend the
> polynomial system to include subtraction of polynomials. (Hint: You
> may find it helpful to define a generic negation operation.)

> **[]{#Exercise 2.89 label="Exercise 2.89"}Exercise 2.89:** Define
> procedures that implement the term-list representation described above
> as appropriate for dense polynomials.

> **[]{#Exercise 2.90 label="Exercise 2.90"}Exercise 2.90:** Suppose we
> want to have a polynomial system that is efficient for both sparse and
> dense polynomials. One way to do this is to allow both kinds of
> term-list representations in our system. The situation is analogous to
> the complex-number example of [Section 2.4](#Section 2.4), where we
> allowed both rectangular and polar representations. To do this we must
> distinguish different types of term lists and make the operations on
> term lists generic. Redesign the polynomial system to implement this
> generalization. This is a major effort, not a local change.

> **[]{#Exercise 2.91 label="Exercise 2.91"}Exercise 2.91:** A
> univariate polynomial can be divided by another one to produce a
> polynomial quotient and a polynomial remainder. For example,
>
> $${x^5 - 1 \over x^2 - 1} = x^3 + x, \hbox{  remainder  } x - 1.$$
>
> Division can be performed via long division. That is, divide the
> highest-order term of the dividend by the highest-order term of the
> divisor. The result is the first term of the quotient. Next, multiply
> the result by the divisor, subtract that from the dividend, and
> produce the rest of the answer by recursively dividing the difference
> by the divisor. Stop when the order of the divisor exceeds the order
> of the dividend and declare the dividend to be the remainder. Also, if
> the dividend ever becomes zero, return zero as both quotient and
> remainder.
>
> We can design a `div/poly` procedure on the model of `add/poly` and
> `mul/poly`. The procedure checks to see if the two polys have the same
> variable. If so, `div/poly` strips off the variable and passes the
> problem to `div/terms`, which performs the division operation on term
> lists. `div/poly` finally reattaches the variable to the result
> supplied by `div/terms`. It is convenient to design `div/terms` to
> compute both the quotient and the remainder of a division. `div/terms`
> can take two term lists as arguments and return a list of the quotient
> term list and the remainder term list.
>
> Complete the following definition of `div/terms` by filling in the
> missing expressions. Use this to implement `div/poly`, which takes two
> polys as arguments and returns a list of the quotient and remainder
> polys.
>
> ::: smallscheme
> (define (div-terms L1 L2) (if (empty-termlist? L1) (list
> (the-empty-termlist) (the-empty-termlist)) (let ((t1 (first-term L1))
> (t2 (first-term L2))) (if (\> (order t2) (order t1)) (list
> (the-empty-termlist) L1) (let ((new-c (div (coeff t1) (coeff t2)))
> (new-o (- (order t1) (order t2)))) (let ((rest-of-result
>  $\langle$  *compute rest of result recursively*  $\rangle$  ))
>  $\langle$  *form complete result*  $\rangle$  ))))))
> :::

#### Hierarchies of types in symbolic algebra {#hierarchies-of-types-in-symbolic-algebra .unnumbered}

Our polynomial system illustrates how objects of one type (polynomials)
may in fact be complex objects that have objects of many different types
as parts. This poses no real difficulty in defining generic operations.
We need only install appropriate generic operations for performing the
necessary manipulations of the parts of the compound types. In fact, we
saw that polynomials form a kind of "recursive data abstraction," in
that parts of a polynomial may themselves be polynomials. Our generic
operations and our data-directed programming style can handle this
complication without much trouble.

On the other hand, polynomial algebra is a system for which the data
types cannot be naturally arranged in a tower. For instance, it is
possible to have polynomials in $x$ whose coefficients are polynomials
in $y$. It is also possible to have polynomials in $y$ whose
coefficients are polynomials in $x$. Neither of these types is "above"
the other in any natural way, yet it is often necessary to add together
elements from each set. There are several ways to do this. One
possibility is to convert one polynomial to the type of the other by
expanding and rearranging terms so that both polynomials have the same
principal variable. One can impose a towerlike structure on this by
ordering the variables and thus always converting any polynomial to a
"canonical form" with the highest-priority variable dominant and the
lower-priority variables buried in the coefficients. This strategy works
fairly well, except that the conversion may expand a polynomial
unnecessarily, making it hard to read and perhaps less efficient to work
with. The tower strategy is certainly not natural for this domain or for
any domain where the user can invent new types dynamically using old
types in various combining forms, such as trigonometric functions, power
series, and integrals.

It should not be surprising that controlling coercion is a serious
problem in the design of large-scale algebraic-manipulation systems.
Much of the complexity of such systems is concerned with relationships
among diverse types. Indeed, it is fair to say that we do not yet
completely understand coercion. In fact, we do not yet completely
understand the concept of a data type. Nevertheless, what we know
provides us with powerful structuring and modularity principles to
support the design of large systems.

> **[]{#Exercise 2.92 label="Exercise 2.92"}Exercise 2.92:** By imposing
> an ordering on variables, extend the polynomial package so that
> addition and multiplication of polynomials works for polynomials in
> different variables. (This is not easy!)

#### Extended exercise: Rational functions {#extended-exercise-rational-functions .unnumbered}

We can extend our generic arithmetic system to include *rational
functions*. These are "fractions" whose numerator and denominator are
polynomials, such as

$${x + 1 \over x^3 - 1}\,.$$

The system should be able to add, subtract, multiply, and divide
rational functions, and to perform such computations as

$${x + 1 \over x^3 - 1} + {x \over x^2 - 1} =
    {x^3 + 2x^2 + 3x + 1 \over x^4 + x^3 - x - 1}\,.$$

(Here the sum has been simplified by removing common factors. Ordinary
"cross multiplication" would have produced a fourth-degree polynomial
over a fifth-degree polynomial.)

If we modify our rational-arithmetic package so that it uses generic
operations, then it will do what we want, except for the problem of
reducing fractions to lowest terms.

> **[]{#Exercise 2.93 label="Exercise 2.93"}Exercise 2.93:** Modify the
> rational-arithmetic package to use generic operations, but change
> `make/rat` so that it does not attempt to reduce fractions to lowest
> terms. Test your system by calling `make/rational` on two polynomials
> to produce a rational function:
>
> ::: scheme
> (define p1 (make-polynomial 'x '((2 1) (0 1)))) (define p2
> (make-polynomial 'x '((3 1) (0 1)))) (define rf (make-rational p2 p1))
> :::
>
> Now add `rf` to itself, using `add`. You will observe that this
> addition procedure does not reduce fractions to lowest terms.

We can reduce polynomial fractions to lowest terms using the same idea
we used with integers: modifying `make/rat` to divide both the numerator
and the denominator by their greatest common divisor. The notion of
"greatest common divisor" makes sense for polynomials. In fact, we can
compute the gcd of two polynomials using essentially the
same Euclid's Algorithm that works for integers.[^126] The integer
version is

::: scheme
(define (gcd a b) (if (= b 0) a (gcd b (remainder a b))))
:::

Using this, we could make the obvious modification to define a
gcd operation that works on term lists:

::: scheme
(define (gcd-terms a b) (if (empty-termlist? b) a (gcd-terms b
(remainder-terms a b))))
:::

where `remainder/terms` picks out the remainder component of the list
returned by the term-list division operation `div/terms` that was
implemented in [Exercise 2.91](#Exercise 2.91).

> **[]{#Exercise 2.94 label="Exercise 2.94"}Exercise 2.94:** Using
> `div/terms`, implement the procedure `remainder/terms` and use this to
> define `gcd/terms` as above. Now write a procedure `gcd/poly` that
> computes the polynomial gcd of two polys. (The procedure
> should signal an error if the two polys are not in the same variable.)
> Install in the system a generic operation `greatest/common/divisor`
> that reduces to `gcd/poly` for polynomials and to ordinary `gcd` for
> ordinary numbers. As a test, try
>
> ::: scheme
> (define p1 (make-polynomial 'x '((4 1) (3 -1) (2 -2) (1 2)))) (define
> p2 (make-polynomial 'x '((3 1) (1 -1)))) (greatest-common-divisor p1
> p2)
> :::
>
> and check your result by hand.

> **[]{#Exercise 2.95 label="Exercise 2.95"}Exercise 2.95:** Define
> $P_1$, $P_2$, and $P_3$ to be the polynomials
>
> $$\begin{array}{l@{{}:}l}
>     P_1     & \quad x^2 - 2x + 1, \\
>     P_2     & \quad 11x^2 + 7, \\
>     P_3     & \quad 13x + 5.
> \end{array}$$
>
> Now define $Q_1$ to be the product of $P_1$ and $P_2$ and $Q_2$ to be
> the product of $P_1$ and $P_3$, and use `greatest/common/divisor`
> ([Exercise 2.94](#Exercise 2.94)) to compute the gcd of
> $Q_1$ and $Q_2$. Note that the answer is not the same as $P_1$. This
> example introduces noninteger operations into the computation, causing
> difficulties with the gcd algorithm.[^127] To understand
> what is happening, try tracing `gcd/terms` while computing the
> gcd or try performing the division by hand.

We can solve the problem exhibited in [Exercise 2.95](#Exercise 2.95) if
we use the following modification of the gcd algorithm
(which really works only in the case of polynomials with integer
coefficients). Before performing any polynomial division in the
gcd computation, we multiply the dividend by an integer
constant factor, chosen to guarantee that no fractions will arise during
the division process. Our answer will thus differ from the actual
gcd by an integer constant factor, but this does not
matter in the case of reducing rational functions to lowest terms; the
gcd will be used to divide both the numerator and
denominator, so the integer constant factor will cancel out.

More precisely, if $P$ and $Q$ are polynomials, let $O_1$ be the order
of $P$ (i.e., the order of the largest term of $P$) and let $O_2$ be the
order of $Q$. Let $c$ be the leading coefficient of $Q$. Then it can be
shown that, if we multiply $P$ by the *integerizing factor*
$c^{1 + O_1 - O_2}$, the resulting polynomial can be divided by $Q$ by
using the `div/terms` algorithm without introducing any fractions. The
operation of multiplying the dividend by this constant and then dividing
is sometimes called the *pseudodivision* of $P$ by $Q$. The remainder of
the division is called the *pseudoremainder*.

> **[]{#Exercise 2.96 label="Exercise 2.96"}Exercise 2.96:**
>
> a.  Implement the procedure `pseudoremainder/terms`, which is just
>     like `remainder/terms` except that it multiplies the dividend by
>     the integerizing factor described above before calling
>     `div/terms`. Modify `gcd/terms` to use `pseudoremainder/terms`,
>     and verify that `greatest/common/divisor` now produces an answer
>     with integer coefficients on the example in [Exercise
>     2.95](#Exercise 2.95).
>
> b.  The gcd now has integer coefficients, but they are
>     larger than those of $P_1$. Modify `gcd/terms` so that it removes
>     common factors from the coefficients of the answer by dividing all
>     the coefficients by their (integer) greatest common divisor.

Thus, here is how to reduce a rational function to lowest terms:

-   Compute the gcd of the numerator and denominator,
    using the version of `gcd/terms` from [Exercise
    2.96](#Exercise 2.96).

-   When you obtain the gcd, multiply both numerator and
    denominator by the same integerizing factor before dividing through
    by the gcd, so that division by the gcd
    will not introduce any noninteger coefficients. As the factor you
    can use the leading coefficient of the gcd raised to
    the power $1 + O_1 - O_2$, where $O_2$ is the order of the
    gcd and $O_1$ is the maximum of the orders of the
    numerator and denominator. This will ensure that dividing the
    numerator and denominator by the gcd will not
    introduce any fractions.

-   The result of this operation will be a numerator and denominator
    with integer coefficients. The coefficients will normally be very
    large because of all of the integerizing factors, so the last step
    is to remove the redundant factors by computing the (integer)
    greatest common divisor of all the coefficients of the numerator and
    the denominator and dividing through by this factor.

> **[]{#Exercise 2.97 label="Exercise 2.97"}Exercise 2.97:**
>
> a.  Implement this algorithm as a procedure `reduce/terms` that takes
>     two term lists `n` and `d` as arguments and returns a list `nn`,
>     `dd`, which are `n` and `d` reduced to lowest terms via the
>     algorithm given above. Also write a procedure `reduce/poly`,
>     analogous to `add/poly`, that checks to see if the two polys have
>     the same variable. If so, `reduce/poly` strips off the variable
>     and passes the problem to `reduce/terms`, then reattaches the
>     variable to the two term lists supplied by `reduce/terms`.
>
> b.  Define a procedure analogous to `reduce/terms` that does what the
>     original `make/rat` did for integers:
>
>     ::: scheme
>     (define (reduce-integers n d) (let ((g (gcd n d))) (list (/ n g)
>     (/ d g))))
>     :::
>
>     and define `reduce` as a generic operation that calls
>     `apply/generic` to dispatch to either `reduce/poly` (for
>     `polynomial` arguments) or `reduce/integers` (for `scheme/number`
>     arguments). You can now easily make the rational-arithmetic
>     package reduce fractions to lowest terms by having `make/rat` call
>     `reduce` before combining the given numerator and denominator to
>     form a rational number. The system now handles rational
>     expressions in either integers or polynomials. To test your
>     program, try the example at the beginning of this extended
>     exercise:
>
>     ::: scheme
>     (define p1 (make-polynomial 'x '((1 1) (0 1)))) (define p2
>     (make-polynomial 'x '((3 1) (0 -1)))) (define p3 (make-polynomial
>     'x '((1 1)))) (define p4 (make-polynomial 'x '((2 1) (0 -1))))
>     (define rf1 (make-rational p1 p2)) (define rf2 (make-rational p3
>     p4)) (add rf1 rf2)
>     :::
>
>     See if you get the correct answer, correctly reduced to lowest
>     terms.

The gcd computation is at the heart of any system that
does operations on rational functions. The algorithm used above,
although mathematically straightforward, is extremely slow. The slowness
is due partly to the large number of division operations and partly to
the enormous size of the intermediate coefficients generated by the
pseudodivisions. One of the active areas in the development of
algebraic-manipulation systems is the design of better algorithms for
computing polynomial gcds.[^128]

# Modularity, Objects, and State {#Chapter 3}

> Mεταβάλλον ὰναπαύεται\
> (Even while it changes, it stands still.)\
> ---Heraclitus

> Plus ça change, plus c'est la même chose.\
> ---Alphonse Karr

The preceding chapters introduced the basic
elements from which programs are made. We saw how primitive procedures
and primitive data are combined to construct compound entities, and we
learned that abstraction is vital in helping us to cope with the
complexity of large systems. But these tools are not sufficient for
designing programs. Effective program synthesis also requires
organizational principles that can guide us in formulating the overall
design of a program. In particular, we need strategies to help us
structure large systems so that they will be *modular*, that is, so that
they can be divided "naturally" into coherent parts that can be
separately developed and maintained.

One powerful design strategy, which is particularly appropriate to the
construction of programs for modeling physical systems, is to base the
structure of our programs on the structure of the system being modeled.
For each object in the system, we construct a corresponding
computational object. For each system action, we define a symbolic
operation in our computational model. Our hope in using this strategy is
that extending the model to accommodate new objects or new actions will
require no strategic changes to the program, only the addition of the
new symbolic analogs of those objects or actions. If we have been
successful in our system organization, then to add a new feature or
debug an old one we will have to work on only a localized part of the
system.

To a large extent, then, the way we organize a large program is dictated
by our perception of the system to be modeled. In this chapter we will
investigate two prominent organizational strategies arising from two
rather different "world views" of the structure of systems. The first
organizational strategy concentrates on *objects*, viewing a large
system as a collection of distinct objects whose behaviors may change
over time. An alternative organizational strategy concentrates on the
*streams* of information that flow in the system, much as an electrical
engineer views a signal-processing system.

Both the object-based approach and the stream-processing approach raise
significant linguistic issues in programming. With objects, we must be
concerned with how a computational object can change and yet maintain
its identity. This will force us to abandon our old substitution model
of computation ([Section 1.1.5](#Section 1.1.5)) in favor of a more
mechanistic but less theoretically tractable *environment model* of
computation. The difficulties of dealing with objects, change, and
identity are a fundamental consequence of the need to grapple with time
in our computational models. These difficulties become even greater when
we allow the possibility of concurrent execution of programs. The stream
approach can be most fully exploited when we decouple simulated time in
our model from the order of the events that take place in the computer
during evaluation. We will accomplish this using a technique known as
*delayed evaluation*.

## Assignment and Local State {#Section 3.1}

We ordinarily view the world as populated by independent objects, each
of which has a state that changes over time. An object is said to "have
state" if its behavior is influenced by its history. A bank account, for
example, has state in that the answer to the question "Can I withdraw
\$100?" depends upon the history of deposit and withdrawal transactions.
We can characterize an object's state by one or more *state variables*,
which among them maintain enough information about history to determine
the object's current behavior. In a simple banking system, we could
characterize the state of an account by a current balance rather than by
remembering the entire history of account transactions.

In a system composed of many objects, the objects are rarely completely
independent. Each may influence the states of others through
interactions, which serve to couple the state variables of one object to
those of other objects. Indeed, the view that a system is composed of
separate objects is most useful when the state variables of the system
can be grouped into closely coupled subsystems that are only loosely
coupled to other subsystems.

This view of a system can be a powerful framework for organizing
computational models of the system. For such a model to be modular, it
should be decomposed into computational objects that model the actual
objects in the system. Each computational object must have its own
*local state variables* describing the actual object's state. Since the
states of objects in the system being modeled change over time, the
state variables of the corresponding computational objects must also
change. If we choose to model the flow of time in the system by the
elapsed time in the computer, then we must have a way to construct
computational objects whose behaviors change as our programs run. In
particular, if we wish to model state variables by ordinary symbolic
names in the programming language, then the language must provide an
*assignment operator* to enable us to change the value associated with a
name.

### Local State Variables {#Section 3.1.1}

To illustrate what we mean by having a computational object with
time-varying state, let us model the situation of withdrawing money from
a bank account. We will do this using a procedure `withdraw`, which
takes as argument an `amount` to be withdrawn. If there is enough money
in the account to accommodate the withdrawal, then `withdraw` should
return the balance remaining after the withdrawal. Otherwise, `withdraw`
should return the message *Insufficient funds*. For example, if we begin
with \$100 in the account, we should obtain the following sequence of
responses using `withdraw`:

::: scheme
(withdraw 25)  *75*  (withdraw 25)  *50*  (withdraw 60)  *\"Insufficient
funds\"*  (withdraw 15)  *35*
:::

Observe that the expression `(withdraw 25)`, evaluated twice, yields
different values. This is a new kind of behavior for a procedure. Until
now, all our procedures could be viewed as specifications for computing
mathematical functions. A call to a procedure computed the value of the
function applied to the given arguments, and two calls to the same
procedure with the same arguments always produced the same result.[^129]

To implement `withdraw`, we can use a variable `balance` to indicate the
balance of money in the account and define `withdraw` as a procedure
that accesses `balance`. The `withdraw` procedure checks to see if
`balance` is at least as large as the requested `amount`. If so,
`withdraw` decrements `balance` by `amount` and returns the new value of
`balance`. Otherwise, `withdraw` returns the *Insufficient funds*
message. Here are the definitions of `balance` and `withdraw`:

::: scheme
(define balance 100) (define (withdraw amount) (if (\>= balance amount)
(begin (set! balance (- balance amount)) balance) \"Insufficient
funds\"))
:::

Decrementing `balance` is accomplished by the expression

::: scheme
(set! balance (- balance amount))
:::

This uses the `set!` special form, whose syntax is

::: scheme
(set!  $\color{SchemeDark}\langle$  *name*  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *new-value*  $\color{SchemeDark}\rangle$ )
:::

Here $\langle$*name*$\kern0.04em\rangle$ is a symbol and
$\langle$*new-value*$\kern0.04em\rangle$ is any expression. `set!`
changes $\langle$*name*$\kern0.04em\rangle$ so that its value is the
result obtained by evaluating $\langle$*new-value*$\kern0.04em\rangle$.
In the case at hand, we are changing `balance` so that its new value
will be the result of subtracting `amount` from the previous value of
`balance`.[^130]

`withdraw` also uses the `begin` special form to cause two expressions
to be evaluated in the case where the `if` test is true: first
decrementing `balance` and then returning the value of `balance`. In
general, evaluating the expression

::: scheme
(begin
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize k}}\rangle$ )
:::

causes the expressions $\langle\kern0.06em$*exp*$_1\rangle$ through
$\langle\kern0.06em$*exp*$_k\rangle$ to be evaluated in sequence and the
value of the final expression $\langle\kern0.06em$*exp*$_k\rangle$ to be
returned as the value of the entire `begin` form.[^131]

Although `withdraw` works as desired, the variable `balance` presents a
problem. As specified above, `balance` is a name defined in the global
environment and is freely accessible to be examined or modified by any
procedure. It would be much better if we could somehow make `balance`
internal to `withdraw`, so that `withdraw` would be the only procedure
that could access `balance` directly and any other procedure could
access `balance` only indirectly (through calls to `withdraw`). This
would more accurately model the notion that `balance` is a local state
variable used by `withdraw` to keep track of the state of the account.

We can make `balance` internal to `withdraw` by rewriting the definition
as follows:

::: scheme
(define new-withdraw (let ((balance 100)) (lambda (amount) (if (\>=
balance amount) (begin (set! balance (- balance amount)) balance)
\"Insufficient funds\"))))
:::

What we have done here is use `let` to establish an environment with a
local variable `balance`, bound to the initial value 100. Within this
local environment, we use `lambda` to create a procedure that takes
`amount` as an argument and behaves like our previous `withdraw`
procedure. This procedure---returned as the result of evaluating the
`let` expression---is `new/withdraw`, which behaves in precisely the
same way as `withdraw` but whose variable `balance` is not accessible by
any other procedure.[^132]

Combining `set!` with local variables is the general programming
technique we will use for constructing computational objects with local
state. Unfortunately, using this technique raises a serious problem:
When we first introduced procedures, we also introduced the substitution
model of evaluation ([Section 1.1.5](#Section 1.1.5)) to provide an
interpretation of what procedure application means. We said that
applying a procedure should be interpreted as evaluating the body of the
procedure with the formal parameters replaced by their values. The
trouble is that, as soon as we introduce assignment into our language,
substitution is no longer an adequate model of procedure application.
(We will see why this is so in [Section 3.1.3](#Section 3.1.3).) As a
consequence, we technically have at this point no way to understand why
the `new/withdraw` procedure behaves as claimed above. In order to
really understand a procedure such as `new/withdraw`, we will need to
develop a new model of procedure application. In [Section
3.2](#Section 3.2) we will introduce such a model, together with an
explanation of `set!` and local variables. First, however, we examine
some variations on the theme established by `new/withdraw`.

The following procedure, `make/withdraw`, creates "withdrawal
processors." The formal parameter `balance` in `make/withdraw` specifies
the initial amount of money in the account.[^133]

::: scheme
(define (make-withdraw balance) (lambda (amount) (if (\>= balance
amount) (begin (set! balance (- balance amount)) balance) \"Insufficient
funds\")))
:::

`make/withdraw` can be used as follows to create two objects `W1` and
`W2`:

::: scheme
(define W1 (make-withdraw 100)) (define W2 (make-withdraw 100))

(W1 50)  *50*  (W2 70)  *30*  (W2 40)  *\"Insufficient funds\"*  (W1 40)
 *10*
:::

Observe that `W1` and `W2` are completely independent objects, each with
its own local state variable `balance`. Withdrawals from one do not
affect the other.

We can also create objects that handle deposits as well as withdrawals,
and thus we can represent simple bank accounts. Here is a procedure that
returns a "bank-account object" with a specified initial balance:

::: scheme
(define (make-account balance) (define (withdraw amount) (if (\>=
balance amount) (begin (set! balance (- balance amount)) balance)
\"Insufficient funds\")) (define (deposit amount) (set! balance (+
balance amount)) balance) (define (dispatch m) (cond ((eq? m 'withdraw)
withdraw) ((eq? m 'deposit) deposit) (else (error \"Unknown request:
MAKE-ACCOUNT\" m)))) dispatch)
:::

Each call to `make/account` sets up an environment with a local state
variable `balance`. Within this environment, `make/account` defines
procedures `deposit` and `withdraw` that access `balance` and an
additional procedure `dispatch` that takes a "message" as input and
returns one of the two local procedures. The `dispatch` procedure itself
is returned as the value that represents the bank-account object. This
is precisely the *message-passing* style of programming that we saw in
[Section 2.4.3](#Section 2.4.3), although here we are using it in
conjunction with the ability to modify local variables.

`make/account` can be used as follows:

::: scheme
(define acc (make-account 100)) ((acc 'withdraw) 50)  *50*  ((acc
'withdraw) 60)  *\"Insufficient funds\"*  ((acc 'deposit) 40)  *90*
((acc 'withdraw) 60)  *30*
:::

Each call to `acc` returns the locally defined `deposit` or `withdraw`
procedure, which is then applied to the specified `amount`. As was the
case with `make/withdraw`, another call to `make/account`

::: scheme
(define acc2 (make-account 100))
:::

will produce a completely separate account object, which maintains its
own local `balance`.

> **[]{#Exercise 3.1 label="Exercise 3.1"}Exercise 3.1:** An
> *accumulator* is a procedure that is called repeatedly with a single
> numeric argument and accumulates its arguments into a sum. Each time
> it is called, it returns the currently accumulated sum. Write a
> procedure `make/accumulator` that generates accumulators, each
> maintaining an independent sum. The input to `make/accumulator` should
> specify the initial value of the sum; for example
>
> ::: scheme
> (define A (make-accumulator 5)) (A 10)  *15*  (A 10)  *25*
> :::

> **[]{#Exercise 3.2 label="Exercise 3.2"}Exercise 3.2:** In
> software-testing applications, it is useful to be able to count the
> number of times a given procedure is called during the course of a
> computation. Write a procedure `make/monitored` that takes as input a
> procedure, `f`, that itself takes one input. The result returned by
> `make/monitored` is a third procedure, say `mf`, that keeps track of
> the number of times it has been called by maintaining an internal
> counter. If the input to `mf` is the special symbol `how/many/calls?`,
> then `mf` returns the value of the counter. If the input is the
> special symbol `reset/count`, then `mf` resets the counter to zero.
> For any other input, `mf` returns the result of calling `f` on that
> input and increments the counter. For instance, we could make a
> monitored version of the `sqrt` procedure:
>
> ::: scheme
> (define s (make-monitored sqrt)) (s 100)  *10*  (s 'how-many-calls?)
>  *1*
> :::

> **[]{#Exercise 3.3 label="Exercise 3.3"}Exercise 3.3:** Modify the
> `make/account` procedure so that it creates password-protected
> accounts. That is, `make/account` should take a symbol as an
> additional argument, as in
>
> ::: scheme
> (define acc (make-account 100 'secret-password))
> :::
>
> The resulting account object should process a request only if it is
> accompanied by the password with which the account was created, and
> should otherwise return a complaint:
>
> ::: scheme
> ((acc 'secret-password 'withdraw) 40)  *60*  ((acc
> 'some-other-password 'deposit) 50)  *\"Incorrect password\"*
> :::

> **[]{#Exercise 3.4 label="Exercise 3.4"}Exercise 3.4:** Modify the
> `make/account` procedure of [Exercise 3.3](#Exercise 3.3) by adding
> another local state variable so that, if an account is accessed more
> than seven consecutive times with an incorrect password, it invokes
> the procedure `call/the/cops`.

### The Benefits of Introducing Assignment {#Section 3.1.2}

As we shall see, introducing assignment into our programming language
leads us into a thicket of difficult conceptual issues. Nevertheless,
viewing systems as collections of objects with local state is a powerful
technique for maintaining a modular design. As a simple example,
consider the design of a procedure `rand` that, whenever it is called,
returns an integer chosen at random.

It is not at all clear what is meant by "chosen at random." What we
presumably want is for successive calls to `rand` to produce a sequence
of numbers that has statistical properties of uniform distribution. We
will not discuss methods for generating suitable sequences here. Rather,
let us assume that we have a procedure `rand/update` that has the
property that if we start with a given number $x_1$ and form

::: scheme
 $\color{SchemeDark}x_2$  = (rand-update  $\color{SchemeDark}x_1$ )
 $\color{SchemeDark}x_3$  = (rand-update  $\color{SchemeDark}x_2$ )
:::

then the sequence of values $x_1$, $x_2$, $x_3$, $\dots$ will have the
desired statistical properties.[^134]

We can implement `rand` as a procedure with a local state variable `x`
that is initialized to some fixed value `random/init`. Each call to
`rand` computes `rand/update` of the current value of `x`, returns this
as the random number, and also stores this as the new value of `x`.

::: scheme
(define rand (let ((x random-init)) (lambda () (set! x (rand-update x))
x)))
:::

Of course, we could generate the same sequence of random numbers without
using assignment by simply calling `rand/update` directly. However, this
would mean that any part of our program that used random numbers would
have to explicitly remember the current value of `x` to be passed as an
argument to `rand/update`. To realize what an annoyance this would be,
consider using random numbers to implement a technique called *Monte
Carlo simulation*.

The Monte Carlo method consists of choosing sample experiments at random
from a large set and then making deductions on the basis of the
probabilities estimated from tabulating the results of those
experiments. For example, we can approximate $\pi$ using the fact that
$6/\pi^2$ is the probability that two integers chosen at random will
have no factors in common; that is, that their greatest common divisor
will be 1.[^135] To obtain the approximation to $\pi$, we perform a
large number of experiments. In each experiment we choose two integers
at random and perform a test to see if their gcd is 1. The
fraction of times that the test is passed gives us our estimate of
$6/\pi^2$, and from this we obtain our approximation to $\pi$.

The heart of our program is a procedure `monte/carlo`, which takes as
arguments the number of times to try an experiment, together with the
experiment, represented as a no-argument procedure that will return
either true or false each time it is run. `monte/carlo` runs the
experiment for the designated number of trials and returns a number
telling the fraction of the trials in which the experiment was found to
be true.

::: scheme
(define (estimate-pi trials) (sqrt (/ 6 (monte-carlo trials
cesaro-test)))) (define (cesaro-test) (= (gcd (rand) (rand)) 1))

(define (monte-carlo trials experiment) (define (iter trials-remaining
trials-passed) (cond ((= trials-remaining 0) (/ trials-passed trials))
((experiment) (iter (- trials-remaining 1) (+ trials-passed 1))) (else
(iter (- trials-remaining 1) trials-passed)))) (iter trials 0))
:::

Now let us try the same computation using `rand/update` directly rather
than `rand`, the way we would be forced to proceed if we did not use
assignment to model local state:

::: scheme
(define (estimate-pi trials) (sqrt (/ 6 (random-gcd-test trials
random-init)))) (define (random-gcd-test trials initial-x) (define (iter
trials-remaining trials-passed x) (let ((x1 (rand-update x))) (let ((x2
(rand-update x1))) (cond ((= trials-remaining 0) (/ trials-passed
trials)) ((= (gcd x1 x2) 1) (iter (- trials-remaining 1) (+
trials-passed 1) x2)) (else (iter (- trials-remaining 1) trials-passed
x2)))))) (iter trials 0 initial-x))
:::

While the program is still simple, it betrays some painful breaches of
modularity. In our first version of the program, using `rand`, we can
express the Monte Carlo method directly as a general `monte/carlo`
procedure that takes as an argument an arbitrary `experiment` procedure.
In our second version of the program, with no local state for the
random-number generator, `random/gcd/test` must explicitly manipulate
the random numbers `x1` and `x2` and recycle `x2` through the iterative
loop as the new input to `rand/update`. This explicit handling of the
random numbers intertwines the structure of accumulating test results
with the fact that our particular experiment uses two random numbers,
whereas other Monte Carlo experiments might use one random number or
three. Even the top-level procedure `estimate/pi` has to be concerned
with supplying an initial random number. The fact that the random-number
generator's insides are leaking out into other parts of the program
makes it difficult for us to isolate the Monte Carlo idea so that it can
be applied to other tasks. In the first version of the program,
assignment encapsulates the state of the random-number generator within
the `rand` procedure, so that the details of random-number generation
remain independent of the rest of the program.

The general phenomenon illustrated by the Monte Carlo example is this:
From the point of view of one part of a complex process, the other parts
appear to change with time. They have hidden time-varying local state.
If we wish to write computer programs whose structure reflects this
decomposition, we make computational objects (such as bank accounts and
random-number generators) whose behavior changes with time. We model
state with local state variables, and we model the changes of state with
assignments to those variables.

It is tempting to conclude this discussion by saying that, by
introducing assignment and the technique of hiding state in local
variables, we are able to structure systems in a more modular fashion
than if all state had to be manipulated explicitly, by passing
additional parameters. Unfortunately, as we shall see, the story is not
so simple.

> **[]{#Exercise 3.5 label="Exercise 3.5"}Exercise 3.5:** *Monte Carlo
> integration* is a method of estimating definite integrals by means of
> Monte Carlo simulation. Consider computing the area of a region of
> space described by a predicate $P(x, y)$ that is true for points
> $(x, y)$ in the region and false for points not in the region. For
> example, the region contained within a circle of radius 3 centered at
> (5, 7) is described by the predicate that tests whether
> $(x - 5)^2 + (y - 7)^2 \le 3^2$. To estimate the area of the region
> described by such a predicate, begin by choosing a rectangle that
> contains the region. For example, a rectangle with diagonally opposite
> corners at (2, 4) and (8, 10) contains the circle above. The desired
> integral is the area of that portion of the rectangle that lies in the
> region. We can estimate the integral by picking, at random, points
> $(x, y)$ that lie in the rectangle, and testing $P(x, y)$ for each
> point to determine whether the point lies in the region. If we try
> this with many points, then the fraction of points that fall in the
> region should give an estimate of the proportion of the rectangle that
> lies in the region. Hence, multiplying this fraction by the area of
> the entire rectangle should produce an estimate of the integral.
>
> Implement Monte Carlo integration as a procedure `estimate/integral`
> that takes as arguments a predicate `P`, upper and lower bounds `x1`,
> `x2`, `y1`, and `y2` for the rectangle, and the number of trials to
> perform in order to produce the estimate. Your procedure should use
> the same `monte/carlo` procedure that was used above to estimate
> $\pi$. Use your `estimate/integral` to produce an estimate of $\pi$ by
> measuring the area of a unit circle.
>
> You will find it useful to have a procedure that returns a number
> chosen at random from a given range. The following `random/in/range`
> procedure implements this in terms of the `random` procedure used in
> [Section 1.2.6](#Section 1.2.6), which returns a nonnegative number
> less than its input.[^136]
>
> ::: scheme
> (define (random-in-range low high) (let ((range (- high low))) (+ low
> (random range))))
> :::

> **[]{#Exercise 3.6 label="Exercise 3.6"}Exercise 3.6:** It is useful
> to be able to reset a random-number generator to produce a sequence
> starting from a given value. Design a new `rand` procedure that is
> called with an argument that is either the symbol `generate` or the
> symbol `reset` and behaves as follows: `(rand ’generate)` produces a
> new random number;
> `((rand ’reset)`$\;\langle$*new-value*$\kern0.11em\rangle$`)` resets
> the internal state variable to the designated
> $\langle$*new-value*$\kern0.08em\rangle$. Thus, by resetting the
> state, one can generate repeatable sequences. These are very handy to
> have when testing and debugging programs that use random numbers.

### The Costs of Introducing Assignment {#Section 3.1.3}

As we have seen, the `set!` operation enables us to model objects that
have local state. However, this advantage comes at a price. Our
programming language can no longer be interpreted in terms of the
substitution model of procedure application that we introduced in
[Section 1.1.5](#Section 1.1.5). Moreover, no simple model with "nice"
mathematical properties can be an adequate framework for dealing with
objects and assignment in programming languages.

So long as we do not use assignments, two evaluations of the same
procedure with the same arguments will produce the same result, so that
procedures can be viewed as computing mathematical functions.
Programming without any use of assignments, as we did throughout the
first two chapters of this book, is accordingly known as *functional
programming*.

To understand how assignment complicates matters, consider a simplified
version of the `make/withdraw` procedure of [Section
3.1.1](#Section 3.1.1) that does not bother to check for an insufficient
amount:

::: scheme
(define (make-simplified-withdraw balance) (lambda (amount) (set!
balance (- balance amount)) balance)) (define W
(make-simplified-withdraw 25)) (W 20)  *5*  (W 10)  *-5*
:::

Compare this procedure with the following `make/decrementer` procedure,
which does not use `set!`:

::: scheme
(define (make-decrementer balance) (lambda (amount) (- balance amount)))
:::

`make/decrementer` returns a procedure that subtracts its input from a
designated amount `balance`, but there is no accumulated effect over
successive calls, as with `make/simplified/withdraw`:

::: scheme
(define D (make-decrementer 25)) (D 20)  *5*  (D 10)  *15*
:::

We can use the substitution model to explain how `make/decrementer`
works. For instance, let us analyze the evaluation of the expression

::: scheme
((make-decrementer 25) 20)
:::

We first simplify the operator of the combination by substituting 25 for
`balance` in the body of `make/decrementer`. This reduces the expression
to

::: scheme
((lambda (amount) (- 25 amount)) 20)
:::

Now we apply the operator by substituting 20 for `amount` in the body of
the `lambda` expression:

::: scheme
(- 25 20)
:::

The final answer is 5.

Observe, however, what happens if we attempt a similar substitution
analysis with `make/simplified/withdraw`:

::: scheme
((make-simplified-withdraw 25) 20)
:::

We first simplify the operator by substituting 25 for `balance` in the
body of `make/simplified/withdraw`. This reduces the expression to[^137]

::: scheme
((lambda (amount) (set! balance (- 25 amount)) 25) 20)
:::

Now we apply the operator by substituting 20 for `amount` in the body of
the `lambda` expression:

::: scheme
(set! balance (- 25 20)) 25
:::

If we adhered to the substitution model, we would have to say that the
meaning of the procedure application is to first set `balance` to 5 and
then return 25 as the value of the expression. This gets the wrong
answer. In order to get the correct answer, we would have to somehow
distinguish the first occurrence of `balance` (before the effect of the
`set!`) from the second occurrence of `balance` (after the effect of the
`set!`), and the substitution model cannot do this.

The trouble here is that substitution is based ultimately on the notion
that the symbols in our language are essentially names for values. But
as soon as we introduce `set!` and the idea that the value of a variable
can change, a variable can no longer be simply a name. Now a variable
somehow refers to a place where a value can be stored, and the value
stored at this place can change. In [Section 3.2](#Section 3.2) we will
see how environments play this role of "place" in our computational
model.

#### Sameness and change {#sameness-and-change .unnumbered}

The issue surfacing here is more profound than the mere breakdown of a
particular model of computation. As soon as we introduce change into our
computational models, many notions that were previously straightforward
become problematical. Consider the concept of two things being "the
same."

Suppose we call `make/decrementer` twice with the same argument to
create two procedures:

::: scheme
(define D1 (make-decrementer 25)) (define D2 (make-decrementer 25))
:::

Are `D1` and `D2` the same? An acceptable answer is yes, because `D1`
and `D2` have the same computational behavior---each is a procedure that
subtracts its input from 25. In fact, `D1` could be substituted for `D2`
in any computation without changing the result.

Contrast this with making two calls to `make/simplified/withdraw`:

::: scheme
(define W1 (make-simplified-withdraw 25)) (define W2
(make-simplified-withdraw 25))
:::

Are `W1` and `W2` the same? Surely not, because calls to `W1` and `W2`
have distinct effects, as shown by the following sequence of
interactions:

::: scheme
(W1 20)  *5*  (W1 20)  *-15*  (W2 20)  *5*
:::

Even though `W1` and `W2` are "equal" in the sense that they are both
created by evaluating the same expression,
`(make/simplified/withdraw 25)`, it is not true that `W1` could be
substituted for `W2` in any expression without changing the result of
evaluating the expression.

A language that supports the concept that "equals can be substituted for
equals" in an expression without changing the value of the expression is
said to be *referentially transparent*. Referential transparency is
violated when we include `set!` in our computer language. This makes it
tricky to determine when we can simplify expressions by substituting
equivalent expressions. Consequently, reasoning about programs that use
assignment becomes drastically more difficult.

Once we forgo referential transparency, the notion of what it means for
computational objects to be "the same" becomes difficult to capture in a
formal way. Indeed, the meaning of "same" in the real world that our
programs model is hardly clear in itself. In general, we can determine
that two apparently identical objects are indeed "the same one" only by
modifying one object and then observing whether the other object has
changed in the same way. But how can we tell if an object has "changed"
other than by observing the "same" object twice and seeing whether some
property of the object differs from one observation to the next? Thus,
we cannot determine "change" without some *a priori* notion of
"sameness," and we cannot determine sameness without observing the
effects of change.

As an example of how this issue arises in programming, consider the
situation where Peter and Paul have a bank account with \$100 in it.
There is a substantial difference between modeling this as

::: scheme
(define peter-acc (make-account 100)) (define paul-acc (make-account
100))
:::

and modeling it as

::: scheme
(define peter-acc (make-account 100)) (define paul-acc peter-acc)
:::

In the first situation, the two bank accounts are distinct. Transactions
made by Peter will not affect Paul's account, and vice versa. In the
second situation, however, we have defined `paul/acc` to be *the same
thing* as `peter/acc`. In effect, Peter and Paul now have a joint bank
account, and if Peter makes a withdrawal from `peter/acc` Paul will
observe less money in `paul/acc`. These two similar but distinct
situations can cause confusion in building computational models. With
the shared account, in particular, it can be especially confusing that
there is one object (the bank account) that has two different names
(`peter/acc` and `paul/acc`); if we are searching for all the places in
our program where `paul/acc` can be changed, we must remember to look
also at things that change `peter/acc`.[^138]

With reference to the above remarks on "sameness" and "change," observe
that if Peter and Paul could only examine their bank balances, and could
not perform operations that changed the balance, then the issue of
whether the two accounts are distinct would be moot. In general, so long
as we never modify data objects, we can regard a compound data object to
be precisely the totality of its pieces. For example, a rational number
is determined by giving its numerator and its denominator. But this view
is no longer valid in the presence of change, where a compound data
object has an "identity" that is something different from the pieces of
which it is composed. A bank account is still "the same" bank account
even if we change the balance by making a withdrawal; conversely, we
could have two different bank accounts with the same state information.
This complication is a consequence, not of our programming language, but
of our perception of a bank account as an object. We do not, for
example, ordinarily regard a rational number as a changeable object with
identity, such that we could change the numerator and still have "the
same" rational number.

#### Pitfalls of imperative programming {#pitfalls-of-imperative-programming .unnumbered}

In contrast to functional programming, programming that makes extensive
use of assignment is known as *imperative programming*. In addition to
raising complications about computational models, programs written in
imperative style are susceptible to bugs that cannot occur in functional
programs. For example, recall the iterative factorial program from
[Section 1.2.1](#Section 1.2.1):

::: scheme
(define (factorial n) (define (iter product counter) (if (\> counter n)
product (iter (\* counter product) (+ counter 1)))) (iter 1 1))
:::

Instead of passing arguments in the internal iterative loop, we could
adopt a more imperative style by using explicit assignment to update the
values of the variables `product` and `counter`:

::: scheme
(define (factorial n) (let ((product 1) (counter 1)) (define (iter) (if
(\> counter n) product (begin (set! product (\* counter product)) (set!
counter (+ counter 1)) (iter)))) (iter)))
:::

This does not change the results produced by the program, but it does
introduce a subtle trap. How do we decide the order of the assignments?
As it happens, the program is correct as written. But writing the
assignments in the opposite order

::: scheme
(set! counter (+ counter 1)) (set! product (\* counter product))
:::

would have produced a different, incorrect result. In general,
programming with assignment forces us to carefully consider the relative
orders of the assignments to make sure that each statement is using the
correct version of the variables that have been changed. This issue
simply does not arise in functional programs.[^139]

The complexity of imperative programs becomes even worse if we consider
applications in which several processes execute concurrently. We will
return to this in [Section 3.4](#Section 3.4). First, however, we will
address the issue of providing a computational model for expressions
that involve assignment, and explore the uses of objects with local
state in designing simulations.

> **[]{#Exercise 3.7 label="Exercise 3.7"}Exercise 3.7:** Consider the
> bank account objects created by `make/account`, with the password
> modification described in [Exercise 3.3](#Exercise 3.3). Suppose that
> our banking system requires the ability to make joint accounts. Define
> a procedure `make/joint` that accomplishes this. `make/joint` should
> take three arguments. The first is a password-protected account. The
> second argument must match the password with which the account was
> defined in order for the `make/joint` operation to proceed. The third
> argument is a new password. `make/joint` is to create an additional
> access to the original account using the new password. For example, if
> `peter/acc` is a bank account with password `open/sesame`, then
>
> ::: scheme
> (define paul-acc (make-joint peter-acc 'open-sesame 'rosebud))
> :::
>
> will allow one to make transactions on `peter/acc` using the name
> `paul/acc` and the password `rosebud`. You may wish to modify your
> solution to [Exercise 3.3](#Exercise 3.3) to accommodate this new
> feature.

> **[]{#Exercise 3.8 label="Exercise 3.8"}Exercise 3.8:** When we
> defined the evaluation model in [Section 1.1.3](#Section 1.1.3), we
> said that the first step in evaluating an expression is to evaluate
> its subexpressions. But we never specified the order in which the
> subexpressions should be evaluated (e.g., left to right or right to
> left). When we introduce assignment, the order in which the arguments
> to a procedure are evaluated can make a difference to the result.
> Define a simple procedure `f` such that evaluating
>
> ::: scheme
> (+ (f 0) (f 1))
> :::
>
> will return 0 if the arguments to `+` are evaluated from left to right
> but will return 1 if the arguments are evaluated from right to left.

## The Environment Model of Evaluation {#Section 3.2}

When we introduced compound procedures in [Chapter 1](#Chapter 1), we
used the substitution model of evaluation ([Section
1.1.5](#Section 1.1.5)) to define what is meant by applying a procedure
to arguments:

-   To apply a compound procedure to arguments, evaluate the body of the
    procedure with each formal parameter replaced by the corresponding
    argument.

Once we admit assignment into our programming language, such a
definition is no longer adequate. In particular, [Section
3.1.3](#Section 3.1.3) argued that, in the presence of assignment, a
variable can no longer be considered to be merely a name for a value.
Rather, a variable must somehow designate a "place" in which values can
be stored. In our new model of evaluation, these places will be
maintained in structures called *environments*.

An environment is a sequence of *frames*. Each frame is a table
(possibly empty) of *bindings*, which associate variable names with
their corresponding values. (A single frame may contain at most one
binding for any variable.) Each frame also has a pointer to its
*enclosing environment*, unless, for the purposes of discussion, the
frame is considered to be *global*. The *value of a variable* with
respect to an environment is the value given by the binding of the
variable in the first frame in the environment that contains a binding
for that variable. If no frame in the sequence specifies a binding for
the variable, then the variable is said to be *unbound* in the
environment.

[Figure 3.1](#Figure 3.1) shows a simple environment structure
consisting of three frames, labeled I, II, and III. In the diagram, A,
B, C, and D are pointers to environments. C and D point to the same
environment. The variables `z` and `x` are bound in frame II, while `y`
and `x` are bound in frame I. The value of `x` in environment D is 3.
The value of `x` with respect to environment B is also 3. This is
determined as follows: We examine the first frame in the sequence (frame
III) and do not find a binding for `x`, so we proceed to the enclosing
environment D and find the binding in frame I. On the other hand, the
value of `x` in environment A is 7, because the first frame in the
sequence (frame II) contains a binding of `x` to 7. With respect to
environment A, the binding of `x` to 7 in frame II is said to *shadow*
the binding of `x` to 3 in frame I.

[]{#Figure 3.1 label="Figure 3.1"}

![image](fig/chap3/Fig3.1.pdf){width="48mm"}

**Figure 3.1:** A simple environment structure.

The environment is crucial to the evaluation process, because it
determines the context in which an expression should be evaluated.
Indeed, one could say that expressions in a programming language do not,
in themselves, have any meaning. Rather, an expression acquires a
meaning only with respect to some environment in which it is evaluated.
Even the interpretation of an expression as straightforward as `(+ 1 1)`
depends on an understanding that one is operating in a context in which
`+` is the symbol for addition. Thus, in our model of evaluation we will
always speak of evaluating an expression with respect to some
environment. To describe interactions with the interpreter, we will
suppose that there is a global environment, consisting of a single frame
(with no enclosing environment) that includes values for the symbols
associated with the primitive procedures. For example, the idea that `+`
is the symbol for addition is captured by saying that the symbol `+` is
bound in the global environment to the primitive addition procedure.

### The Rules for Evaluation {#Section 3.2.1}

The overall specification of how the interpreter evaluates a combination
remains the same as when we first introduced it in [Section
1.1.3](#Section 1.1.3):

-   To evaluate a combination:

1.  Evaluate the subexpressions of the combination.[^140]

2.  Apply the value of the operator subexpression to the values of the
    operand subexpressions.

The environment model of evaluation replaces the substitution model in
specifying what it means to apply a compound procedure to arguments.

In the environment model of evaluation, a procedure is always a pair
consisting of some code and a pointer to an environment. Procedures are
created in one way only: by evaluating a λ-expression. This produces a
procedure whose code is obtained from the text of the λ-expression and
whose environment is the environment in which the λ-expression was
evaluated to produce the procedure. For example, consider the procedure
definition

::: scheme
(define (square x) (\* x x))
:::

evaluated in the global environment. The procedure definition syntax is
just syntactic sugar for an underlying implicit λ-expression. It would
have been equivalent to have used

::: scheme
(define square (lambda (x) (\* x x)))
:::

which evaluates `(lambda (x) (* x x))` and binds `square` to the
resulting value, all in the global environment.

[Figure 3.2](#Figure 3.2) shows the result of evaluating this `define`
expression. The procedure object is a pair whose code specifies that the
procedure has one formal parameter, namely `x`, and a procedure body
`(* x x)`. The environment part of the procedure is a pointer to the
global environment, since that is the environment in which the
λ-expression was evaluated to produce the procedure. A new binding,
which associates the procedure object with the symbol `square`, has been
added to the global frame. In general, `define` creates definitions by
adding bindings to frames.

[]{#Figure 3.2 label="Figure 3.2"}

![image](fig/chap3/Fig3.2b.pdf){width="49mm"}

> **Figure 3.2:** Environment structure produced by evaluating\
> `(define (square x) (* x x))` in the global environment.

Now that we have seen how procedures are created, we can describe how
procedures are applied. The environment model specifies: To apply a
procedure to arguments, create a new environment containing a frame that
binds the parameters to the values of the arguments. The enclosing
environment of this frame is the environment specified by the procedure.
Now, within this new environment, evaluate the procedure body.

To show how this rule is followed, [Figure 3.3](#Figure 3.3) illustrates
the environment structure created by evaluating the expression
`(square 5)` in the global environment, where `square` is the procedure
generated in [Figure 3.2](#Figure 3.2). Applying the procedure results
in the creation of a new environment, labeled E1 in the figure, that
begins with a frame in which `x`, the formal parameter for the
procedure, is bound to the argument 5. The pointer leading upward from
this frame shows that the frame's enclosing environment is the global
environment. The global environment is chosen here, because this is the
environment that is indicated as part of the `square` procedure object.
Within E1, we evaluate the body of the procedure, `(* x x)`. Since the
value of `x` in E1 is 5, the result is `(* 5 5)`, or 25.

[]{#Figure 3.3 label="Figure 3.3"}

![image](fig/chap3/Fig3.3b.pdf){width="78mm"}

> **Figure 3.3:** Environment created by evaluating `(square 5)` in the
> global environment.

The environment model of procedure application can be summarized by two
rules:

-   A procedure object is applied to a set of arguments by constructing
    a frame, binding the formal parameters of the procedure to the
    arguments of the call, and then evaluating the body of the procedure
    in the context of the new environment constructed. The new frame has
    as its enclosing environment the environment part of the procedure
    object being applied.

-   A procedure is created by evaluating a λ-expression relative to a
    given environment. The resulting procedure object is a pair
    consisting of the text of the λ-expression and a pointer to the
    environment in which the procedure was created.

We also specify that defining a symbol using `define` creates a binding
in the current environment frame and assigns to the symbol the indicated
value.[^141] Finally, we specify the behavior of `set!`, the operation
that forced us to introduce the environment model in the first place.
Evaluating the expression
`(set!`$\;\langle$*variable*$\kern0.08em\rangle$$\;\langle$*value*$\kern0.08em\rangle$`)`
in some environment locates the binding of the variable in the
environment and changes that binding to indicate the new value. That is,
one finds the first frame in the environment that contains a binding for
the variable and modifies that frame. If the variable is unbound in the
environment, then `set!` signals an error.

These evaluation rules, though considerably more complex than the
substitution model, are still reasonably straightforward. Moreover, the
evaluation model, though abstract, provides a correct description of how
the interpreter evaluates expressions. In [Chapter 4](#Chapter 4) we
shall see how this model can serve as a blueprint for implementing a
working interpreter. The following sections elaborate the details of the
model by analyzing some illustrative programs.

### Applying Simple Procedures {#Section 3.2.2}

When we introduced the substitution model in [Section
1.1.5](#Section 1.1.5) we showed how the combination `(f 5)` evaluates
to 136, given the following procedure definitions:

::: scheme
(define (square x) (\* x x)) (define (sum-of-squares x y) (+ (square x)
(square y))) (define (f a) (sum-of-squares (+ a 1) (\* a 2)))
:::

We can analyze the same example using the environment model. [Figure
3.4](#Figure 3.4) shows the three procedure objects created by
evaluating the definitions of `f`, `square`, and `sum/of/squares` in the
global environment. Each procedure object consists of some code,
together with a pointer to the global environment.

[]{#Figure 3.4 label="Figure 3.4"}

![image](fig/chap3/Fig3.4a.pdf){width="106mm"}

**Figure 3.4:** Procedure objects in the global frame.

In [Figure 3.5](#Figure 3.5) we see the environment structure created by
evaluating the expression `(f 5)`. The call to `f` creates a new
environment E1 beginning with a frame in which `a`, the formal parameter
of `f`, is bound to the argument 5. In E1, we evaluate the body of `f`:

::: scheme
(sum-of-squares (+ a 1) (\* a 2))
:::

To evaluate this combination, we first evaluate the subexpressions. The
first subexpression, `sum/of/squares`, has a value that is a procedure
object. (Notice how this value is found: We first look in the first
frame of E1, which contains no binding for `sum/of/squares`. Then we
proceed to the enclosing environment, i.e. the global environment, and
find the binding shown in [Figure 3.4](#Figure 3.4).) The other two
subexpressions are evaluated by applying the primitive operations `+`
and `*` to evaluate the two combinations `(+ a 1)` and `(* a 2)` to
obtain 6 and 10, respectively.

Now we apply the procedure object `sum/of/squares` to the arguments 6
and 10. This results in a new environment E2 in which the formal
parameters `x` and `y` are bound to the arguments. Within E2 we evaluate
the combination `(+ (square x) (square y))`. This leads us to evaluate
`(square x)`, where `square` is found in the global frame and `x` is 6.
Once again, we set up a new environment, E3, in which `x` is bound to 6,
and within this we evaluate the body of `square`, which is `(* x x)`.
Also as part of applying `sum/of/squares`, we must evaluate the
subexpression `(square y)`, where `y` is 10. This second call to
`square` creates another environment, E4, in which `x`, the formal
parameter of `square`, is bound to 10. And within E4 we must evaluate
`(* x x)`.

[]{#Figure 3.5 label="Figure 3.5"}

![image](fig/chap3/Fig3.5a.pdf){width="100mm"}

> **Figure 3.5:** Environments created by evaluating `(f 5)` using the
> procedures in [Figure 3.4](#Figure 3.4).

The important point to observe is that each call to `square` creates a
new environment containing a binding for `x`. We can see here how the
different frames serve to keep separate the different local variables
all named `x`. Notice that each frame created by `square` points to the
global environment, since this is the environment indicated by the
`square` procedure object.

After the subexpressions are evaluated, the results are returned. The
values generated by the two calls to `square` are added by
`sum/of/squares`, and this result is returned by `f`. Since our focus
here is on the environment structures, we will not dwell on how these
returned values are passed from call to call; however, this is also an
important aspect of the evaluation process, and we will return to it in
detail in [Chapter 5](#Chapter 5).

> **[]{#Exercise 3.9 label="Exercise 3.9"}Exercise 3.9:** In [Section
> 1.2.1](#Section 1.2.1) we used the substitution model to analyze two
> procedures for computing factorials, a recursive version
>
> ::: scheme
> (define (factorial n) (if (= n 1) 1 (\* n (factorial (- n 1)))))
> :::
>
> and an iterative version
>
> ::: scheme
> (define (factorial n) (fact-iter 1 1 n)) (define (fact-iter product
> counter max-count) (if (\> counter max-count) product (fact-iter (\*
> counter product) (+ counter 1) max-count)))
> :::
>
> Show the environment structures created by evaluating\
> `(factorial 6)` using each version of the `factorial` procedure.[^142]

### Frames as the Repository of Local State {#Section 3.2.3}

We can turn to the environment model to see how procedures and
assignment can be used to represent objects with local state. As an
example, consider the "withdrawal processor" from [Section
3.1.1](#Section 3.1.1) created by calling the procedure

::: scheme
(define (make-withdraw balance) (lambda (amount) (if (\>= balance
amount) (begin (set! balance (- balance amount)) balance) \"Insufficient
funds\")))
:::

Let us describe the evaluation of

::: scheme
(define W1 (make-withdraw 100))
:::

followed by

::: scheme
(W1 50)  *50*
:::

[Figure 3.6](#Figure 3.6) shows the result of defining the
`make/withdraw` procedure in the global environment. This produces a
procedure object that contains a pointer to the global environment. So
far, this is no different from the examples we have already seen, except
that the body of the procedure is itself a λ-expression.

[]{#Figure 3.6 label="Figure 3.6"}

![image](fig/chap3/Fig3.6b.pdf){width="91mm"}

> **Figure 3.6:** Result of defining `make/withdraw` in the global
> environment.

[]{#Figure 3.7 label="Figure 3.7"}

![image](fig/chap3/Fig3.7a.pdf){width="100mm"}

**Figure 3.7:** Result of evaluating `(define W1 (make/withdraw 100))`.

The interesting part of the computation happens when we apply the
procedure `make/withdraw` to an argument:

::: scheme
(define W1 (make-withdraw 100))
:::

We begin, as usual, by setting up an environment E1 in which the formal
parameter `balance` is bound to the argument 100. Within this
environment, we evaluate the body of `make/withdraw`, namely the
λ-expression. This constructs a new procedure object, whose code is as
specified by the `lambda` and whose environment is E1, the environment
in which the `lambda` was evaluated to produce the procedure. The
resulting procedure object is the value returned by the call to
`make/withdraw`. This is bound to `W1` in the global environment, since
the `define` itself is being evaluated in the global environment.
[Figure 3.7](#Figure 3.7) shows the resulting environment structure.

[]{#Figure 3.8 label="Figure 3.8"}

![image](fig/chap3/Fig3.8c.pdf){width="99mm"}

**Figure 3.8:** Environments created by applying the procedure object
`W1`.

Now we can analyze what happens when `W1` is applied to an argument:

::: scheme
(W1 50)  *50*
:::

We begin by constructing a frame in which `amount`, the formal parameter
of `W1`, is bound to the argument 50. The crucial point to observe is
that this frame has as its enclosing environment not the global
environment, but rather the environment E1, because this is the
environment that is specified by the `W1` procedure object. Within this
new environment, we evaluate the body of the procedure:

::: scheme
(if (\>= balance amount) (begin (set! balance (- balance amount))
balance) \"Insufficient funds\")
:::

The resulting environment structure is shown in [Figure
3.8](#Figure 3.8). The expression being evaluated references both
`amount` and `balance`. `amount` will be found in the first frame in the
environment, while `balance` will be found by following the
enclosing-environment pointer to E1.

[]{#Figure 3.9 label="Figure 3.9"}

![image](fig/chap3/Fig3.9a.pdf){width="96mm"}

**Figure 3.9:** Environments after the call to `W1`.

When the `set!` is executed, the binding of `balance` in E1 is changed.
At the completion of the call to `W1`, `balance` is 50, and the frame
that contains `balance` is still pointed to by the procedure object
`W1`. The frame that binds `amount` (in which we executed the code that
changed `balance`) is no longer relevant, since the procedure call that
constructed it has terminated, and there are no pointers to that frame
from other parts of the environment. The next time `W1` is called, this
will build a new frame that binds `amount` and whose enclosing
environment is E1. We see that E1 serves as the "place" that holds the
local state variable for the procedure object `W1`. [Figure
3.9](#Figure 3.9) shows the situation after the call to `W1`.

Observe what happens when we create a second "withdraw" object by making
another call to `make/withdraw`:

::: scheme
(define W2 (make-withdraw 100))
:::

[]{#Figure 3.10 label="Figure 3.10"}

![image](fig/chap3/Fig3.10a.pdf){width="108mm"}

> **Figure 3.10:** Using `(define W2 (make/withdraw 100))` to create a
> second object.

This produces the environment structure of [Figure 3.10](#Figure 3.10),
which shows that `W2` is a procedure object, that is, a pair with some
code and an environment. The environment E2 for `W2` was created by the
call to `make/withdraw`. It contains a frame with its own local binding
for `balance`. On the other hand, `W1` and `W2` have the same code: the
code specified by the λ-expression in the body of `make/withdraw`.[^143]
We see here why `W1` and `W2` behave as independent objects. Calls to
`W1` reference the state variable `balance` stored in E1, whereas calls
to `W2` reference the `balance` stored in E2. Thus, changes to the local
state of one object do not affect the other object.

> **[]{#Exercise 3.10 label="Exercise 3.10"}Exercise 3.10:** In the
> `make/withdraw` procedure, the local variable `balance` is created as
> a parameter of `make/withdraw`. We could also create the local state
> variable explicitly, using `let`, as follows:
>
> ::: scheme
> (define (make-withdraw initial-amount) (let ((balance initial-amount))
> (lambda (amount) (if (\>= balance amount) (begin (set! balance (-
> balance amount)) balance) \"Insufficient funds\"))))
> :::
>
> Recall from [Section 1.3.2](#Section 1.3.2) that `let` is simply
> syntactic sugar for a procedure call:
>
> ::: scheme
> (let
> (( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}\rangle$ ))
>  $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
> :::
>
> is interpreted as an alternate syntax for
>
> ::: scheme
> ((lambda
> ( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}\rangle$ )
>  $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
>  $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}\rangle$ )
> :::
>
> Use the environment model to analyze this alternate version of
> `make/withdraw`, drawing figures like the ones above to illustrate the
> interactions
>
> ::: scheme
> (define W1 (make-withdraw 100)) (W1 50) (define W2 (make-withdraw
> 100))
> :::
>
> Show that the two versions of `make/withdraw` create objects with the
> same behavior. How do the environment structures differ for the two
> versions?

### Internal Definitions {#Section 3.2.4}

[Section 1.1.8](#Section 1.1.8) introduced the idea that procedures can
have internal definitions, thus leading to a block structure as in the
following procedure to compute square roots:

::: scheme
(define (sqrt x) (define (good-enough? guess) (\< (abs (- (square guess)
x)) 0.001)) (define (improve guess) (average guess (/ x guess))) (define
(sqrt-iter guess) (if (good-enough? guess) guess (sqrt-iter (improve
guess)))) (sqrt-iter 1.0))
:::

Now we can use the environment model to see why these internal
definitions behave as desired. [Figure 3.11](#Figure 3.11) shows the
point in the evaluation of the expression `(sqrt 2)` where the internal
procedure `good/enough?` has been called for the first time with `guess`
equal to 1.

Observe the structure of the environment. `sqrt` is a symbol in the
global environment that is bound to a procedure object whose associated
environment is the global environment. When `sqrt` was called, a new
environment E1 was formed, subordinate to the global environment, in
which the parameter `x` is bound to 2. The body of `sqrt` was then
evaluated in E1. Since the first expression in the body of `sqrt` is

::: scheme
(define (good-enough? guess) (\< (abs (- (square guess) x)) 0.001))
:::

evaluating this expression defined the procedure `good/enough?` in the
environment E1. To be more precise, the symbol `good/enough?` was added
to the first frame of E1, bound to a procedure object whose associated
environment is E1. Similarly, `improve` and `sqrt/iter` were defined as
procedures in E1. For conciseness, [Figure 3.11](#Figure 3.11) shows
only the procedure object for `good/enough?`.

[]{#Figure 3.11 label="Figure 3.11"}

![image](fig/chap3/Fig3.11a.pdf){width="107mm"}

> **Figure 3.11:** `sqrt` procedure with internal definitions.

After the local procedures were defined, the expression
`(sqrt/iter 1.0)` was evaluated, still in environment E1. So the
procedure object bound to `sqrt/iter` in E1 was called with 1 as an
argument. This created an environment E2 in which `guess`, the parameter
of `sqrt/iter`, is bound to 1. `sqrt/iter` in turn called `good/enough?`
with the value of `guess` (from E2) as the argument for `good/enough?`.
This set up another environment, E3, in which `guess` (the parameter of
`good/enough?`) is bound to 1. Although `sqrt/iter` and `good/enough?`
both have a parameter named `guess`, these are two distinct local
variables located in different frames. Also, E2 and E3 both have E1 as
their enclosing environment, because the `sqrt/iter` and `good/enough?`
procedures both have E1 as their environment part. One consequence of
this is that the symbol `x` that appears in the body of `good/enough?`
will reference the binding of `x` that appears in E1, namely the value
of `x` with which the original `sqrt` procedure was called.

The environment model thus explains the two key properties that make
local procedure definitions a useful technique for modularizing
programs:

-   The names of the local procedures do not interfere with names
    external to the enclosing procedure, because the local procedure
    names will be bound in the frame that the procedure creates when it
    is run, rather than being bound in the global environment.

-   The local procedures can access the arguments of the enclosing
    procedure, simply by using parameter names as free variables. This
    is because the body of the local procedure is evaluated in an
    environment that is subordinate to the evaluation environment for
    the enclosing procedure.

> **[]{#Exercise 3.11 label="Exercise 3.11"}Exercise 3.11:** In [Section
> 3.2.3](#Section 3.2.3) we saw how the environment model described the
> behavior of procedures with local state. Now we have seen how internal
> definitions work. A typical message-passing procedure contains both of
> these aspects. Consider the bank account procedure of [Section
> 3.1.1](#Section 3.1.1):
>
> ::: scheme
> (define (make-account balance) (define (withdraw amount) (if (\>=
> balance amount) (begin (set! balance (- balance amount)) balance)
> \"Insufficient funds\")) (define (deposit amount) (set! balance (+
> balance amount)) balance) (define (dispatch m) (cond ((eq? m
> 'withdraw) withdraw) ((eq? m 'deposit) deposit) (else (error \"Unknown
> request: MAKE-ACCOUNT\" m)))) dispatch)
> :::
>
> Show the environment structure generated by the sequence of
> interactions
>
> ::: scheme
> (define acc (make-account 50)) ((acc 'deposit) 40)  *90*  ((acc
> 'withdraw) 60)  *30*
> :::
>
> Where is the local state for `acc` kept? Suppose we define another
> account
>
> ::: scheme
> (define acc2 (make-account 100))
> :::
>
> How are the local states for the two accounts kept distinct? Which
> parts of the environment structure are shared between `acc` and
> `acc2`?

## Modeling with Mutable Data {#Section 3.3}

Chapter 2 dealt with compound data as a means for constructing
computational objects that have several parts, in order to model
real-world objects that have several aspects. In that chapter we
introduced the discipline of data abstraction, according to which data
structures are specified in terms of constructors, which create data
objects, and selectors, which access the parts of compound data objects.
But we now know that there is another aspect of data that [Chapter
2](#Chapter 2) did not address. The desire to model systems composed of
objects that have changing state leads us to the need to modify compound
data objects, as well as to construct and select from them. In order to
model compound objects with changing state, we will design data
abstractions to include, in addition to selectors and constructors,
operations called *mutators*, which modify data objects. For instance,
modeling a banking system requires us to change account balances. Thus,
a data structure for representing bank accounts might admit an operation

::: scheme
(set-balance!
 $\color{SchemeDark}\langle$  *account*  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *new-value*  $\color{SchemeDark}\rangle$ )
:::

that changes the balance of the designated account to the designated new
value. Data objects for which mutators are defined are known as *mutable
data objects*.

[Chapter 2](#Chapter 2) introduced pairs as a general-purpose "glue" for
synthesizing compound data. We begin this section by defining basic
mutators for pairs, so that pairs can serve as building blocks for
constructing mutable data objects. These mutators greatly enhance the
representational power of pairs, enabling us to build data structures
other than the sequences and trees that we worked with in [Section
2.2](#Section 2.2). We also present some examples of simulations in
which complex systems are modeled as collections of objects with local
state.

### Mutable List Structure {#Section 3.3.1}

The basic operations on pairs---`cons`, `car`, and `cdr`---can be used
to construct list structure and to select parts from list structure, but
they are incapable of modifying list structure. The same is true of the
list operations we have used so far, such as `append` and `list`, since
these can be defined in terms of `cons`, `car`, and `cdr`. To modify
list structures we need new operations.

The primitive mutators for pairs are `set/car!` and `set/cdr!`.
`set/car!` takes two arguments, the first of which must be a pair. It
modifies this pair, replacing the `car` pointer by a pointer to the
second argument of `set/car!`.[^144]

As an example, suppose that `x` is bound to the list `((a b) c d)` and
`y` to the list `(e f)` as illustrated in [Figure 3.12](#Figure 3.12).
Evaluating the expression ` (set/car! x y)` modifies the pair to which
`x` is bound, replacing its `car` by the value of `y`. The result of the
operation is shown in [Figure 3.13](#Figure 3.13). The structure `x` has
been modified and would now be printed as `((e f) c d)`. The pairs
representing the list `(a b)`, identified by the pointer that was
replaced, are now detached from the original structure.[^145]

Compare [Figure 3.13](#Figure 3.13) with [Figure 3.14](#Figure 3.14),
which illustrates the result of executing `(define z (cons y (cdr x)))`
with `x` and `y` bound to the original lists of [Figure
3.12](#Figure 3.12). The variable `z` is now bound to a new pair created
by the `cons` operation; the list to which `x` is bound is unchanged.

[]{#Figure 3.12 label="Figure 3.12"}

![image](fig/chap3/Fig3.12b.pdf){width="72mm"}

> **Figure 3.12:** Lists `x`: `((a b) c d)` and `y`: `(e f)`.

[]{#Figure 3.13 label="Figure 3.13"}

![image](fig/chap3/Fig3.13b.pdf){width="72mm"}

**Figure 3.13:** Effect of `(set/car! x y)` on the lists in [Figure
3.12](#Figure 3.12).

[]{#Figure 3.14 label="Figure 3.14"}

![image](fig/chap3/Fig3.14b.pdf){width="72mm"}

> **Figure 3.14:** Effect of `(define z (cons y (cdr x)))` on the lists
> in [Figure 3.12](#Figure 3.12).

[]{#Figure 3.15 label="Figure 3.15"}

![image](fig/chap3/Fig3.15b.pdf){width="72mm"}

**Figure 3.15:** Effect of `(set/cdr! x y)` on the lists in [Figure
3.12](#Figure 3.12).

The `set/cdr!` operation is similar to `set/car!`. The only difference
is that the `cdr` pointer of the pair, rather than the `car` pointer, is
replaced. The effect of executing `(set/cdr! x y)` on the lists of
[Figure 3.12](#Figure 3.12) is shown in [Figure 3.15](#Figure 3.15).
Here the `cdr` pointer of `x` has been replaced by the pointer to
`(e f)`. Also, the list `(c d)`, which used to be the `cdr` of `x`, is
now detached from the structure.

`cons` builds new list structure by creating new pairs, while `set/car!`
and `set/cdr!` modify existing pairs. Indeed, we could implement `cons`
in terms of the two mutators, together with a procedure `get/new/pair`,
which returns a new pair that is not part of any existing list
structure. We obtain the new pair, set its `car` and `cdr` pointers to
the designated objects, and return the new pair as the result of the
`cons`.[^146]

::: scheme
(define (cons x y) (let ((new (get-new-pair))) (set-car! new x)
(set-cdr! new y) new))
:::

> **[]{#Exercise 3.12 label="Exercise 3.12"}Exercise 3.12:** The
> following procedure for appending lists was introduced in [Section
> 2.2.1](#Section 2.2.1):
>
> ::: scheme
> (define (append x y) (if (null? x) y (cons (car x) (append (cdr x)
> y))))
> :::
>
> `append` forms a new list by successively `cons`ing the elements of
> `x` onto `y`. The procedure `append!` is similar to `append`, but it
> is a mutator rather than a constructor. It appends the lists by
> splicing them together, modifying the final pair of `x` so that its
> `cdr` is now `y`. (It is an error to call `append!` with an empty
> `x`.)
>
> ::: scheme
> (define (append! x y) (set-cdr! (last-pair x) y) x)
> :::
>
> Here `last/pair` is a procedure that returns the last pair in its
> argument:
>
> ::: scheme
> (define (last-pair x) (if (null? (cdr x)) x (last-pair (cdr x))))
> :::
>
> Consider the interaction
>
> ::: scheme
> (define x (list 'a 'b)) (define y (list 'c 'd)) (define z (append x
> y)) z  *(a b c d)*  (cdr x)
>  $\color{SchemeDark}\langle$  *response*  $\color{SchemeDark}\rangle$
> (define w (append! x y)) w  *(a b c d)*  (cdr x)
>  $\color{SchemeDark}\langle$  *response*  $\color{SchemeDark}\rangle$
> :::
>
> What are the missing $\langle$*response*$\rangle$s? Draw
> box-and-pointer\
> diagrams to explain your answer.

> **[]{#Exercise 3.13 label="Exercise 3.13"}Exercise 3.13:** Consider
> the following `make/cycle` procedure, which uses the `last/pair`
> procedure defined in [Exercise 3.12](#Exercise 3.12):
>
> ::: scheme
> (define (make-cycle x) (set-cdr! (last-pair x) x) x)
> :::
>
> Draw a box-and-pointer diagram that shows the structure `z` created by
>
> ::: scheme
> (define z (make-cycle (list 'a 'b 'c)))
> :::
>
> What happens if we try to compute `(last/pair z)`?

> **[]{#Exercise 3.14 label="Exercise 3.14"}Exercise 3.14:** The
> following procedure is quite useful, although obscure:
>
> ::: scheme
> (define (mystery x) (define (loop x y) (if (null? x) y (let ((temp
> (cdr x))) (set-cdr! x y) (loop temp x)))) (loop x '()))
> :::
>
> `loop` uses the "temporary" variable `temp` to hold the old value of
> the `cdr` of `x`, since the `set/cdr!` on the next line destroys the
> `cdr`. Explain what `mystery` does in general. Suppose `v` is defined
> by `(define v (list ’a ’b ’c ’d))`. Draw the box-and-pointer diagram
> that represents the list to which `v` is bound. Suppose that we now
> evaluate `(define w (mystery v))`. Draw box-and-pointer diagrams that
> show the structures `v` and `w` after evaluating this expression. What
> would be printed as the values of `v` and `w`?

#### Sharing and identity {#sharing-and-identity .unnumbered}

We mentioned in [Section 3.1.3](#Section 3.1.3) the theoretical issues
of "sameness" and "change" raised by the introduction of assignment.
These issues arise in practice when individual pairs are *shared* among
different data objects. For example, consider the structure formed by

::: scheme
(define x (list 'a 'b)) (define z1 (cons x x))
:::

As shown in [Figure 3.16](#Figure 3.16), `z1` is a pair whose `car` and
`cdr` both point to the same pair `x`. This sharing of `x` by the `car`
and `cdr` of `z1` is a consequence of the straightforward way in which
`cons` is implemented. In general, using `cons` to construct lists will
result in an interlinked structure of pairs in which many individual
pairs are shared by many different structures.

In contrast to [Figure 3.16](#Figure 3.16), [Figure 3.17](#Figure 3.17)
shows the structure created by

::: scheme
(define z2 (cons (list 'a 'b) (list 'a 'b)))
:::

In this structure, the pairs in the two `(a b)` lists are distinct,
although the actual symbols are shared.[^147]

[]{#Figure 3.16 label="Figure 3.16"}

![image](fig/chap3/Fig3.16b.pdf){width="46mm"}

> **Figure 3.16:** The list `z1` formed by `(cons x x)`.

[]{#Figure 3.17 label="Figure 3.17"}

![image](fig/chap3/Fig3.17b.pdf){width="71mm"}

> **Figure 3.17:** The list `z2` formed by
> `(cons (list ’a ’b) (list ’a ’b))`.

When thought of as a list, `z1` and `z2` both represent "the same" list,
`((a b) a b)`. In general, sharing is completely undetectable if we
operate on lists using only `cons`, `car`, and `cdr`. However, if we
allow mutators on list structure, sharing becomes significant. As an
example of the difference that sharing can make, consider the following
procedure, which modifies the `car` of the structure to which it is
applied:

::: scheme
(define (set-to-wow! x) (set-car! (car x) 'wow) x)
:::

Even though `z1` and `z2` are "the same" structure, applying
`set/to/wow!` to them yields different results. With `z1`, altering the
`car` also changes the `cdr`, because in `z1` the `car` and the `cdr`
are the same pair. With `z2`, the `car` and `cdr` are distinct, so
`set/to/wow!` modifies only the `car`:

::: scheme
z1  *((a b) a b)*  (set-to-wow! z1)  *((wow b) wow b)*  z2  *((a b) a
b)*  (set-to-wow! z2)  *((wow b) a b)*
:::

One way to detect sharing in list structures is to use the predicate
`eq?`, which we introduced in [Section 2.3.1](#Section 2.3.1) as a way
to test whether two symbols are equal. More generally, `(eq? x y)` tests
whether `x` and `y` are the same object (that is, whether `x` and `y`
are equal as pointers). Thus, with `z1` and `z2` as defined in [Figure
3.16](#Figure 3.16) and [Figure 3.17](#Figure 3.17),
`(eq? (car z1) (cdr z1))` is true and `(eq? (car z2) (cdr z2))` is
false.

As will be seen in the following sections, we can exploit sharing to
greatly extend the repertoire of data structures that can be represented
by pairs. On the other hand, sharing can also be dangerous, since
modifications made to structures will also affect other structures that
happen to share the modified parts. The mutation operations `set/car!`
and `set/cdr!` should be used with care; unless we have a good
understanding of how our data objects are shared, mutation can have
unanticipated results.[^148]

> **[]{#Exercise 3.15 label="Exercise 3.15"}Exercise 3.15:** Draw
> box-and-pointer diagrams to explain the effect of `set/to/wow!` on the
> structures `z1` and `z2` above.

> **[]{#Exercise 3.16 label="Exercise 3.16"}Exercise 3.16:** Ben
> Bitdiddle decides to write a procedure to count the number of pairs in
> any list structure. "It's easy," he reasons. "The number of pairs in
> any structure is the number in the `car` plus the number in the `cdr`
> plus one more to count the current pair." So Ben writes the following
> procedure:
>
> ::: scheme
> (define (count-pairs x) (if (not (pair? x)) 0 (+ (count-pairs (car x))
> (count-pairs (cdr x)) 1)))
> :::
>
> Show that this procedure is not correct. In particular, draw
> box-and-pointer diagrams representing list structures made up of
> exactly three pairs for which Ben's procedure would return 3; return
> 4; return 7; never return at all.

> **[]{#Exercise 3.17 label="Exercise 3.17"}Exercise 3.17:** Devise a
> correct version of the `count/pairs` procedure of [Exercise
> 3.16](#Exercise 3.16) that returns the number of distinct pairs in any
> structure. (Hint: Traverse the structure, maintaining an auxiliary
> data structure that is used to keep track of which pairs have already
> been counted.)

> **[]{#Exercise 3.18 label="Exercise 3.18"}Exercise 3.18:** Write a
> procedure that examines a list and determines whether it contains a
> cycle, that is, whether a program that tried to find the end of the
> list by taking successive `cdr`s would go into an infinite loop.
> [Exercise 3.13](#Exercise 3.13) constructed such lists.

> **[]{#Exercise 3.19 label="Exercise 3.19"}Exercise 3.19:** Redo
> [Exercise 3.18](#Exercise 3.18) using an algorithm that takes only a
> constant amount of space. (This requires a very clever idea.)

#### Mutation is just assignment {#mutation-is-just-assignment .unnumbered}

When we introduced compound data, we observed in [Section
2.1.3](#Section 2.1.3) that pairs can be represented purely in terms of
procedures:

::: scheme
(define (cons x y) (define (dispatch m) (cond ((eq? m 'car) x) ((eq? m
'cdr) y) (else (error \"Undefined operation: CONS\" m)))) dispatch)
(define (car z) (z 'car)) (define (cdr z) (z 'cdr))
:::

The same observation is true for mutable data. We can implement mutable
data objects as procedures using assignment and local state. For
instance, we can extend the above pair implementation to handle
`set/car!` and `set/cdr!` in a manner analogous to the way we
implemented bank accounts using `make/account` in [Section
3.1.1](#Section 3.1.1):

::: scheme
(define (cons x y) (define (set-x! v) (set! x v)) (define (set-y! v)
(set! y v)) (define (dispatch m) (cond ((eq? m 'car) x) ((eq? m 'cdr) y)
((eq? m 'set-car!) set-x!) ((eq? m 'set-cdr!) set-y!) (else (error
\"Undefined operation: CONS\" m)))) dispatch) (define (car z) (z 'car))
(define (cdr z) (z 'cdr)) (define (set-car! z new-value) ((z 'set-car!)
new-value) z) (define (set-cdr! z new-value) ((z 'set-cdr!) new-value)
z)
:::

Assignment is all that is needed, theoretically, to account for the
behavior of mutable data. As soon as we admit `set!` to our language, we
raise all the issues, not only of assignment, but of mutable data in
general.[^149]

> **[]{#Exercise 3.20 label="Exercise 3.20"}Exercise 3.20:** Draw
> environment diagrams to illustrate the evaluation of the sequence of
> expressions
>
> ::: scheme
> (define x (cons 1 2)) (define z (cons x x)) (set-car! (cdr z) 17) (car
> x)  *17*
> :::
>
> using the procedural implementation of pairs given above. (Compare
> [Exercise 3.11](#Exercise 3.11).)

### Representing Queues {#Section 3.3.2}

The mutators `set/car!` and `set/cdr!` enable us to use pairs to
construct data structures that cannot be built with `cons`, `car`, and
`cdr` alone. This section shows how to use pairs to represent a data
structure called a queue. [Section 3.3.3](#Section 3.3.3) will show how
to represent data structures called tables.

A *queue* is a sequence in which items are inserted at one end (called
the *rear* of the queue) and deleted from the other end (the *front*).
[Figure 3.18](#Figure 3.18) shows an initially empty queue in which the
items `a` and `b` are inserted. Then `a` is removed, `c` and `d` are
inserted, and `b` is removed. Because items are always removed in the
order in which they are inserted, a queue is sometimes called a *FIFO*
(first in, first out) buffer.

[]{#Figure 3.18 label="Figure 3.18"}

![image](fig/chap3/Fig3.18a.pdf){width="70mm"}

**Figure 3.18:** Queue operations.

In terms of data abstraction, we can regard a queue as defined by the
following set of operations:

-   a constructor: `(make/queue)` returns an empty queue (a queue
    containing no items).

-   two selectors:

    `(empty/queue? `$\langle$*`queue`*$\rangle$`)` tests if the queue is
    empty.

    `(front/queue `$\langle$*`queue`*$\rangle$`)` returns the object at
    the front of the queue, signaling an error if the queue is empty; it
    does not modify the queue.

-   two mutators:

    `(insert/queue! `$\langle$*`queue`*$\rangle$` `$\langle$*`item`*$\rangle$`)`
    inserts the item at the rear of the queue and returns the modified
    queue as its value.

    `(delete/queue! `$\langle$*`queue`*$\rangle$`)` removes the item at
    the front of the queue and returns the modified queue as its value,
    signaling an error if the queue is empty before the deletion.

Because a queue is a sequence of items, we could certainly represent it
as an ordinary list; the front of the queue would be the `car` of the
list, inserting an item in the queue would amount to appending a new
element at the end of the list, and deleting an item from the queue
would just be taking the `cdr` of the list. However, this representation
is inefficient, because in order to insert an item we must scan the list
until we reach the end. Since the only method we have for scanning a
list is by successive `cdr` operations, this scanning requires
$\Theta(n)$ steps for a list of $n$ items. A simple modification to the
list representation overcomes this disadvantage by allowing the queue
operations to be implemented so that they require $\Theta$(1) steps;
that is, so that the number of steps needed is independent of the length
of the queue.

The difficulty with the list representation arises from the need to scan
to find the end of the list. The reason we need to scan is that,
although the standard way of representing a list as a chain of pairs
readily provides us with a pointer to the beginning of the list, it
gives us no easily accessible pointer to the end. The modification that
avoids the drawback is to represent the queue as a list, together with
an additional pointer that indicates the final pair in the list. That
way, when we go to insert an item, we can consult the rear pointer and
so avoid scanning the list.

A queue is represented, then, as a pair of pointers, `front/ptr` and
`rear/ptr`, which indicate, respectively, the first and last pairs in an
ordinary list. Since we would like the queue to be an identifiable
object, we can use `cons` to combine the two pointers. Thus, the queue
itself will be the `cons` of the two pointers. [Figure
3.19](#Figure 3.19) illustrates this representation.

[]{#Figure 3.19 label="Figure 3.19"}

![image](fig/chap3/Fig3.19b.pdf){width="69mm"}

> **Figure 3.19:** Implementation of a queue as a list with front and
> rear pointers.

To define the queue operations we use the following procedures, which
enable us to select and to modify the front and rear pointers of a
queue:

::: scheme
(define (front-ptr queue) (car queue)) (define (rear-ptr queue) (cdr
queue)) (define (set-front-ptr! queue item) (set-car! queue item))
(define (set-rear-ptr! queue item) (set-cdr! queue item))
:::

Now we can implement the actual queue operations. We will consider a
queue to be empty if its front pointer is the empty list:

::: scheme
(define (empty-queue? queue) (null? (front-ptr queue)))
:::

The `make/queue` constructor returns, as an initially empty queue, a
pair whose `car` and `cdr` are both the empty list:

::: scheme
(define (make-queue) (cons '() '()))
:::

To select the item at the front of the queue, we return the `car` of the
pair indicated by the front pointer:

::: scheme
(define (front-queue queue) (if (empty-queue? queue) (error \"FRONT
called with an empty queue\" queue) (car (front-ptr queue))))
:::

[]{#Figure 3.20 label="Figure 3.20"}

![image](fig/chap3/Fig3.20b.pdf){width="88mm"}

> **Figure 3.20:** Result of using `(insert/queue! q ’d)` on the queue
> of [Figure 3.19](#Figure 3.19).

To insert an item in a queue, we follow the method whose result is
indicated in [Figure 3.20](#Figure 3.20). We first create a new pair
whose `car` is the item to be inserted and whose `cdr` is the empty
list. If the queue was initially empty, we set the front and rear
pointers of the queue to this new pair. Otherwise, we modify the final
pair in the queue to point to the new pair, and also set the rear
pointer to the new pair.

::: scheme
(define (insert-queue! queue item) (let ((new-pair (cons item '())))
(cond ((empty-queue? queue) (set-front-ptr! queue new-pair)
(set-rear-ptr! queue new-pair) queue) (else (set-cdr! (rear-ptr queue)
new-pair) (set-rear-ptr! queue new-pair) queue))))
:::

[]{#Figure 3.21 label="Figure 3.21"}

![image](fig/chap3/Fig3.21b.pdf){width="88mm"}

> **Figure 3.21:** Result of using `(delete/queue! q)` on the queue of
> [Figure 3.20](#Figure 3.20).

To delete the item at the front of the queue, we merely modify the front
pointer so that it now points at the second item in the queue, which can
be found by following the `cdr` pointer of the first item (see [Figure
3.21](#Figure 3.21)):[^150]

::: scheme
(define (delete-queue! queue) (cond ((empty-queue? queue) (error
\"DELETE! called with an empty queue\" queue)) (else (set-front-ptr!
queue (cdr (front-ptr queue))) queue)))
:::

> **[]{#Exercise 3.21 label="Exercise 3.21"}Exercise 3.21:** Ben
> Bitdiddle decides to test the queue implementation described above. He
> types in the procedures to the Lisp interpreter and proceeds to try
> them out:
>
> ::: scheme
> (define q1 (make-queue)) (insert-queue! q1 'a)  *((a) a)*
> (insert-queue! q1 'b)  *((a b) b)*  (delete-queue! q1)  *((b) b)*
> (delete-queue! q1)  *(() b)*
> :::
>
> "It's all wrong!" he complains. "The interpreter's response shows that
> the last item is inserted into the queue twice. And when I delete both
> items, the second `b` is still there, so the queue isn't empty, even
> though it's supposed to be." Eva Lu Ator suggests that Ben has
> misunderstood what is happening. "It's not that the items are going
> into the queue twice," she explains. "It's just that the standard Lisp
> printer doesn't know how to make sense of the queue representation. If
> you want to see the queue printed correctly, you'll have to define
> your own print procedure for queues." Explain what Eva Lu is talking
> about. In particular, show why Ben's examples produce the printed
> results that they do. Define a procedure `print/queue` that takes a
> queue as input and prints the sequence of items in the queue.

> **[]{#Exercise 3.22 label="Exercise 3.22"}Exercise 3.22:** Instead of
> representing a queue as a pair of pointers, we can build a queue as a
> procedure with local state. The local state will consist of pointers
> to the beginning and the end of an ordinary list. Thus, the
> `make/queue` procedure will have the form
>
> ::: scheme
> (define (make-queue) (let ((front-ptr  $\dots$  ) (rear-ptr  $\dots$
> ))  $\color{SchemeDark}\langle$  *definitions of internal
> procedures*  $\color{SchemeDark}\rangle$  (define (dispatch m)
>  $\dots$ ) dispatch))
> :::
>
> Complete the definition of `make/queue` and provide implementations of
> the queue operations using this representation.

> **[]{#Exercise 3.23 label="Exercise 3.23"}Exercise 3.23:** A *deque*
> ("double-ended queue") is a sequence in which items can be inserted
> and deleted at either the front or the rear. Operations on deques are
> the constructor `make/deque`, the predicate `empty/deque?`, selectors
> `front/deque` and `rear/deque`, mutators `front/insert/deque!`,
> `rear/insert/deque!`, `front/delete/deque!`, and `rear/delete/deque!`.
> Show how to represent deques using pairs, and give implementations of
> the operations.[^151] All operations should be accomplished in
> $\Theta$(1) steps.

### Representing Tables {#Section 3.3.3}

When we studied various ways of representing sets in [Chapter
2](#Chapter 2), we mentioned in [Section 2.3.3](#Section 2.3.3) the task
of maintaining a table of records indexed by identifying keys. In the
implementation of data-directed programming in [Section
2.4.3](#Section 2.4.3), we made extensive use of two-dimensional tables,
in which information is stored and retrieved using two keys. Here we see
how to build tables as mutable list structures.

[]{#Figure 3.22 label="Figure 3.22"}

![image](fig/chap3/Fig3.22c.pdf){width="81mm"}

**Figure 3.22:** A table represented as a headed list.

We first consider a one-dimensional table, in which each value is stored
under a single key. We implement the table as a list of records, each of
which is implemented as a pair consisting of a key and the associated
value. The records are glued together to form a list by pairs whose
`car`s point to successive records. These gluing pairs are called the
*backbone* of the table. In order to have a place that we can change
when we add a new record to the table, we build the table as a *headed
list*. A headed list has a special backbone pair at the beginning, which
holds a dummy "record"---in this case the arbitrarily chosen symbol
`*table*`. [Figure 3.22](#Figure 3.22) shows the box-and-pointer diagram
for the table

::: scheme
a: 1 b: 2 c: 3
:::

To extract information from a table we use the `lookup` procedure, which
takes a key as argument and returns the associated value (or false if
there is no value stored under that key). `lookup` is defined in terms
of the `assoc` operation, which expects a key and a list of records as
arguments. Note that `assoc` never sees the dummy record. `assoc`
returns the record that has the given key as its `car`.[^152] `lookup`
then checks to see that the resulting record returned by `assoc` is not
false, and returns the value (the `cdr`) of the record.

::: scheme
(define (lookup key table) (let ((record (assoc key (cdr table)))) (if
record (cdr record) false))) (define (assoc key records) (cond ((null?
records) false) ((equal? key (caar records)) (car records)) (else (assoc
key (cdr records)))))
:::

To insert a value in a table under a specified key, we first use `assoc`
to see if there is already a record in the table with this key. If not,
we form a new record by `cons`ing the key with the value, and insert
this at the head of the table's list of records, after the dummy record.
If there already is a record with this key, we set the `cdr` of this
record to the designated new value. The header of the table provides us
with a fixed location to modify in order to insert the new record.[^153]

::: scheme
(define (insert! key value table) (let ((record (assoc key (cdr
table)))) (if record (set-cdr! record value) (set-cdr! table (cons (cons
key value) (cdr table))))) 'ok)
:::

To construct a new table, we simply create a list containing the symbol
`*table*`:

::: scheme
(define (make-table) (list '\*table\*))
:::

#### Two-dimensional tables {#two-dimensional-tables .unnumbered}

In a two-dimensional table, each value is indexed by two keys. We can
construct such a table as a one-dimensional table in which each key
identifies a subtable. [Figure 3.23](#Figure 3.23) shows the
box-and-pointer diagram for the table

math: +: 43 letters: a: 97 -: 45 b: 98 \*: 42

which has two subtables. (The subtables don't need a special header
symbol, since the key that identifies the subtable serves this purpose.)

When we look up an item, we use the first key to identify the correct
subtable. Then we use the second key to identify the record within the
subtable.

::: scheme
(define (lookup key-1 key-2 table) (let ((subtable (assoc key-1 (cdr
table)))) (if subtable (let ((record (assoc key-2 (cdr subtable)))) (if
record (cdr record) false)) false)))
:::

[]{#Figure 3.23 label="Figure 3.23"}

![image](fig/chap3/Fig3.23a.pdf){width="103mm"}

**Figure 3.23:** A two-dimensional table.

To insert a new item under a pair of keys, we use `assoc` to see if
there is a subtable stored under the first key. If not, we build a new
subtable containing the single record (`key/2`, `value`) and insert it
into the table under the first key. If a subtable already exists for the
first key, we insert the new record into this subtable, using the
insertion method for one-dimensional tables described above:

::: scheme
(define (insert! key-1 key-2 value table) (let ((subtable (assoc key-1
(cdr table)))) (if subtable (let ((record (assoc key-2 (cdr subtable))))
(if record (set-cdr! record value) (set-cdr! subtable (cons (cons key-2
value) (cdr subtable))))) (set-cdr! table (cons (list key-1 (cons key-2
value)) (cdr table))))) 'ok)
:::

#### Creating local tables {#creating-local-tables .unnumbered}

The `lookup` and `insert!` operations defined above take the table as an
argument. This enables us to use programs that access more than one
table. Another way to deal with multiple tables is to have separate
`lookup` and `insert!` procedures for each table. We can do this by
representing a table procedurally, as an object that maintains an
internal table as part of its local state. When sent an appropriate
message, this "table object" supplies the procedure with which to
operate on the internal table. Here is a generator for two-dimensional
tables represented in this fashion:

::: scheme
(define (make-table) (let ((local-table (list '\*table\*))) (define
(lookup key-1 key-2) (let ((subtable (assoc key-1 (cdr local-table))))
(if subtable (let ((record (assoc key-2 (cdr subtable)))) (if record
(cdr record) false)) false))) (define (insert! key-1 key-2 value) (let
((subtable (assoc key-1 (cdr local-table)))) (if subtable (let ((record
(assoc key-2 (cdr subtable)))) (if record (set-cdr! record value)
(set-cdr! subtable (cons (cons key-2 value) (cdr subtable))))) (set-cdr!
local-table (cons (list key-1 (cons key-2 value)) (cdr local-table)))))
'ok) (define (dispatch m) (cond ((eq? m 'lookup-proc) lookup) ((eq? m
'insert-proc!) insert!) (else (error \"Unknown operation: TABLE\" m))))
dispatch))
:::

Using `make/table`, we could implement the `get` and `put` operations
used in [Section 2.4.3](#Section 2.4.3) for data-directed programming,
as follows:

::: scheme
(define operation-table (make-table)) (define get (operation-table
'lookup-proc)) (define put (operation-table 'insert-proc!))
:::

`get` takes as arguments two keys, and `put` takes as arguments two keys
and a value. Both operations access the same local table, which is
encapsulated within the object created by the call to `make/table`.

> **[]{#Exercise 3.24 label="Exercise 3.24"}Exercise 3.24:** In the
> table implementations above, the keys are tested for equality using
> `equal?` (called by `assoc`). This is not always the appropriate test.
> For instance, we might have a table with numeric keys in which we
> don't need an exact match to the number we're looking up, but only a
> number within some tolerance of it. Design a table constructor
> `make/table` that takes as an argument a `same/key?` procedure that
> will be used to test "equality" of keys. `make/table` should return a
> `dispatch` procedure that can be used to access appropriate `lookup`
> and `insert!` procedures for a local table.

> **[]{#Exercise 3.25 label="Exercise 3.25"}Exercise 3.25:**
> Generalizing one- and two-dimensional tables, show how to implement a
> table in which values are stored under an arbitrary number of keys and
> different values may be stored under different numbers of keys. The
> `lookup` and `insert!` procedures should take as input a list of keys
> used to access the table.

> **[]{#Exercise 3.26 label="Exercise 3.26"}Exercise 3.26:** To search a
> table as implemented above, one needs to scan through the list of
> records. This is basically the unordered list representation of
> [Section 2.3.3](#Section 2.3.3). For large tables, it may be more
> efficient to structure the table in a different manner. Describe a
> table implementation where the (key, value) records are organized
> using a binary tree, assuming that keys can be ordered in some way
> (e.g., numerically or alphabetically). (Compare [Exercise
> 2.66](#Exercise 2.66) of [Chapter 2](#Chapter 2).)

> **[]{#Exercise 3.27 label="Exercise 3.27"}Exercise 3.27:**
> *Memoization* (also called *tabulation*) is a technique that enables a
> procedure to record, in a local table, values that have previously
> been computed. This technique can make a vast difference in the
> performance of a program. A memoized procedure maintains a table in
> which values of previous calls are stored using as keys the arguments
> that produced the values. When the memoized procedure is asked to
> compute a value, it first checks the table to see if the value is
> already there and, if so, just returns that value. Otherwise, it
> computes the new value in the ordinary way and stores this in the
> table. As an example of memoization, recall from [Section
> 1.2.2](#Section 1.2.2) the exponential process for computing Fibonacci
> numbers:
>
> ::: scheme
> (define (fib n) (cond ((= n 0) 0) ((= n 1) 1) (else (+ (fib (- n 1))
> (fib (- n 2))))))
> :::
>
> The memoized version of the same procedure is
>
> ::: scheme
> (define memo-fib (memoize (lambda (n) (cond ((= n 0) 0) ((= n 1) 1)
> (else (+ (memo-fib (- n 1)) (memo-fib (- n 2))))))))
> :::
>
> where the memoizer is defined as
>
> ::: scheme
> (define (memoize f) (let ((table (make-table))) (lambda (x) (let
> ((previously-computed-result (lookup x table))) (or
> previously-computed-result (let ((result (f x))) (insert! x result
> table) result))))))
> :::
>
> Draw an environment diagram to analyze the computation of
> `(memo/fib 3)`. Explain why `memo/fib` computes the $n^{\mathrm{th}}$
> Fibonacci number in a number of steps proportional to $n$. Would the
> scheme still work if we had simply defined `memo/fib` to be
> `(memoize fib)`?

### A Simulator for Digital Circuits {#Section 3.3.4}

Designing complex digital systems, such as computers, is an important
engineering activity. Digital systems are constructed by interconnecting
simple elements. Although the behavior of these individual elements is
simple, networks of them can have very complex behavior. Computer
simulation of proposed circuit designs is an important tool used by
digital systems engineers. In this section we design a system for
performing digital logic simulations. This system typifies a kind of
program called an *event-driven simulation*, in which actions ("events")
trigger further events that happen at a later time, which in turn
trigger more events, and so on.

Our computational model of a circuit will be composed of objects that
correspond to the elementary components from which the circuit is
constructed. There are *wires*, which carry *digital signals*. A digital
signal may at any moment have only one of two possible values, 0 and 1.
There are also various types of digital *function boxes*, which connect
wires carrying input signals to other output wires. Such boxes produce
output signals computed from their input signals. The output signal is
delayed by a time that depends on the type of the function box. For
example, an *inverter* is a primitive function box that inverts its
input. If the input signal to an inverter changes to 0, then one
inverter-delay later the inverter will change its output signal to 1. If
the input signal to an inverter changes to 1, then one inverter-delay
later the inverter will change its output signal to 0. We draw an
inverter symbolically as in [Figure 3.24](#Figure 3.24). An *and-gate*,
also shown in [Figure 3.24](#Figure 3.24), is a primitive function box
with two inputs and one output. It drives its output signal to a value
that is the *logical and* of the inputs. That is, if both of its input
signals become 1, then one and-gate-delay time later the and-gate will
force its output signal to be 1; otherwise the output will be 0. An
*or-gate* is a similar two-input primitive function box that drives its
output signal to a value that is the *logical or* of the inputs. That
is, the output will become 1 if at least one of the input signals is 1;
otherwise the output will become 0.

[]{#Figure 3.24 label="Figure 3.24"}

![image](fig/chap3/Fig3.24b.pdf){width="74mm"}

**Figure 3.24:** Primitive functions in the digital logic simulator.

We can connect primitive functions together to construct more complex
functions. To accomplish this we wire the outputs of some function boxes
to the inputs of other function boxes. For example, the *half-adder*
circuit shown in [Figure 3.25](#Figure 3.25) consists of an or-gate, two
and-gates, and an inverter. It takes two input signals, A and B, and has
two output signals, S and C. S will become 1 whenever precisely one of A
and B is 1, and C will become 1 whenever A and B are both 1. We can see
from the figure that, because of the delays involved, the outputs may be
generated at different times. Many of the difficulties in the design of
digital circuits arise from this fact.

[]{#Figure 3.25 label="Figure 3.25"}

![image](fig/chap3/Fig3.25c.pdf){width="72mm"}

**Figure 3.25:** A half-adder circuit.

We will now build a program for modeling the digital logic circuits we
wish to study. The program will construct computational objects modeling
the wires, which will "hold" the signals. Function boxes will be modeled
by procedures that enforce the correct relationships among the signals.

One basic element of our simulation will be a procedure `make/wire`,
which constructs wires. For example, we can construct six wires as
follows:

::: scheme
(define a (make-wire)) (define b (make-wire)) (define c (make-wire))
(define d (make-wire)) (define e (make-wire)) (define s (make-wire))
:::

We attach a function box to a set of wires by calling a procedure that
constructs that kind of box. The arguments to the constructor procedure
are the wires to be attached to the box. For example, given that we can
construct and-gates, or-gates, and inverters, we can wire together the
half-adder shown in [Figure 3.25](#Figure 3.25):

::: scheme
(or-gate a b d)  *ok*  (and-gate a b c)  *ok*  (inverter c e)  *ok*
(and-gate d e s)  *ok*
:::

Better yet, we can explicitly name this operation by defining a
procedure `half/adder` that constructs this circuit, given the four
external wires to be attached to the half-adder:

::: scheme
(define (half-adder a b s c) (let ((d (make-wire)) (e (make-wire)))
(or-gate a b d) (and-gate a b c) (inverter c e) (and-gate d e s) 'ok))
:::

The advantage of making this definition is that we can use `half/adder`
itself as a building block in creating more complex circuits. [Figure
3.26](#Figure 3.26), for example, shows a *full-adder* composed of two
half-adders and an or-gate.[^154] We can construct a full-adder as
follows:

::: scheme
(define (full-adder a b c-in sum c-out) (let ((s (make-wire)) (c1
(make-wire)) (c2 (make-wire))) (half-adder b c-in s c1) (half-adder a s
sum c2) (or-gate c1 c2 c-out) 'ok))
:::

[]{#Figure 3.26 label="Figure 3.26"}

![image](fig/chap3/Fig3.26a.pdf){width="74mm"}

**Figure 3.26:** A full-adder circuit.

Having defined `full/adder` as a procedure, we can now use it as a
building block for creating still more complex circuits. (For example,
see [Exercise 3.30](#Exercise 3.30).)

In essence, our simulator provides us with the tools to construct a
language of circuits. If we adopt the general perspective on languages
with which we approached the study of Lisp in [Section
1.1](#Section 1.1), we can say that the primitive function boxes form
the primitive elements of the language, that wiring boxes together
provides a means of combination, and that specifying wiring patterns as
procedures serves as a means of abstraction.

#### Primitive function boxes {#primitive-function-boxes .unnumbered}

The primitive function boxes implement the "forces" by which a change in
the signal on one wire influences the signals on other wires. To build
function boxes, we use the following operations on wires:

-   `(get/signal`$\;\;\langle\kern0.06em\hbox{\ttfamily\slshape wire}\kern0.08em\rangle$`)`

    returns the current value of the signal on the wire.

-   `(set/signal!`$\;\;\langle\kern0.08em\hbox{\ttfamily\slshape wire}\kern0.08em\rangle\;\;\langle\kern0.08em\hbox{\ttfamily\slshape new value}\kern0.08em\rangle$`)`

    changes the value of the signal on the wire to the new value.

-   `(add/action!`$\;\;\langle\kern0.08em\hbox{\ttfamily\slshape wire}\kern0.08em\rangle\;\;\langle\kern0.08em\hbox{\ttfamily\slshape procedure of no arguments}\kern0.02em\rangle$`)`

    asserts that the designated procedure should be run whenever the
    signal on the wire changes value. Such procedures are the vehicles
    by which changes in the signal value on the wire are communicated to
    other wires.

In addition, we will make use of a procedure `after/delay` that takes a
time delay and a procedure to be run and executes the given procedure
after the given delay.

Using these procedures, we can define the primitive digital logic
functions. To connect an input to an output through an inverter, we use
`add/action!` to associate with the input wire a procedure that will be
run whenever the signal on the input wire changes value. The procedure
computes the `logical/not` of the input signal, and then, after one
`inverter/delay`, sets the output signal to be this new value:

::: scheme
(define (inverter input output) (define (invert-input) (let ((new-value
(logical-not (get-signal input)))) (after-delay inverter-delay (lambda
() (set-signal! output new-value))))) (add-action! input invert-input)
'ok) (define (logical-not s) (cond ((= s 0) 1) ((= s 1) 0) (else (error
\"Invalid signal\" s))))
:::

An and-gate is a little more complex. The action procedure must be run
if either of the inputs to the gate changes. It computes the
`logical/and` (using a procedure analogous to `logical/not`) of the
values of the signals on the input wires and sets up a change to the new
value to occur on the output wire after one `and/gate/delay`.

::: scheme
(define (and-gate a1 a2 output) (define (and-action-procedure) (let
((new-value (logical-and (get-signal a1) (get-signal a2)))) (after-delay
and-gate-delay (lambda () (set-signal! output new-value)))))
(add-action! a1 and-action-procedure) (add-action! a2
and-action-procedure) 'ok)
:::

> **[]{#Exercise 3.28 label="Exercise 3.28"}Exercise 3.28:** Define an
> or-gate as a primitive function box. Your `or/gate` constructor should
> be similar to `and/gate`.

> **[]{#Exercise 3.29 label="Exercise 3.29"}Exercise 3.29:** Another way
> to construct an or-gate is as a compound digital logic device, built
> from and-gates and inverters. Define a procedure `or/gate` that
> accomplishes this. What is the delay time of the or-gate in terms of
> `and/gate/delay` and `inverter/delay`?

> **[]{#Exercise 3.30 label="Exercise 3.30"}Exercise 3.30:** [Figure
> 3.27](#Figure 3.27) shows a *ripple-carry adder* formed by stringing
> together $n$ full-adders. This is the simplest form of parallel adder
> for adding two $n$-bit binary numbers. The inputs $A_1$, $A_2$, $A_3$,
> $\dots$, $A_n$ and $B_1$, $B_2$, $B_3$, $\dots$, $B_n$ are the two
> binary numbers to be added (each $A_k$ and $B_k$ is a 0 or a 1). The
> circuit generates $S_1$, $S_2$, $S_3$, $\dots$, $S_n$, the $n$ bits of
> the sum, and $C$, the carry from the addition. Write a procedure
> `ripple/carry/adder` that generates this circuit. The procedure should
> take as arguments three lists of $n$ wires each---the $A_k$, the
> $B_k$, and the $S_k$---and also another wire $C$. The major drawback
> of the ripple-carry adder is the need to wait for the carry signals to
> propagate. What is the delay needed to obtain the complete output from
> an $n$-bit ripple-carry adder, expressed in terms of the delays for
> and-gates, or-gates, and inverters?

[]{#Figure 3.27 label="Figure 3.27"}

![image](fig/chap3/Fig3.27a.pdf){width="96mm"}

**Figure 3.27:** A ripple-carry adder for $n$-bit numbers.

#### Representing wires {#representing-wires .unnumbered}

A wire in our simulation will be a computational object with two local
state variables: a `signal/value` (initially taken to be 0) and a
collection of `action/procedures` to be run when the signal changes
value. We implement the wire, using message-passing style, as a
collection of local procedures together with a `dispatch` procedure that
selects the appropriate local operation, just as we did with the simple
bank-account object in [Section 3.1.1](#Section 3.1.1):

::: scheme
(define (make-wire) (let ((signal-value 0) (action-procedures '()))
(define (set-my-signal! new-value) (if (not (= signal-value new-value))
(begin (set! signal-value new-value) (call-each action-procedures))
'done)) (define (accept-action-procedure! proc) (set! action-procedures
(cons proc action-procedures)) (proc)) (define (dispatch m) (cond ((eq?
m 'get-signal) signal-value) ((eq? m 'set-signal!) set-my-signal!) ((eq?
m 'add-action!) accept-action-procedure!) (else (error \"Unknown
operation: WIRE\" m)))) dispatch))
:::

The local procedure `set/my/signal!` tests whether the new signal value
changes the signal on the wire. If so, it runs each of the action
procedures, using the following procedure `call/each`, which calls each
of the items in a list of no-argument procedures:

::: scheme
(define (call-each procedures) (if (null? procedures) 'done (begin ((car
procedures)) (call-each (cdr procedures)))))
:::

The local procedure `accept/action/procedure!` adds the given procedure
to the list of procedures to be run, and then runs the new procedure
once. (See [Exercise 3.31](#Exercise 3.31).)

With the local `dispatch` procedure set up as specified, we can provide
the following procedures to access the local operations on wires:[^155]

::: scheme
(define (get-signal wire) (wire 'get-signal)) (define (set-signal! wire
new-value) ((wire 'set-signal!) new-value)) (define (add-action! wire
action-procedure) ((wire 'add-action!) action-procedure))
:::

Wires, which have time-varying signals and may be incrementally attached
to devices, are typical of mutable objects. We have modeled them as
procedures with local state variables that are modified by assignment.
When a new wire is created, a new set of state variables is allocated
(by the `let` expression in `make/wire`) and a new `dispatch` procedure
is constructed and returned, capturing the environment with the new
state variables.

The wires are shared among the various devices that have been connected
to them. Thus, a change made by an interaction with one device will
affect all the other devices attached to the wire. The wire communicates
the change to its neighbors by calling the action procedures provided to
it when the connections were established.

#### The agenda {#the-agenda .unnumbered}

The only thing needed to complete the simulator is `after/delay`. The
idea here is that we maintain a data structure, called an *agenda*, that
contains a schedule of things to do. The following operations are
defined for agendas:

-   `(make/agenda)` returns a new empty agenda.

-   `(empty/agenda?`$\;\;\langle\kern0.08em\hbox{\ttfamily\slshape agenda}\kern0.06em\rangle\hbox{\tt)}$
    is true if the specified agenda is empty.

-   `(first/agenda/item`$\;\;\langle\kern0.08em\hbox{\ttfamily\slshape agenda}\kern0.06em\rangle\hbox{\tt)}$
    returns the first item on the agenda.

-   `(remove/first/agenda/item!`$\;\langle\kern0.08em\hbox{\ttfamily\slshape agenda}\kern0.06em\rangle\hbox{\tt)}$
    modifies the agenda by removing the first item.

-   `(add/to/agenda!`$\;\;\langle\kern0.03em\hbox{\ttfamily\slshape time}\kern0.06em\rangle\;\;\langle\kern0.08em\hbox{\ttfamily\slshape action}\kern0.06em\rangle\;\;\langle\kern0.08em\hbox{\ttfamily\slshape agenda}\kern0.06em\rangle\hbox{\tt)}$
    modifies the agenda by adding the given action procedure to be run
    at the specified time.

-   `(current/time`$\;\;\langle\kern0.08em\hbox{\ttfamily\slshape agenda}\kern0.04em\rangle\hbox{\tt)}$
    returns the current simulation time.

The particular agenda that we use is denoted by `the/agenda`. The
procedure `after/delay` adds new elements to `the/agenda`:

::: scheme
(define (after-delay delay action) (add-to-agenda! (+ delay
(current-time the-agenda)) action the-agenda))
:::

The simulation is driven by the procedure `propagate`, which operates on
`the/agenda`, executing each procedure on the agenda in sequence. In
general, as the simulation runs, new items will be added to the agenda,
and `propagate` will continue the simulation as long as there are items
on the agenda:

::: scheme
(define (propagate) (if (empty-agenda? the-agenda) 'done (let
((first-item (first-agenda-item the-agenda))) (first-item)
(remove-first-agenda-item! the-agenda) (propagate))))
:::

#### A sample simulation {#a-sample-simulation .unnumbered}

The following procedure, which places a "probe" on a wire, shows the
simulator in action. The probe tells the wire that, whenever its signal
changes value, it should print the new signal value, together with the
current time and a name that identifies the wire:

::: scheme
(define (probe name wire) (add-action! wire (lambda () (newline)
(display name) (display \" \") (display (current-time the-agenda))
(display \" New-value = \") (display (get-signal wire)))))
:::

We begin by initializing the agenda and specifying delays for the
primitive function boxes:

::: scheme
(define the-agenda (make-agenda)) (define inverter-delay 2) (define
and-gate-delay 3) (define or-gate-delay 5)
:::

Now we define four wires, placing probes on two of them:

::: scheme
(define input-1 (make-wire)) (define input-2 (make-wire)) (define sum
(make-wire)) (define carry (make-wire))

(probe 'sum sum)  *sum 0 New-value = 0*

(probe 'carry carry)  *carry 0 New-value = 0*
:::

Next we connect the wires in a half-adder circuit (as in [Figure
3.25](#Figure 3.25)), set the signal on `input/1` to 1, and run the
simulation:

::: scheme
(half-adder input-1 input-2 sum carry)  *ok*
:::

::: scheme
(set-signal! input-1 1)  *done*
:::

::: scheme
(propagate)  *sum 8 New-value = 1*   *done*
:::

The `sum` signal changes to 1 at time 8. We are now eight time units
from the beginning of the simulation. At this point, we can set the
signal on `input/2` to 1 and allow the values to propagate:

::: scheme
(set-signal! input-2 1)  *done*
:::

::: scheme
(propagate)  *carry 11 New-value = 1*   *sum 16 New-value = 0*   *done*
:::

The `carry` changes to 1 at time 11 and the `sum` changes to 0 at time
16.

> **[]{#Exercise 3.31 label="Exercise 3.31"}Exercise 3.31:** The
> internal procedure `accept/action/procedure!` defined in `make/wire`
> specifies that when a new action procedure is added to a wire, the
> procedure is immediately run. Explain why this initialization is
> necessary. In particular, trace through the half-adder example in the
> paragraphs above and say how the system's response would differ if we
> had defined `accept/action/procedure!` as
>
> ::: scheme
> (define (accept-action-procedure! proc) (set! action-procedures (cons
> proc action-procedures)))
> :::

#### Implementing the agenda {#implementing-the-agenda .unnumbered}

Finally, we give details of the agenda data structure, which holds the
procedures that are scheduled for future execution.

The agenda is made up of *time segments*. Each time segment is a pair
consisting of a number (the time) and a queue (see [Exercise
3.32](#Exercise 3.32)) that holds the procedures that are scheduled to
be run during that time segment.

::: scheme
(define (make-time-segment time queue) (cons time queue)) (define
(segment-time s) (car s)) (define (segment-queue s) (cdr s))
:::

We will operate on the time-segment queues using the queue operations
described in [Section 3.3.2](#Section 3.3.2).

The agenda itself is a one-dimensional table of time segments. It
differs from the tables described in [Section 3.3.3](#Section 3.3.3) in
that the segments will be sorted in order of increasing time. In
addition, we store the *current time* (i.e., the time of the last action
that was processed) at the head of the agenda. A newly constructed
agenda has no time segments and has a current time of 0:[^156]

::: scheme
(define (make-agenda) (list 0)) (define (current-time agenda) (car
agenda)) (define (set-current-time! agenda time) (set-car! agenda time))
(define (segments agenda) (cdr agenda)) (define (set-segments! agenda
segments) (set-cdr! agenda segments)) (define (first-segment agenda)
(car (segments agenda))) (define (rest-segments agenda) (cdr (segments
agenda)))
:::

An agenda is empty if it has no time segments:

::: scheme
(define (empty-agenda? agenda) (null? (segments agenda)))
:::

To add an action to an agenda, we first check if the agenda is empty. If
so, we create a time segment for the action and install this in the
agenda. Otherwise, we scan the agenda, examining the time of each
segment. If we find a segment for our appointed time, we add the action
to the associated queue. If we reach a time later than the one to which
we are appointed, we insert a new time segment into the agenda just
before it. If we reach the end of the agenda, we must create a new time
segment at the end.

::: scheme
(define (add-to-agenda! time action agenda) (define (belongs-before?
segments) (or (null? segments) (\< time (segment-time (car segments)))))
(define (make-new-time-segment time action) (let ((q (make-queue)))
(insert-queue! q action) (make-time-segment time q))) (define
(add-to-segments! segments) (if (= (segment-time (car segments)) time)
(insert-queue! (segment-queue (car segments)) action) (let ((rest (cdr
segments))) (if (belongs-before? rest) (set-cdr! segments (cons
(make-new-time-segment time action) (cdr segments))) (add-to-segments!
rest))))) (let ((segments (segments agenda))) (if (belongs-before?
segments) (set-segments! agenda (cons (make-new-time-segment time
action) segments)) (add-to-segments! segments))))
:::

The procedure that removes the first item from the agenda deletes the
item at the front of the queue in the first time segment. If this
deletion makes the time segment empty, we remove it from the list of
segments:[^157]

::: scheme
(define (remove-first-agenda-item! agenda) (let ((q (segment-queue
(first-segment agenda)))) (delete-queue! q) (if (empty-queue? q)
(set-segments! agenda (rest-segments agenda)))))
:::

The first agenda item is found at the head of the queue in the first
time segment. Whenever we extract an item, we also update the current
time:[^158]

::: scheme
(define (first-agenda-item agenda) (if (empty-agenda? agenda) (error
\"Agenda is empty: FIRST-AGENDA-ITEM\") (let ((first-seg (first-segment
agenda))) (set-current-time! agenda (segment-time first-seg))
(front-queue (segment-queue first-seg)))))
:::

> **[]{#Exercise 3.32 label="Exercise 3.32"}Exercise 3.32:** The
> procedures to be run during each time segment of the agenda are kept
> in a queue. Thus, the procedures for each segment are called in the
> order in which they were added to the agenda (first in, first out).
> Explain why this order must be used. In particular, trace the behavior
> of an and-gate whose inputs change from 0, 1 to 1, 0 in the same
> segment and say how the behavior would differ if we stored a segment's
> procedures in an ordinary list, adding and removing procedures only at
> the front (last in, first out).

### Propagation of Constraints {#Section 3.3.5}

Computer programs are traditionally organized as one-directional
computations, which perform operations on prespecified arguments to
produce desired outputs. On the other hand, we often model systems in
terms of relations among quantities. For example, a mathematical model
of a mechanical structure might include the information that the
deflection $d$ of a metal rod is related to the force $F$ on the rod,
the length $L$ of the rod, the cross-sectional area $A$, and the elastic
modulus $E$ via the equation

$$dAE = FL.$$

Such an equation is not one-directional. Given any four of the
quantities, we can use it to compute the fifth. Yet translating the
equation into a traditional computer language would force us to choose
one of the quantities to be computed in terms of the other four. Thus, a
procedure for computing the area $A$ could not be used to compute the
deflection $d$, even though the computations of $A$ and $d$ arise from
the same equation.[^159]

In this section, we sketch the design of a language that enables us to
work in terms of relations themselves. The primitive elements of the
language are *primitive constraints*, which state that certain relations
hold between quantities. For example, `(adder a b c)` specifies that the
quantities $a$, $b$, and $c$ must be related by the equation
$a + b = c$, `(multiplier x y z)` expresses the constraint $xy = z$, and
`(constant 3.14 x)` says that the value of $x$ must be 3.14.

Our language provides a means of combining primitive constraints in
order to express more complex relations. We combine constraints by
constructing *constraint networks*, in which constraints are joined by
*connectors*. A connector is an object that "holds" a value that may
participate in one or more constraints. For example, we know that the
relationship between Fahrenheit and Celsius temperatures is

$$9C = 5(F - 32).$$

Such a constraint can be thought of as a network consisting of primitive
adder, multiplier, and constant constraints ([Figure
3.28](#Figure 3.28)). In the figure, we see on the left a multiplier box
with three terminals, labeled $m$`<!-- -->`{=html}1,
$m$`<!-- -->`{=html}2, and $p$. These connect the multiplier to the rest
of the network as follows: The $m$`<!-- -->`{=html}1 terminal is linked
to a connector $C$, which will hold the Celsius temperature. The
$m$`<!-- -->`{=html}2 terminal is linked to a connector $w$, which is
also linked to a constant box that holds 9. The $p$ terminal, which the
multiplier box constrains to be the product of $m$`<!-- -->`{=html}1 and
$m$`<!-- -->`{=html}2, is linked to the $p$ terminal of another
multiplier box, whose $m$`<!-- -->`{=html}2 is connected to a constant 5
and whose $m$`<!-- -->`{=html}1 is connected to one of the terms in a
sum.

[]{#Figure 3.28 label="Figure 3.28"}

![image](fig/chap3/Fig3.28.pdf){width="87mm"}

> **Figure 3.28:** The relation $9C = 5(F - 32)$ expressed as a
> constraint network.

Computation by such a network proceeds as follows: When a connector is
given a value (by the user or by a constraint box to which it is
linked), it awakens all of its associated constraints (except for the
constraint that just awakened it) to inform them that it has a value.
Each awakened constraint box then polls its connectors to see if there
is enough information to determine a value for a connector. If so, the
box sets that connector, which then awakens all of its associated
constraints, and so on. For instance, in conversion between Celsius and
Fahrenheit, $w$, $x$, and $y$ are immediately set by the constant boxes
to 9, 5, and 32, respectively. The connectors awaken the multipliers and
the adder, which determine that there is not enough information to
proceed. If the user (or some other part of the network) sets $C$ to a
value (say 25), the leftmost multiplier will be awakened, and it will
set $u$ to $25 \cdot 9 = 225$. Then $u$ awakens the second multiplier,
which sets $v$ to 45, and $v$ awakens the adder, which sets $f$ to 77.

#### Using the constraint system {#using-the-constraint-system .unnumbered}

To use the constraint system to carry out the temperature computation
outlined above, we first create two connectors, `C` and `F`, by calling
the constructor `make/connector`, and link `C` and `F` in an appropriate
network:

::: scheme
(define C (make-connector)) (define F (make-connector))
(celsius-fahrenheit-converter C F)  *ok*
:::

The procedure that creates the network is defined as follows:

::: scheme
(define (celsius-fahrenheit-converter c f) (let ((u (make-connector)) (v
(make-connector)) (w (make-connector)) (x (make-connector)) (y
(make-connector))) (multiplier c w u) (multiplier v x u) (adder v y f)
(constant 9 w) (constant 5 x) (constant 32 y) 'ok))
:::

This procedure creates the internal connectors `u`, `v`, `w`, `x`, and
`y`, and links them as shown in [Figure 3.28](#Figure 3.28) using the
primitive constraint constructors `adder`, `multiplier`, and `constant`.
Just as with the digital-circuit simulator of [Section
3.3.4](#Section 3.3.4), expressing these combinations of primitive
elements in terms of procedures automatically provides our language with
a means of abstraction for compound objects.

To watch the network in action, we can place probes on the connectors
`C` and `F`, using a `probe` procedure similar to the one we used to
monitor wires in [Section 3.3.4](#Section 3.3.4). Placing a probe on a
connector will cause a message to be printed whenever the connector is
given a value:

::: scheme
(probe \"Celsius temp\" C) (probe \"Fahrenheit temp\" F)
:::

Next we set the value of `C` to 25. (The third argument to `set/value!`
tells `C` that this directive comes from the `user`.)

::: scheme
(set-value! C 25 'user)  *Probe: Celsius temp = 25*   *Probe: Fahrenheit
temp = 77*   *done*
:::

The probe on `C` awakens and reports the value. `C` also propagates its
value through the network as described above. This sets `F` to 77, which
is reported by the probe on `F`.

Now we can try to set `F` to a new value, say 212:

::: scheme
(set-value! F 212 'user)  *Error! Contradiction (77 212)*
:::

The connector complains that it has sensed a contradiction: Its value is
77, and someone is trying to set it to 212. If we really want to reuse
the network with new values, we can tell `C` to forget its old value:

::: scheme
(forget-value! C 'user)  *Probe: Celsius temp = ?*   *Probe: Fahrenheit
temp = ?*   *done*
:::

`C` finds that the `user`, who set its value originally, is now
retracting that value, so `C` agrees to lose its value, as shown by the
probe, and informs the rest of the network of this fact. This
information eventually propagates to `F`, which now finds that it has no
reason for continuing to believe that its own value is 77. Thus, `F`
also gives up its value, as shown by the probe.

Now that `F` has no value, we are free to set it to 212:

::: scheme
(set-value! F 212 'user)  *Probe: Fahrenheit temp = 212*   *Probe:
Celsius temp = 100*   *done*
:::

This new value, when propagated through the network, forces `C` to have
a value of 100, and this is registered by the probe on `C`. Notice that
the very same network is being used to compute `C` given `F` and to
compute `F` given `C`. This nondirectionality of computation is the
distinguishing feature of constraint-based systems.

#### Implementing the constraint system {#implementing-the-constraint-system .unnumbered}

The constraint system is implemented via procedural objects with local
state, in a manner very similar to the digital-circuit simulator of
[Section 3.3.4](#Section 3.3.4). Although the primitive objects of the
constraint system are somewhat more complex, the overall system is
simpler, since there is no concern about agendas and logic delays.

The basic operations on connectors are the following:

-   `(has/value? `$\langle$*`connector`*$\rangle$`)` tells whether the
    connector has a value.

-   `(get/value `$\langle$*`connector`*$\rangle$`)` returns the
    connector's current value.

-   `(set/value! `$\langle$*`connector`*$\rangle$` `$\langle$*`new/value`*$\rangle$` `$\langle$*`informant`*$\rangle$`)`
    indicates that the informant is requesting the connector to set its
    value to the new value.

-   `(forget/value! `$\langle$*`connector`*$\rangle$` `$\langle$*`retractor`*$\rangle$`)`
    tells the connector that the retractor is requesting it to forget
    its value.

-   `(connect `$\langle$*`connector`*$\rangle$` `$\langle$*`new/constraint`*$\rangle$`)`
    tells the connector to participate in the new constraint.

The connectors communicate with the constraints by means of the
procedures `inform/about/value`, which tells the given constraint that
the connector has a value, and `inform/about/no/value`, which tells the
constraint that the connector has lost its value.

`adder` constructs an adder constraint among summand connectors `a1` and
`a2` and a `sum` connector. An adder is implemented as a procedure with
local state (the procedure `me` below):

::: scheme
(define (adder a1 a2 sum) (define (process-new-value) (cond ((and
(has-value? a1) (has-value? a2)) (set-value! sum (+ (get-value a1)
(get-value a2)) me)) ((and (has-value? a1) (has-value? sum)) (set-value!
a2 (- (get-value sum) (get-value a1)) me)) ((and (has-value? a2)
(has-value? sum)) (set-value! a1 (- (get-value sum) (get-value a2))
me)))) (define (process-forget-value) (forget-value! sum me)
(forget-value! a1 me) (forget-value! a2 me) (process-new-value)) (define
(me request) (cond ((eq? request 'I-have-a-value) (process-new-value))
((eq? request 'I-lost-my-value) (process-forget-value)) (else (error
\"Unknown request: ADDER\" request)))) (connect a1 me) (connect a2 me)
(connect sum me) me)
:::

`adder` connects the new adder to the designated connectors and returns
it as its value. The procedure `me`, which represents the adder, acts as
a dispatch to the local procedures. The following "syntax interfaces"
(see [Footnote 27](#Footnote 27) in [Section 3.3.4](#Section 3.3.4)) are
used in conjunction with the dispatch:

::: scheme
(define (inform-about-value constraint) (constraint 'I-have-a-value))
(define (inform-about-no-value constraint) (constraint
'I-lost-my-value))
:::

The adder's local procedure `process/new/value` is called when the adder
is informed that one of its connectors has a value. The adder first
checks to see if both `a1` and `a2` have values. If so, it tells `sum`
to set its value to the sum of the two addends. The `informant` argument
to `set/value!` is `me`, which is the adder object itself. If `a1` and
`a2` do not both have values, then the adder checks to see if perhaps
`a1` and `sum` have values. If so, it sets `a2` to the difference of
these two. Finally, if `a2` and `sum` have values, this gives the adder
enough information to set `a1`. If the adder is told that one of its
connectors has lost a value, it requests that all of its connectors now
lose their values. (Only those values that were set by this adder are
actually lost.) Then it runs `process/new/value`. The reason for this
last step is that one or more connectors may still have a value (that
is, a connector may have had a value that was not originally set by the
adder), and these values may need to be propagated back through the
adder.

A multiplier is very similar to an adder. It will set its `product` to 0
if either of the factors is 0, even if the other factor is not known.

::: scheme
(define (multiplier m1 m2 product) (define (process-new-value) (cond
((or (and (has-value? m1) (= (get-value m1) 0)) (and (has-value? m2) (=
(get-value m2) 0))) (set-value! product 0 me)) ((and (has-value? m1)
(has-value? m2)) (set-value! product (\* (get-value m1) (get-value m2))
me)) ((and (has-value? product) (has-value? m1)) (set-value! m2 (/
(get-value product) (get-value m1)) me)) ((and (has-value? product)
(has-value? m2)) (set-value! m1 (/ (get-value product) (get-value m2))
me)))) (define (process-forget-value) (forget-value! product me)
(forget-value! m1 me) (forget-value! m2 me) (process-new-value)) (define
(me request) (cond ((eq? request 'I-have-a-value) (process-new-value))
((eq? request 'I-lost-my-value) (process-forget-value)) (else (error
\"Unknown request: MULTIPLIER\" request)))) (connect m1 me) (connect m2
me) (connect product me) me)
:::

A `constant` constructor simply sets the value of the designated
connector. Any `I/have/a/value` or `I/lost/my/value` message sent to the
constant box will produce an error.

::: scheme
(define (constant value connector) (define (me request) (error \"Unknown
request: CONSTANT\" request)) (connect connector me) (set-value!
connector value me) me)
:::

Finally, a probe prints a message about the setting or unsetting of the
designated connector:

::: scheme
(define (probe name connector) (define (print-probe value) (newline)
(display \"Probe: \") (display name) (display \" = \") (display value))
(define (process-new-value) (print-probe (get-value connector))) (define
(process-forget-value) (print-probe \"?\")) (define (me request) (cond
((eq? request 'I-have-a-value) (process-new-value)) ((eq? request
'I-lost-my-value) (process-forget-value)) (else (error \"Unknown
request: PROBE\" request)))) (connect connector me) me)
:::

#### Representing connectors {#representing-connectors .unnumbered}

A connector is represented as a procedural object with local state
variables `value`, the current value of the connector; `informant`, the
object that set the connector's value; and `constraints`, a list of the
constraints in which the connector participates.

::: scheme
(define (make-connector) (let ((value false) (informant false)
(constraints '())) (define (set-my-value newval setter) (cond ((not
(has-value? me)) (set! value newval) (set! informant setter)
(for-each-except setter inform-about-value constraints)) ((not (= value
newval)) (error \"Contradiction\" (list value newval))) (else
'ignored))) (define (forget-my-value retractor) (if (eq? retractor
informant) (begin (set! informant false) (for-each-except retractor
inform-about-no-value constraints)) 'ignored)) (define (connect
new-constraint) (if (not (memq new-constraint constraints)) (set!
constraints (cons new-constraint constraints))) (if (has-value? me)
(inform-about-value new-constraint)) 'done) (define (me request) (cond
((eq? request 'has-value?) (if informant true false)) ((eq? request
'value) value) ((eq? request 'set-value!) set-my-value) ((eq? request
'forget) forget-my-value) ((eq? request 'connect) connect) (else (error
\"Unknown operation: CONNECTOR\" request)))) me))
:::

The connector's local procedure `set/my/value` is called when there is a
request to set the connector's value. If the connector does not
currently have a value, it will set its value and remember as
`informant` the constraint that requested the value to be set.[^160]
Then the connector will notify all of its participating constraints
except the constraint that requested the value to be set. This is
accomplished using the following iterator, which applies a designated
procedure to all items in a list except a given one:

::: scheme
(define (for-each-except exception procedure list) (define (loop items)
(cond ((null? items) 'done) ((eq? (car items) exception) (loop (cdr
items))) (else (procedure (car items)) (loop (cdr items))))) (loop
list))
:::

If a connector is asked to forget its value, it runs the local procedure
`forget/my/value`, which first checks to make sure that the request is
coming from the same object that set the value originally. If so, the
connector informs its associated constraints about the loss of the
value.

The local procedure `connect` adds the designated new constraint to the
list of constraints if it is not already in that list. Then, if the
connector has a value, it informs the new constraint of this fact.

The connector's procedure `me` serves as a dispatch to the other
internal procedures and also represents the connector as an object. The
following procedures provide a syntax interface for the dispatch:

::: scheme
(define (has-value? connector) (connector 'has-value?)) (define
(get-value connector) (connector 'value)) (define (set-value! connector
new-value informant) ((connector 'set-value!) new-value informant))
(define (forget-value! connector retractor) ((connector 'forget)
retractor)) (define (connect connector new-constraint) ((connector
'connect) new-constraint))
:::

> **[]{#Exercise 3.33 label="Exercise 3.33"}Exercise 3.33:** Using
> primitive multiplier, adder, and constant constraints, define a
> procedure `averager` that takes three connectors `a`, `b`, and `c` as
> inputs and establishes the constraint that the value of `c` is the
> average of the values of `a` and `b`.

> **[]{#Exercise 3.34 label="Exercise 3.34"}Exercise 3.34:** Louis
> Reasoner wants to build a squarer, a constraint device with two
> terminals such that the value of connector `b` on the second terminal
> will always be the square of the value `a` on the first terminal. He
> proposes the following simple device made from a multiplier:
>
> ::: scheme
> (define (squarer a b) (multiplier a a b))
> :::
>
> There is a serious flaw in this idea. Explain.

> **[]{#Exercise 3.35 label="Exercise 3.35"}Exercise 3.35:** Ben
> Bitdiddle tells Louis that one way to avoid the trouble in [Exercise
> 3.34](#Exercise 3.34) is to define a squarer as a new primitive
> constraint. Fill in the missing portions in Ben's outline for a
> procedure to implement such a constraint:
>
> ::: scheme
> (define (squarer a b) (define (process-new-value) (if (has-value? b)
> (if (\< (get-value b) 0) (error \"square less than 0: SQUARER\"
> (get-value b))
>  $\color{SchemeDark}\langle$  *alternative1*  $\color{SchemeDark}\rangle$ )
>  $\color{SchemeDark}\langle$  *alternative2*  $\color{SchemeDark}\rangle$ ))
> (define (process-forget-value)
>  $\color{SchemeDark}\langle$  *body1*  $\color{SchemeDark}\rangle$ )
> (define (me request)
>  $\color{SchemeDark}\langle$  *body2*  $\color{SchemeDark}\rangle$ )
>  $\color{SchemeDark}\langle$  *rest of
> definition*  $\color{SchemeDark}\rangle$  me)
> :::

> **[]{#Exercise 3.36 label="Exercise 3.36"}Exercise 3.36:** Suppose we
> evaluate the following sequence of expressions in the global
> environment:
>
> ::: scheme
> (define a (make-connector)) (define b (make-connector)) (set-value! a
> 10 'user)
> :::
>
> At some time during evaluation of the `set/value!`, the following
> expression from the connector's local procedure is evaluated:
>
> ::: scheme
> (for-each-except setter inform-about-value constraints)
> :::
>
> Draw an environment diagram showing the environment in which the above
> expression is evaluated.

> **[]{#Exercise 3.37 label="Exercise 3.37"}Exercise 3.37:** The
> `celsius/fahrenheit/converter` procedure is cumbersome when compared
> with a more expression-oriented style of definition, such as
>
> ::: scheme
> (define (celsius-fahrenheit-converter x) (c+ (c\* (c/ (cv 9) (cv 5))
> x) (cv 32))) (define C (make-connector)) (define F
> (celsius-fahrenheit-converter C))
> :::
>
> Here `c+`, `c*`, etc. are the "constraint" versions of the arithmetic
> operations. For example, `c+` takes two connectors as arguments and
> returns a connector that is related to these by an adder constraint:
>
> ::: scheme
> (define (c+ x y) (let ((z (make-connector))) (adder x y z) z))
> :::
>
> Define analogous procedures `c-`, `c*`, `c/`, and `cv` (constant
> value) that enable us to define compound constraints as in the
> converter example above.[^161]

## Concurrency: Time Is of the Essence {#Section 3.4}

We've seen the power of computational objects with local state as tools
for modeling. Yet, as [Section 3.1.3](#Section 3.1.3) warned, this power
extracts a price: the loss of referential transparency, giving rise to a
thicket of questions about sameness and change, and the need to abandon
the substitution model of evaluation in favor of the more intricate
environment model.

The central issue lurking beneath the complexity of state, sameness, and
change is that by introducing assignment we are forced to admit *time*
into our computational models. Before we introduced assignment, all our
programs were timeless, in the sense that any expression that has a
value always has the same value. In contrast, recall the example of
modeling withdrawals from a bank account and returning the resulting
balance, introduced at the beginning of [Section 3.1.1](#Section 3.1.1):

::: scheme
(withdraw 25)  *75*  (withdraw 25)  *50*
:::

Here successive evaluations of the same expression yield different
values. This behavior arises from the fact that the execution of
assignment statements (in this case, assignments to the variable
`balance`) delineates *moments in time* when values change. The result
of evaluating an expression depends not only on the expression itself,
but also on whether the evaluation occurs before or after these moments.
Building models in terms of computational objects with local state
forces us to confront time as an essential concept in programming.

We can go further in structuring computational models to match our
perception of the physical world. Objects in the world do not change one
at a time in sequence. Rather we perceive them as acting
*concurrently*---all at once. So it is often natural to model systems as
collections of computational processes that execute concurrently. Just
as we can make our programs modular by organizing models in terms of
objects with separate local state, it is often appropriate to divide
computational models into parts that evolve separately and concurrently.
Even if the programs are to be executed on a sequential computer, the
practice of writing programs as if they were to be executed concurrently
forces the programmer to avoid inessential timing constraints and thus
makes programs more modular.

In addition to making programs more modular, concurrent computation can
provide a speed advantage over sequential computation. Sequential
computers execute only one operation at a time, so the amount of time it
takes to perform a task is proportional to the total number of
operations performed.[^162] However, if it is possible to decompose a
problem into pieces that are relatively independent and need to
communicate only rarely, it may be possible to allocate pieces to
separate computing processors, producing a speed advantage proportional
to the number of processors available.

Unfortunately, the complexities introduced by assignment become even
more problematic in the presence of concurrency. The fact of concurrent
execution, either because the world operates in parallel or because our
computers do, entails additional complexity in our understanding of
time.

### The Nature of Time in Concurrent Systems {#Section 3.4.1}

On the surface, time seems straightforward. It is an ordering imposed on
events.[^163] For any events $A$ and $B$, either $A$ occurs before $B$,
$A$ and $B$ are simultaneous, or $A$ occurs after $B$. For instance,
returning to the bank account example, suppose that Peter withdraws \$10
and Paul withdraws \$25 from a joint account that initially contains
\$100, leaving \$65 in the account. Depending on the order of the two
withdrawals, the sequence of balances in the account is either
$\,\$100 \to \$90 \to \$65\,$ or $\,\$100 \to \$75
\to \$65\,$. In a computer implementation of the banking system, this
changing sequence of balances could be modeled by successive assignments
to a variable `balance`.

In complex situations, however, such a view can be problematic. Suppose
that Peter and Paul, and other people besides, are accessing the same
bank account through a network of banking machines distributed all over
the world. The actual sequence of balances in the account will depend
critically on the detailed timing of the accesses and the details of the
communication among the machines.

This indeterminacy in the order of events can pose serious problems in
the design of concurrent systems. For instance, suppose that the
withdrawals made by Peter and Paul are implemented as two separate
processes sharing a common variable `balance`, each process specified by
the procedure given in [Section 3.1.1](#Section 3.1.1):

::: scheme
(define (withdraw amount) (if (\>= balance amount) (begin (set! balance
(- balance amount)) balance) \"Insufficient funds\"))
:::

If the two processes operate independently, then Peter might test the
balance and attempt to withdraw a legitimate amount. However, Paul might
withdraw some funds in between the time that Peter checks the balance
and the time Peter completes the withdrawal, thus invalidating Peter's
test.

Things can be worse still. Consider the expression

::: scheme
(set! balance (- balance amount))
:::

executed as part of each withdrawal process. This consists of three
steps: (1) accessing the value of the `balance` variable; (2) computing
the new balance; (3) setting `balance` to this new value. If Peter and
Paul's withdrawals execute this statement concurrently, then the two
withdrawals might interleave the order in which they access `balance`
and set it to the new value.

[]{#Figure 3.29 label="Figure 3.29"}

![image](fig/chap3/Fig3.29b.pdf){width="109mm"}

> **Figure 3.29:** Timing diagram showing how interleaving the order of
> events in two banking withdrawals can lead to an incorrect final
> balance.

The timing diagram in [Figure 3.29](#Figure 3.29) depicts an order of
events where `balance` starts at 100, Peter withdraws 10, Paul withdraws
25, and yet the final value of `balance` is 75. As shown in the diagram,
the reason for this anomaly is that Paul's assignment of 75 to `balance`
is made under the assumption that the value of `balance` to be
decremented is 100. That assumption, however, became invalid when Peter
changed `balance` to 90. This is a catastrophic failure for the banking
system, because the total amount of money in the system is not
conserved. Before the transactions, the total amount of money was \$100.
Afterwards, Peter has \$10, Paul has \$25, and the bank has \$75.[^164]

The general phenomenon illustrated here is that several processes may
share a common state variable. What makes this complicated is that more
than one process may be trying to manipulate the shared state at the
same time. For the bank account example, during each transaction, each
customer should be able to act as if the other customers did not exist.
When a customer changes the balance in a way that depends on the
balance, he must be able to assume that, just before the moment of
change, the balance is still what he thought it was.

#### Correct behavior of concurrent programs {#correct-behavior-of-concurrent-programs .unnumbered}

The above example typifies the subtle bugs that can creep into
concurrent programs. The root of this complexity lies in the assignments
to variables that are shared among the different processes. We already
know that we must be careful in writing programs that use `set!`,
because the results of a computation depend on the order in which the
assignments occur.[^165] With concurrent processes we must be especially
careful about assignments, because we may not be able to control the
order of the assignments made by the different processes. If several
such changes might be made concurrently (as with two depositors
accessing a joint account) we need some way to ensure that our system
behaves correctly. For example, in the case of withdrawals from a joint
bank account, we must ensure that money is conserved. To make concurrent
programs behave correctly, we may have to place some restrictions on
concurrent execution.

One possible restriction on concurrency would stipulate that no two
operations that change any shared state variables can occur at the same
time. This is an extremely stringent requirement. For distributed
banking, it would require the system designer to ensure that only one
transaction could proceed at a time. This would be both inefficient and
overly conservative. [Figure 3.30](#Figure 3.30) shows Peter and Paul
sharing a bank account, where Paul has a private account as well. The
diagram illustrates two withdrawals from the shared account (one by
Peter and one by Paul) and a deposit to Paul's private account.[^166]
The two withdrawals from the shared account must not be concurrent
(since both access and update the same account), and Paul's deposit and
withdrawal must not be concurrent (since both access and update the
amount in Paul's wallet). But there should be no problem permitting
Paul's deposit to his private account to proceed concurrently with
Peter's withdrawal from the shared account.

[]{#Figure 3.30 label="Figure 3.30"}

![image](fig/chap3/Fig3.30b.pdf){width="94mm"}

> **Figure 3.30:** Concurrent deposits and withdrawals from a joint
> account in Bank1 and a private account in Bank2.

A less stringent restriction on concurrency would ensure that a
concurrent system produces the same result as if the processes had run
sequentially in some order. There are two important aspects to this
requirement. First, it does not require the processes to actually run
sequentially, but only to produce results that are the same *as if* they
had run sequentially. For the example in [Figure 3.30](#Figure 3.30),
the designer of the bank account system can safely allow Paul's deposit
and Peter's withdrawal to happen concurrently, because the net result
will be the same as if the two operations had happened sequentially.
Second, there may be more than one possible "correct" result produced by
a concurrent program, because we require only that the result be the
same as for *some* sequential order. For example, suppose that Peter and
Paul's joint account starts out with \$100, and Peter deposits \$40
while Paul concurrently withdraws half the money in the account. Then
sequential execution could result in the account balance being either
\$70 or \$90 (see [Exercise 3.38](#Exercise 3.38)).[^167]

There are still weaker requirements for correct execution of concurrent
programs. A program for simulating diffusion (say, the flow of heat in
an object) might consist of a large number of processes, each one
representing a small volume of space, that update their values
concurrently. Each process repeatedly changes its value to the average
of its own value and its neighbors' values. This algorithm converges to
the right answer independent of the order in which the operations are
done; there is no need for any restrictions on concurrent use of the
shared values.

> **[]{#Exercise 3.38 label="Exercise 3.38"}Exercise 3.38:** Suppose
> that Peter, Paul, and Mary share a joint bank account that initially
> contains \$100. Concurrently, Peter deposits \$10, Paul withdraws
> \$20, and Mary withdraws half the money in the account, by executing
> the following commands:
>
> ::: scheme
> Peter: (set! balance (+ balance 10)) Paul: (set! balance (- balance
> 20)) Mary: (set! balance (- balance (/ balance 2)))
> :::
>
> a.  List all the different possible values for `balance` after these
>     three transactions have been completed, assuming that the banking
>     system forces the three processes to run sequentially in some
>     order.
>
> b.  What are some other values that could be produced if the system
>     allows the processes to be interleaved? Draw timing diagrams like
>     the one in [Figure 3.29](#Figure 3.29) to explain how these values
>     can occur.

### Mechanisms for Controlling Concurrency {#Section 3.4.2}

We've seen that the difficulty in dealing with concurrent processes is
rooted in the need to consider the interleaving of the order of events
in the different processes. For example, suppose we have two processes,
one with three ordered events $(a, b, c)$ and one with three ordered
events $(x, y, z)$. If the two processes run concurrently, with no
constraints on how their execution is interleaved, then there are 20
different possible orderings for the events that are consistent with the
individual orderings for the two processes:

(a,b,c,x,y,z) (a,x,b,y,c,z) (x,a,b,c,y,z) (x,a,y,z,b,c) (a,b,x,c,y,z)
(a,x,b,y,z,c) (x,a,b,y,c,z) (x,y,a,b,c,z) (a,b,x,y,c,z) (a,x,y,b,c,z)
(x,a,b,y,z,c) (x,y,a,b,z,c) (a,b,x,y,z,c) (a,x,y,b,z,c) (x,a,y,b,c,z)
(x,y,a,z,b,c) (a,x,b,c,y,z) (a,x,y,z,b,c) (x,a,y,b,z,c) (x,y,z,a,b,c)

As programmers designing this system, we would have to consider the
effects of each of these 20 orderings and check that each behavior is
acceptable. Such an approach rapidly becomes unwieldy as the numbers of
processes and events increase.

A more practical approach to the design of concurrent systems is to
devise general mechanisms that allow us to constrain the interleaving of
concurrent processes so that we can be sure that the program behavior is
correct. Many mechanisms have been developed for this purpose. In this
section, we describe one of them, the *serializer*.

#### Serializing access to shared state {#serializing-access-to-shared-state .unnumbered}

Serialization implements the following idea: Processes will execute
concurrently, but there will be certain collections of procedures that
cannot be executed concurrently. More precisely, serialization creates
distinguished sets of procedures such that only one execution of a
procedure in each serialized set is permitted to happen at a time. If
some procedure in the set is being executed, then a process that
attempts to execute any procedure in the set will be forced to wait
until the first execution has finished.

We can use serialization to control access to shared variables. For
example, if we want to update a shared variable based on the previous
value of that variable, we put the access to the previous value of the
variable and the assignment of the new value to the variable in the same
procedure. We then ensure that no other procedure that assigns to the
variable can run concurrently with this procedure by serializing all of
these procedures with the same serializer. This guarantees that the
value of the variable cannot be changed between an access and the
corresponding assignment.

#### Serializers in Scheme {#serializers-in-scheme .unnumbered}

To make the above mechanism more concrete, suppose that we have extended
Scheme to include a procedure called `parallel/execute`:

::: scheme
(parallel-execute
 $\color{SchemeDark}\langle$  *p*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *p*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *p*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize k}}\rangle$ )
:::

Each $\langle p \rangle$ must be a procedure of no arguments.
`parallel/execute` creates a separate process for each
$\langle p \rangle$, which applies $\langle p \rangle$ (to no
arguments). These processes all run concurrently.[^168]

As an example of how this is used, consider

::: scheme
(define x 10) (parallel-execute (lambda () (set! x (\* x x))) (lambda ()
(set! x (+ x 1))))
:::

This creates two concurrent processes---$P_1$, which sets `x` to `x`
times `x`, and $P_2$, which increments `x`. After execution is complete,
`x` will be left with one of five possible values, depending on the
interleaving of the events of $P_1$ and $P_2$:

::: scheme
101:  [$P_1$ sets `x` to 100 and then $P_2$ increments `x` to
101.]{.roman}  121:  [$P_2$ increments `x` to 11 and then $P_1$ sets `x`
to `x` `*` `x`.]{.roman}  110:  [$P_2$ changes `x` from 10 to 11 between
the two times that]{.roman}   [$P_1$ accesses the value of `x` during
the evaluation of `(* x x)`.]{.roman}  11:  [$P_2$ accesses `x`, then
$P_1$ sets `x` to 100, then $P_2$ sets `x`.]{.roman}  100:  [$P_1$
accesses `x` (twice), then $P_2$ sets `x` to 11, then $P_1$ sets
`x`.]{.roman}
:::

We can constrain the concurrency by using serialized procedures, which
are created by *serializers*. Serializers are constructed by
`make/serializer`, whose implementation is given below. A serializer
takes a procedure as argument and returns a serialized procedure that
behaves like the original procedure. All calls to a given serializer
return serialized procedures in the same set.

Thus, in contrast to the example above, executing

::: scheme
(define x 10) (define s (make-serializer)) (parallel-execute (s (lambda
() (set! x (\* x x)))) (s (lambda () (set! x (+ x 1)))))
:::

can produce only two possible values for `x`, 101 or 121. The other
possibilities are eliminated, because the execution of $P_1$ and $P_2$
cannot be interleaved.

Here is a version of the `make/account` procedure from [Section
3.1.1](#Section 3.1.1), where the deposits and withdrawals have been
serialized:

::: scheme
(define (make-account balance) (define (withdraw amount) (if (\>=
balance amount) (begin (set! balance (- balance amount)) balance)
\"Insufficient funds\")) (define (deposit amount) (set! balance (+
balance amount)) balance) (let ((protected (make-serializer))) (define
(dispatch m) (cond ((eq? m 'withdraw) (protected withdraw)) ((eq? m
'deposit) (protected deposit)) ((eq? m 'balance) balance) (else (error
\"Unknown request: MAKE-ACCOUNT\" m)))) dispatch))
:::

With this implementation, two processes cannot be withdrawing from or
depositing into a single account concurrently. This eliminates the
source of the error illustrated in [Figure 3.29](#Figure 3.29), where
Peter changes the account balance between the times when Paul accesses
the balance to compute the new value and when Paul actually performs the
assignment. On the other hand, each account has its own serializer, so
that deposits and withdrawals for different accounts can proceed
concurrently.

> **[]{#Exercise 3.39 label="Exercise 3.39"}Exercise 3.39:** Which of
> the five possibilities in the parallel execution shown above remain if
> we instead serialize execution as follows:
>
> ::: scheme
> (define x 10) (define s (make-serializer)) (parallel-execute (lambda
> () (set! x ((s (lambda () (\* x x)))))) (s (lambda () (set! x (+ x
> 1)))))
> :::

> **[]{#Exercise 3.40 label="Exercise 3.40"}Exercise 3.40:** Give all
> possible values of `x` that can result from executing
>
> ::: scheme
> (define x 10) (parallel-execute (lambda () (set! x (\* x x))) (lambda
> () (set! x (\* x x x))))
> :::
>
> Which of these possibilities remain if we instead use serialized
> procedures:
>
> ::: scheme
> (define x 10) (define s (make-serializer)) (parallel-execute (s
> (lambda () (set! x (\* x x)))) (s (lambda () (set! x (\* x x x)))))
> :::

> **[]{#Exercise 3.41 label="Exercise 3.41"}Exercise 3.41:** Ben
> Bitdiddle worries that it would be better to implement the bank
> account as follows (where the commented line has been changed):
>
> ::: scheme
> (define (make-account balance) (define (withdraw amount) (if (\>=
> balance amount) (begin (set! balance (- balance amount)) balance)
> \"Insufficient funds\")) (define (deposit amount) (set! balance (+
> balance amount)) balance) (let ((protected (make-serializer))) (define
> (dispatch m) (cond ((eq? m 'withdraw) (protected withdraw)) ((eq? m
> 'deposit) (protected deposit)) ((eq? m 'balance) ((protected (lambda
> () balance))))  [; serialized]{.roman}  (else (error \"Unknown
> request: MAKE-ACCOUNT\" m)))) dispatch))
> :::
>
> because allowing unserialized access to the bank balance can result in
> anomalous behavior. Do you agree? Is there any scenario that
> demonstrates Ben's concern?

> **[]{#Exercise 3.42 label="Exercise 3.42"}Exercise 3.42:** Ben
> Bitdiddle suggests that it's a waste of time to create a new
> serialized procedure in response to every `withdraw` and `deposit`
> message. He says that `make/account` could be changed so that the
> calls to `protected` are done outside the `dispatch` procedure. That
> is, an account would return the same serialized procedure (which was
> created at the same time as the account) each time it is asked for a
> withdrawal procedure.
>
> ::: scheme
> (define (make-account balance) (define (withdraw amount) (if (\>=
> balance amount) (begin (set! balance (- balance amount)) balance)
> \"Insufficient funds\")) (define (deposit amount) (set! balance (+
> balance amount)) balance) (let ((protected (make-serializer))) (let
> ((protected-withdraw (protected withdraw)) (protected-deposit
> (protected deposit))) (define (dispatch m) (cond ((eq? m 'withdraw)
> protected-withdraw) ((eq? m 'deposit) protected-deposit) ((eq? m
> 'balance) balance) (else (error \"Unknown request: MAKE-ACCOUNT\"
> m)))) dispatch)))
> :::
>
> Is this a safe change to make? In particular, is there any difference
> in what concurrency is allowed by these two versions of
> `make/account`?

#### Complexity of using multiple shared resources {#complexity-of-using-multiple-shared-resources .unnumbered}

Serializers provide a powerful abstraction that helps isolate the
complexities of concurrent programs so that they can be dealt with
carefully and (hopefully) correctly. However, while using serializers is
relatively straightforward when there is only a single shared resource
(such as a single bank account), concurrent programming can be
treacherously difficult when there are multiple shared resources.

To illustrate one of the difficulties that can arise, suppose we wish to
swap the balances in two bank accounts. We access each account to find
the balance, compute the difference between the balances, withdraw this
difference from one account, and deposit it in the other account. We
could implement this as follows:[^169]

::: scheme
(define (exchange account1 account2) (let ((difference (- (account1
'balance) (account2 'balance)))) ((account1 'withdraw) difference)
((account2 'deposit) difference)))
:::

This procedure works well when only a single process is trying to do the
exchange. Suppose, however, that Peter and Paul both have access to
accounts $a$`<!-- -->`{=html}1, $a$`<!-- -->`{=html}2, and
$a$`<!-- -->`{=html}3, and that Peter exchanges $a$`<!-- -->`{=html}1
and $a$`<!-- -->`{=html}2 while Paul concurrently exchanges
$a$`<!-- -->`{=html}1 and $a$`<!-- -->`{=html}3. Even with account
deposits and withdrawals serialized for individual accounts (as in the
`make/account` procedure shown above in this section), `exchange` can
still produce incorrect results. For example, Peter might compute the
difference in the balances for $a$`<!-- -->`{=html}1 and
$a$`<!-- -->`{=html}2, but then Paul might change the balance in
$a$`<!-- -->`{=html}1 before Peter is able to complete the
exchange.[^170] For correct behavior, we must arrange for the `exchange`
procedure to lock out any other concurrent accesses to the accounts
during the entire time of the exchange.

One way we can accomplish this is by using both accounts' serializers to
serialize the entire `exchange` procedure. To do this, we will arrange
for access to an account's serializer. Note that we are deliberately
breaking the modularity of the bank-account object by exposing the
serializer. The following version of `make/account` is identical to the
original version given in [Section 3.1.1](#Section 3.1.1), except that a
serializer is provided to protect the balance variable, and the
serializer is exported via message passing:

::: scheme
(define (make-account-and-serializer balance) (define (withdraw amount)
(if (\>= balance amount) (begin (set! balance (- balance amount))
balance) \"Insufficient funds\")) (define (deposit amount) (set! balance
(+ balance amount)) balance) (let ((balance-serializer
(make-serializer))) (define (dispatch m) (cond ((eq? m 'withdraw)
withdraw) ((eq? m 'deposit) deposit) ((eq? m 'balance) balance) ((eq? m
'serializer) balance-serializer) (else (error \"Unknown request:
MAKE-ACCOUNT\" m)))) dispatch))
:::

We can use this to do serialized deposits and withdrawals. However,
unlike our earlier serialized account, it is now the responsibility of
each user of bank-account objects to explicitly manage the
serialization, for example as follows:[^171]

::: scheme
(define (deposit account amount) (let ((s (account 'serializer)) (d
(account 'deposit))) ((s d) amount)))
:::

Exporting the serializer in this way gives us enough flexibility to
implement a serialized exchange program. We simply serialize the
original `exchange` procedure with the serializers for both accounts:

::: scheme
(define (serialized-exchange account1 account2) (let ((serializer1
(account1 'serializer)) (serializer2 (account2 'serializer)))
((serializer1 (serializer2 exchange)) account1 account2)))
:::

> **[]{#Exercise 3.43 label="Exercise 3.43"}Exercise 3.43:** Suppose
> that the balances in three accounts start out as \$10, \$20, and \$30,
> and that multiple processes run, exchanging the balances in the
> accounts. Argue that if the processes are run sequentially, after any
> number of concurrent exchanges, the account balances should be \$10,
> \$20, and \$30 in some order. Draw a timing diagram like the one in
> [Figure 3.29](#Figure 3.29) to show how this condition can be violated
> if the exchanges are implemented using the first version of the
> account-exchange program in this section. On the other hand, argue
> that even with this `exchange` program, the sum of the balances in the
> accounts will be preserved. Draw a timing diagram to show how even
> this condition would be violated if we did not serialize the
> transactions on individual accounts.

> **[]{#Exercise 3.44 label="Exercise 3.44"}Exercise 3.44:** Consider
> the problem of transferring an amount from one account to another. Ben
> Bitdiddle claims that this can be accomplished with the following
> procedure, even if there are multiple people concurrently transferring
> money among multiple accounts, using any account mechanism that
> serializes deposit and withdrawal transactions, for example, the
> version of `make/account` in the text above.
>
> ::: scheme
> (define (transfer from-account to-account amount) ((from-account
> 'withdraw) amount) ((to-account 'deposit) amount))
> :::
>
> Louis Reasoner claims that there is a problem here, and that we need
> to use a more sophisticated method, such as the one required for
> dealing with the exchange problem. Is Louis right? If not, what is the
> essential difference between the transfer problem and the exchange
> problem? (You should assume that the balance in `from/account` is at
> least `amount`.)

> **[]{#Exercise 3.45 label="Exercise 3.45"}Exercise 3.45:** Louis
> Reasoner thinks our bank-account system is unnecessarily complex and
> error-prone now that deposits and withdrawals aren't automatically
> serialized. He suggests that `make/account/and/serializer` should have
> exported the serializer (for use by such procedures as
> `serialized/exchange`) in addition to (rather than instead of) using
> it to serialize accounts and deposits as `make/account` did. He
> proposes to redefine accounts as follows:
>
> ::: smallscheme
> (define (make-account-and-serializer balance) (define (withdraw
> amount) (if (\>= balance amount) (begin (set! balance (- balance
> amount)) balance) \"Insufficient funds\")) (define (deposit amount)
> (set! balance (+ balance amount)) balance) (let ((balance-serializer
> (make-serializer))) (define (dispatch m) (cond ((eq? m 'withdraw)
> (balance-serializer withdraw)) ((eq? m 'deposit) (balance-serializer
> deposit)) ((eq? m 'balance) balance) ((eq? m 'serializer)
> balance-serializer) (else (error \"Unknown request: MAKE-ACCOUNT\"
> m)))) dispatch))
> :::
>
> Then deposits are handled as with the original `make/account`:
>
> ::: scheme
> (define (deposit account amount) ((account 'deposit) amount))
> :::
>
> Explain what is wrong with Louis's reasoning. In particular, consider
> what happens when `serialized/exchange` is called.

#### Implementing serializers {#implementing-serializers .unnumbered}

We implement serializers in terms of a more primitive synchronization
mechanism called a *mutex*. A mutex is an object that supports two
operations---the mutex can be *acquired*, and the mutex can be
*released*. Once a mutex has been acquired, no other acquire operations
on that mutex may proceed until the mutex is released.[^172] In our
implementation, each serializer has an associated mutex. Given a
procedure `p`, the serializer returns a procedure that acquires the
mutex, runs `p`, and then releases the mutex. This ensures that only one
of the procedures produced by the serializer can be running at once,
which is precisely the serialization property that we need to guarantee.

::: scheme
(define (make-serializer) (let ((mutex (make-mutex))) (lambda (p)
(define (serialized-p . args) (mutex 'acquire) (let ((val (apply p
args))) (mutex 'release) val)) serialized-p)))
:::

The mutex is a mutable object (here we'll use a one-element list, which
we'll refer to as a *cell*) that can hold the value true or false. When
the value is false, the mutex is available to be acquired. When the
value is true, the mutex is unavailable, and any process that attempts
to acquire the mutex must wait.

Our mutex constructor `make/mutex` begins by initializing the cell
contents to false. To acquire the mutex, we test the cell. If the mutex
is available, we set the cell contents to true and proceed. Otherwise,
we wait in a loop, attempting to acquire over and over again, until we
find that the mutex is available.[^173] To release the mutex, we set the
cell contents to false.

::: scheme
(define (make-mutex) (let ((cell (list false))) (define (the-mutex m)
(cond ((eq? m 'acquire) (if (test-and-set! cell) (the-mutex 'acquire)))
 [; retry]{.roman}  ((eq? m 'release) (clear! cell)))) the-mutex))
(define (clear! cell) (set-car! cell false))
:::

`test/and/set!` tests the cell and returns the result of the test. In
addition, if the test was false, `test/and/set!` sets the cell contents
to true before returning false. We can express this behavior as the
following procedure:

::: scheme
(define (test-and-set! cell) (if (car cell) true (begin (set-car! cell
true) false)))
:::

However, this implementation of `test/and/set!` does not suffice as it
stands. There is a crucial subtlety here, which is the essential place
where concurrency control enters the system: The `test/and/set!`
operation must be performed *atomically*. That is, we must guarantee
that, once a process has tested the cell and found it to be false, the
cell contents will actually be set to true before any other process can
test the cell. If we do not make this guarantee, then the mutex can fail
in a way similar to the bank-account failure in [Figure
3.29](#Figure 3.29). (See [Exercise 3.46](#Exercise 3.46).)

The actual implementation of `test/and/set!` depends on the details of
how our system runs concurrent processes. For example, we might be
executing concurrent processes on a sequential processor using a
time-slicing mechanism that cycles through the processes, permitting
each process to run for a short time before interrupting it and moving
on to the next process. In that case, `test/and/set!` can work by
disabling time slicing during the testing and setting.[^174]
Alternatively, multiprocessing computers provide instructions that
support atomic operations directly in hardware.[^175]

> **[]{#Exercise 3.46 label="Exercise 3.46"}Exercise 3.46:** Suppose
> that we implement `test/and/set!` using an ordinary procedure as shown
> in the text, without attempting to make the operation atomic. Draw a
> timing diagram like the one in [Figure 3.29](#Figure 3.29) to
> demonstrate how the mutex implementation can fail by allowing two
> processes to acquire the mutex at the same time.

> **[]{#Exercise 3.47 label="Exercise 3.47"}Exercise 3.47:** A semaphore
> (of size $n$) is a generalization of a mutex. Like a mutex, a
> semaphore supports acquire and release operations, but it is more
> general in that up to $n$ processes can acquire it concurrently.
> Additional processes that attempt to acquire the semaphore must wait
> for release operations. Give implementations of semaphores
>
> a.  in terms of mutexes
>
> b.  in terms of atomic `test/and/set!` operations.

#### Deadlock {#deadlock .unnumbered}

Now that we have seen how to implement serializers, we can see that
account exchanging still has a problem, even with the
`serialized/exchange` procedure above. Imagine that Peter attempts to
exchange $a$`<!-- -->`{=html}1 with $a$`<!-- -->`{=html}2 while Paul
concurrently attempts to exchange $a$`<!-- -->`{=html}2 with
$a$`<!-- -->`{=html}1. Suppose that Peter's process reaches the point
where it has entered a serialized procedure protecting
$a$`<!-- -->`{=html}1 and, just after that, Paul's process enters a
serialized procedure protecting $a$`<!-- -->`{=html}2. Now Peter cannot
proceed (to enter a serialized procedure protecting
$a$`<!-- -->`{=html}2) until Paul exits the serialized procedure
protecting $a$`<!-- -->`{=html}2. Similarly, Paul cannot proceed until
Peter exits the serialized procedure protecting $a$`<!-- -->`{=html}1.
Each process is stalled forever, waiting for the other. This situation
is called a *deadlock*. Deadlock is always a danger in systems that
provide concurrent access to multiple shared resources.

One way to avoid the deadlock in this situation is to give each account
a unique identification number and rewrite `serialized/exchange` so that
a process will always attempt to enter a procedure protecting the
lowest-numbered account first. Although this method works well for the
exchange problem, there are other situations that require more
sophisticated deadlock-avoidance techniques, or where deadlock cannot be
avoided at all. (See [Exercise 3.48](#Exercise 3.48) and [Exercise
3.49](#Exercise 3.49).)[^176]

> **[]{#Exercise 3.48 label="Exercise 3.48"}Exercise 3.48:** Explain in
> detail why the deadlock-avoidance method described above, (i.e., the
> accounts are numbered, and each process attempts to acquire the
> smaller-numbered account first) avoids deadlock in the exchange
> problem. Rewrite `serialized/exchange` to incorporate this idea. (You
> will also need to modify `make/account` so that each account is
> created with a number, which can be accessed by sending an appropriate
> message.)

> **[]{#Exercise 3.49 label="Exercise 3.49"}Exercise 3.49:** Give a
> scenario where the deadlock-avoidance mechanism described above does
> not work. (Hint: In the exchange problem, each process knows in
> advance which accounts it will need to get access to. Consider a
> situation where a process must get access to some shared resources
> before it can know which additional shared resources it will require.)

#### Concurrency, time, and communication {#concurrency-time-and-communication .unnumbered}

We've seen how programming concurrent systems requires controlling the
ordering of events when different processes access shared state, and
we've seen how to achieve this control through judicious use of
serializers. But the problems of concurrency lie deeper than this,
because, from a fundamental point of view, it's not always clear what is
meant by "shared state."

Mechanisms such as `test/and/set!` require processes to examine a global
shared flag at arbitrary times. This is problematic and inefficient to
implement in modern high-speed processors, where due to optimization
techniques such as pipelining and cached memory, the contents of memory
may not be in a consistent state at every instant. In contemporary
multiprocessing systems, therefore, the serializer paradigm is being
supplanted by new approaches to concurrency control.[^177]

The problematic aspects of shared state also arise in large, distributed
systems. For instance, imagine a distributed banking system where
individual branch banks maintain local values for bank balances and
periodically compare these with values maintained by other branches. In
such a system the value of "the account balance" would be undetermined,
except right after synchronization. If Peter deposits money in an
account he holds jointly with Paul, when should we say that the account
balance has changed---when the balance in the local branch changes, or
not until after the synchronization? And if Paul accesses the account
from a different branch, what are the reasonable constraints to place on
the banking system such that the behavior is "correct"? The only thing
that might matter for correctness is the behavior observed by Peter and
Paul individually and the "state" of the account immediately after
synchronization. Questions about the "real" account balance or the order
of events between synchronizations may be irrelevant or
meaningless.[^178]

The basic phenomenon here is that synchronizing different processes,
establishing shared state, or imposing an order on events requires
communication among the processes. In essence, any notion of time in
concurrency control must be intimately tied to communication.[^179] It
is intriguing that a similar connection between time and communication
also arises in the Theory of Relativity, where the speed of light (the
fastest signal that can be used to synchronize events) is a fundamental
constant relating time and space. The complexities we encounter in
dealing with time and state in our computational models may in fact
mirror a fundamental complexity of the physical universe.

## Streams {#Section 3.5}

We've gained a good understanding of assignment as a tool in modeling,
as well as an appreciation of the complex problems that assignment
raises. It is time to ask whether we could have gone about things in a
different way, so as to avoid some of these problems. In this section,
we explore an alternative approach to modeling state, based on data
structures called *streams*. As we shall see, streams can mitigate some
of the complexity of modeling state.

Let's step back and review where this complexity comes from. In an
attempt to model real-world phenomena, we made some apparently
reasonable decisions: We modeled real-world objects with local state by
computational objects with local variables. We identified time variation
in the real world with time variation in the computer. We implemented
the time variation of the states of the model objects in the computer
with assignments to the local variables of the model objects.

Is there another approach? Can we avoid identifying time in the computer
with time in the modeled world? Must we make the model change with time
in order to model phenomena in a changing world? Think about the issue
in terms of mathematical functions. We can describe the time-varying
behavior of a quantity $x$ as a function of time $x(t)$. If we
concentrate on $x$ instant by instant, we think of it as a changing
quantity. Yet if we concentrate on the entire time history of values, we
do not emphasize change---the function itself does not change.[^180]

If time is measured in discrete steps, then we can model a time function
as a (possibly infinite) sequence. In this section, we will see how to
model change in terms of sequences that represent the time histories of
the systems being modeled. To accomplish this, we introduce new data
structures called *streams*. From an abstract point of view, a stream is
simply a sequence. However, we will find that the straightforward
implementation of streams as lists (as in [Section
2.2.1](#Section 2.2.1)) doesn't fully reveal the power of stream
processing. As an alternative, we introduce the technique of *delayed
evaluation*, which enables us to represent very large (even infinite)
sequences as streams.

Stream processing lets us model systems that have state without ever
using assignment or mutable data. This has important implications, both
theoretical and practical, because we can build models that avoid the
drawbacks inherent in introducing assignment. On the other hand, the
stream framework raises difficulties of its own, and the question of
which modeling technique leads to more modular and more easily
maintained systems remains open.

### Streams Are Delayed Lists {#Section 3.5.1}

As we saw in [Section 2.2.3](#Section 2.2.3), sequences can serve as
standard interfaces for combining program modules. We formulated
powerful abstractions for manipulating sequences, such as `map`,
`filter`, and `accumulate`, that capture a wide variety of operations in
a manner that is both succinct and elegant.

Unfortunately, if we represent sequences as lists, this elegance is
bought at the price of severe inefficiency with respect to both the time
and space required by our computations. When we represent manipulations
on sequences as transformations of lists, our programs must construct
and copy data structures (which may be huge) at every step of a process.

To see why this is true, let us compare two programs for computing the
sum of all the prime numbers in an interval. The first program is
written in standard iterative style:[^181]

::: scheme
(define (sum-primes a b) (define (iter count accum) (cond ((\> count b)
accum) ((prime? count) (iter (+ count 1) (+ count accum))) (else (iter
(+ count 1) accum)))) (iter a 0))
:::

The second program performs the same computation using the sequence
operations of [Section 2.2.3](#Section 2.2.3):

::: scheme
(define (sum-primes a b) (accumulate + 0 (filter prime?
(enumerate-interval a b))))
:::

In carrying out the computation, the first program needs to store only
the sum being accumulated. In contrast, the filter in the second program
cannot do any testing until `enumerate/interval` has constructed a
complete list of the numbers in the interval. The filter generates
another list, which in turn is passed to `accumulate` before being
collapsed to form a sum. Such large intermediate storage is not needed
by the first program, which we can think of as enumerating the interval
incrementally, adding each prime to the sum as it is generated.

The inefficiency in using lists becomes painfully apparent if we use the
sequence paradigm to compute the second prime in the interval from
10,000 to 1,000,000 by evaluating the expression

::: scheme
(car (cdr (filter prime? (enumerate-interval 10000 1000000))))
:::

This expression does find the second prime, but the computational
overhead is outrageous. We construct a list of almost a million
integers, filter this list by testing each element for primality, and
then ignore almost all of the result. In a more traditional programming
style, we would interleave the enumeration and the filtering, and stop
when we reached the second prime.

Streams are a clever idea that allows one to use sequence manipulations
without incurring the costs of manipulating sequences as lists. With
streams we can achieve the best of both worlds: We can formulate
programs elegantly as sequence manipulations, while attaining the
efficiency of incremental computation. The basic idea is to arrange to
construct a stream only partially, and to pass the partial construction
to the program that consumes the stream. If the consumer attempts to
access a part of the stream that has not yet been constructed, the
stream will automatically construct just enough more of itself to
produce the required part, thus preserving the illusion that the entire
stream exists. In other words, although we will write programs as if we
were processing complete sequences, we design our stream implementation
to automatically and transparently interleave the construction of the
stream with its use.

On the surface, streams are just lists with different names for the
procedures that manipulate them. There is a constructor, `cons/stream`,
and two selectors, `stream/car` and `stream/cdr`, which satisfy the
constraints

::: scheme
(stream-car (cons-stream x y)) = x (stream-cdr (cons-stream x y)) = y
:::

There is a distinguishable object, `the/empty/stream`, which cannot be
the result of any `cons/stream` operation, and which can be identified
with the predicate `stream/null?`.[^182] Thus we can make and use
streams, in just the same way as we can make and use lists, to represent
aggregate data arranged in a sequence. In particular, we can build
stream analogs of the list operations from [Chapter 2](#Chapter 2), such
as `list/ref`, `map`, and `for/each`:[^183]

::: scheme
(define (stream-ref s n) (if (= n 0) (stream-car s) (stream-ref
(stream-cdr s) (- n 1)))) (define (stream-map proc s) (if (stream-null?
s) the-empty-stream (cons-stream (proc (stream-car s)) (stream-map proc
(stream-cdr s))))) (define (stream-for-each proc s) (if (stream-null? s)
'done (begin (proc (stream-car s)) (stream-for-each proc (stream-cdr
s)))))
:::

`stream/for/each` is useful for viewing streams:

::: scheme
(define (display-stream s) (stream-for-each display-line s)) (define
(display-line x) (newline) (display x))
:::

To make the stream implementation automatically and transparently
interleave the construction of a stream with its use, we will arrange
for the `cdr` of a stream to be evaluated when it is accessed by the
`stream/cdr` procedure rather than when the stream is constructed by
`cons/stream`. This implementation choice is reminiscent of our
discussion of rational numbers in [Section 2.1.2](#Section 2.1.2), where
we saw that we can choose to implement rational numbers so that the
reduction of numerator and denominator to lowest terms is performed
either at construction time or at selection time. The two
rational-number implementations produce the same data abstraction, but
the choice has an effect on efficiency. There is a similar relationship
between streams and ordinary lists. As a data abstraction, streams are
the same as lists. The difference is the time at which the elements are
evaluated. With ordinary lists, both the `car` and the `cdr` are
evaluated at construction time. With streams, the `cdr` is evaluated at
selection time.

Our implementation of streams will be based on a special form called
`delay`. Evaluating `(delay `$\langle$*`exp`*$\rangle$`)` does not
evaluate the expression $\langle$*exp*$\kern0.08em\rangle$, but rather
returns a so-called *delayed object*, which we can think of as a
"promise" to evaluate $\langle$*exp*$\kern0.08em\rangle$ at some future
time. As a companion to `delay`, there is a procedure called `force`
that takes a delayed object as argument and performs the evaluation---in
effect, forcing the `delay` to fulfill its promise. We will see below
how `delay` and `force` can be implemented, but first let us use these
to construct streams.

`cons/stream` is a special form defined so that

::: scheme
(cons-stream
 $\color{SchemeDark}\langle$  *a*  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *b*  $\color{SchemeDark}\rangle$ )
:::

is equivalent to

::: scheme
(cons  $\color{SchemeDark}\langle$  *a*  $\color{SchemeDark}\rangle$
(delay  $\color{SchemeDark}\langle$  *b*  $\color{SchemeDark}\rangle$ ))
:::

What this means is that we will construct streams using pairs. However,
rather than placing the value of the rest of the stream into the `cdr`
of the pair we will put there a promise to compute the rest if it is
ever requested. `stream/car` and `stream/cdr` can now be defined as
procedures:

::: scheme
(define (stream-car stream) (car stream)) (define (stream-cdr stream)
(force (cdr stream)))
:::

`stream/car` selects the `car` of the pair; `stream/cdr` selects the
`cdr` of the pair and evaluates the delayed expression found there to
obtain the rest of the stream.[^184]

#### The stream implementation in action {#the-stream-implementation-in-action .unnumbered}

To see how this implementation behaves, let us analyze the "outrageous"
prime computation we saw above, reformulated in terms of streams:

::: scheme
(stream-car (stream-cdr (stream-filter prime? (stream-enumerate-interval
10000 1000000))))
:::

We will see that it does indeed work efficiently.

We begin by calling `stream/enumerate/interval` with the arguments
10,000 and 1,000,000. `stream/enumerate/interval` is the stream analog
of `enumerate/interval` ([Section 2.2.3](#Section 2.2.3)):

::: scheme
(define (stream-enumerate-interval low high) (if (\> low high)
the-empty-stream (cons-stream low (stream-enumerate-interval (+ low 1)
high))))
:::

and thus the result returned by `stream/enumerate/interval`, formed by
the `cons/stream`, is[^185]

::: scheme
(cons 10000 (delay (stream-enumerate-interval 10001 1000000)))
:::

That is, `stream/enumerate/interval` returns a stream represented as a
pair whose `car` is 10,000 and whose `cdr` is a promise to enumerate
more of the interval if so requested. This stream is now filtered for
primes, using the stream analog of the `filter` procedure ([Section
2.2.3](#Section 2.2.3)):

::: scheme
(define (stream-filter pred stream) (cond ((stream-null? stream)
the-empty-stream) ((pred (stream-car stream)) (cons-stream (stream-car
stream) (stream-filter pred (stream-cdr stream)))) (else (stream-filter
pred (stream-cdr stream)))))
:::

`stream/filter` tests the `stream/car` of the stream (the `car` of the
pair, which is 10,000). Since this is not prime, `stream/filter`
examines the `stream/cdr` of its input stream. The call to `stream/cdr`
forces evaluation of the delayed `stream/enumerate/interval`, which now
returns

::: scheme
(cons 10001 (delay (stream-enumerate-interval 10002 1000000)))
:::

`stream/filter` now looks at the `stream/car` of this stream, 10,001,
sees that this is not prime either, forces another `stream/cdr`, and so
on, until `stream/enumerate/interval` yields the prime 10,007, whereupon
`stream/filter`, according to its definition, returns

::: scheme
(cons-stream (stream-car stream) (stream-filter pred (stream-cdr
stream)))
:::

which in this case is

::: scheme
(cons 10007 (delay (stream-filter prime? (cons 10008 (delay
(stream-enumerate-interval 10009 1000000))))))
:::

This result is now passed to `stream/cdr` in our original expression.
This forces the delayed `stream/filter`, which in turn keeps forcing the
delayed `stream/enumerate/interval` until it finds the next prime, which
is 10,009. Finally, the result passed to `stream/car` in our original
expression is

::: scheme
(cons 10009 (delay (stream-filter prime? (cons 10010 (delay
(stream-enumerate-interval 10011 1000000))))))
:::

`stream/car` returns 10,009, and the computation is complete. Only as
many integers were tested for primality as were necessary to find the
second prime, and the interval was enumerated only as far as was
necessary to feed the prime filter.

In general, we can think of delayed evaluation as "demand-driven"
programming, whereby each stage in the stream process is activated only
enough to satisfy the next stage. What we have done is to decouple the
actual order of events in the computation from the apparent structure of
our procedures. We write procedures as if the streams existed "all at
once" when, in reality, the computation is performed incrementally, as
in traditional programming styles.

#### Implementing `delay` and `force` {#implementing-delay-and-force .unnumbered}

Although `delay` and `force` may seem like mysterious operations, their
implementation is really quite straightforward. `delay` must package an
expression so that it can be evaluated later on demand, and we can
accomplish this simply by treating the expression as the body of a
procedure. `delay` can be a special form such that

::: scheme
(delay
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}\rangle$ )
:::

is syntactic sugar for

::: scheme
(lambda ()
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}\rangle$ )
:::

`force` simply calls the procedure (of no arguments) produced by
`delay`, so we can implement `force` as a procedure:

::: scheme
(define (force delayed-object) (delayed-object))
:::

This implementation suffices for `delay` and `force` to work as
advertised, but there is an important optimization that we can include.
In many applications, we end up forcing the same delayed object many
times. This can lead to serious inefficiency in recursive programs
involving streams. (See [Exercise 3.57](#Exercise 3.57).) The solution
is to build delayed objects so that the first time they are forced, they
store the value that is computed. Subsequent forcings will simply return
the stored value without repeating the computation. In other words, we
implement `delay` as a special-purpose memoized procedure similar to the
one described in [Exercise 3.27](#Exercise 3.27). One way to accomplish
this is to use the following procedure, which takes as argument a
procedure (of no arguments) and returns a memoized version of the
procedure. The first time the memoized procedure is run, it saves the
computed result. On subsequent evaluations, it simply returns the
result.

::: scheme
(define (memo-proc proc) (let ((already-run? false) (result false))
(lambda () (if (not already-run?) (begin (set! result (proc)) (set!
already-run? true) result) result))))
:::

`delay` is then defined so that `(delay `$\langle$*`exp`*$\rangle$`)` is
equivalent to

::: scheme
(memo-proc (lambda ()
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}\rangle$ ))
:::

and `force` is as defined previously.[^186]

> **[]{#Exercise 3.50 label="Exercise 3.50"}Exercise 3.50:** Complete
> the following definition, which generalizes `stream/map` to allow
> procedures that take multiple arguments, analogous to `map` in
> [Section 2.2.1](#Section 2.2.1), [Footnote 12](#Footnote 12).
>
> ::: scheme
> (define (stream-map proc . argstreams) (if
> ( $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$  (car
> argstreams)) the-empty-stream
> ( $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$  (apply
> proc (map  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
> argstreams)) (apply stream-map (cons proc (map
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
> argstreams))))))
> :::

> **[]{#Exercise 3.51 label="Exercise 3.51"}Exercise 3.51:** In order to
> take a closer look at delayed evaluation, we will use the following
> procedure, which simply returns its argument after printing it:
>
> ::: scheme
> (define (show x) (display-line x) x)
> :::
>
> What does the interpreter print in response to evaluating each
> expression in the following sequence?[^187]
>
> ::: scheme
> (define x (stream-map show (stream-enumerate-interval 0 10)))
> (stream-ref x 5) (stream-ref x 7)
> :::

> **[]{#Exercise 3.52 label="Exercise 3.52"}Exercise 3.52:** Consider
> the sequence of expressions
>
> ::: scheme
> (define sum 0) (define (accum x) (set! sum (+ x sum)) sum) (define seq
> (stream-map accum (stream-enumerate-interval 1 20))) (define y
> (stream-filter even? seq)) (define z (stream-filter (lambda (x) (=
> (remainder x 5) 0)) seq)) (stream-ref y 7) (display-stream z)
> :::
>
> What is the value of `sum` after each of the above expressions is
> evaluated? What is the printed response to evaluating the `stream/ref`
> and `display/stream` expressions? Would these responses differ if we
> had implemented `(delay `$\langle$*`exp`*$\rangle$`)` simply as
> `(lambda () `$\langle$*`exp`*$\rangle$`)` without using the
> optimization provided by `memo/proc`? Explain.

### Infinite Streams {#Section 3.5.2}

We have seen how to support the illusion of manipulating streams as
complete entities even though, in actuality, we compute only as much of
the stream as we need to access. We can exploit this technique to
represent sequences efficiently as streams, even if the sequences are
very long. What is more striking, we can use streams to represent
sequences that are infinitely long. For instance, consider the following
definition of the stream of positive integers:

::: scheme
(define (integers-starting-from n) (cons-stream n
(integers-starting-from (+ n 1)))) (define integers
(integers-starting-from 1))
:::

This makes sense because `integers` will be a pair whose `car` is 1 and
whose `cdr` is a promise to produce the integers beginning with 2. This
is an infinitely long stream, but in any given time we can examine only
a finite portion of it. Thus, our programs will never know that the
entire infinite stream is not there.

Using `integers` we can define other infinite streams, such as the
stream of integers that are not divisible by 7:

::: scheme
(define (divisible? x y) (= (remainder x y) 0)) (define no-sevens
(stream-filter (lambda (x) (not (divisible? x 7))) integers))
:::

Then we can find integers not divisible by 7 simply by accessing
elements of this stream:

::: scheme
(stream-ref no-sevens 100)  *117*
:::

In analogy with `integers`, we can define the infinite stream of
Fibonacci numbers:

::: scheme
(define (fibgen a b) (cons-stream a (fibgen b (+ a b)))) (define fibs
(fibgen 0 1))
:::

`fibs` is a pair whose `car` is 0 and whose `cdr` is a promise to
evaluate `(fibgen 1 1)`. When we evaluate this delayed `(fibgen 1 1)`,
it will produce a pair whose `car` is 1 and whose `cdr` is a promise to
evaluate `(fibgen 1 2)`, and so on.

For a look at a more exciting infinite stream, we can generalize the
`no/sevens` example to construct the infinite stream of prime numbers,
using a method known as the *sieve of Eratosthenes*.[^188] We start with
the integers beginning with 2, which is the first prime. To get the rest
of the primes, we start by filtering the multiples of 2 from the rest of
the integers. This leaves a stream beginning with 3, which is the next
prime. Now we filter the multiples of 3 from the rest of this stream.
This leaves a stream beginning with 5, which is the next prime, and so
on. In other words, we construct the primes by a sieving process,
described as follows: To sieve a stream `S`, form a stream whose first
element is the first element of `S` and the rest of which is obtained by
filtering all multiples of the first element of `S` out of the rest of
`S` and sieving the result. This process is readily described in terms
of stream operations:

::: scheme
(define (sieve stream) (cons-stream (stream-car stream) (sieve
(stream-filter (lambda (x) (not (divisible? x (stream-car stream))))
(stream-cdr stream))))) (define primes (sieve (integers-starting-from
2)))
:::

Now to find a particular prime we need only ask for it:

::: scheme
(stream-ref primes 50)  *233*
:::

It is interesting to contemplate the signal-processing system set up by
`sieve`, shown in the "Henderson diagram" in [Figure
3.31](#Figure 3.31).[^189] The input stream feeds into an "un`cons`er"
that separates the first element of the stream from the rest of the
stream. The first element is used to construct a divisibility filter,
through which the rest is passed, and the output of the filter is fed to
another sieve box. Then the original first element is `cons`ed onto the
output of the internal sieve to form the output stream. Thus, not only
is the stream infinite, but the signal processor is also infinite,
because the sieve contains a sieve within it.

[]{#Figure 3.31 label="Figure 3.31"}

![image](fig/chap3/Fig3.31.pdf){width="111mm"}

> **Figure 3.31:** The prime sieve viewed as a signal-processing system.

#### Defining streams implicitly {#defining-streams-implicitly .unnumbered}

The `integers` and `fibs` streams above were defined by specifying
"generating" procedures that explicitly compute the stream elements one
by one. An alternative way to specify streams is to take advantage of
delayed evaluation to define streams implicitly. For example, the
following expression defines the stream `ones` to be an infinite stream
of ones:

::: scheme
(define ones (cons-stream 1 ones))
:::

This works much like the definition of a recursive procedure: `ones` is
a pair whose `car` is 1 and whose `cdr` is a promise to evaluate `ones`.
Evaluating the `cdr` gives us again a 1 and a promise to evaluate
`ones`, and so on.

We can do more interesting things by manipulating streams with
operations such as `add/streams`, which produces the elementwise sum of
two given streams:[^190]

::: scheme
(define (add-streams s1 s2) (stream-map + s1 s2))
:::

Now we can define the integers as follows:

::: scheme
(define integers (cons-stream 1 (add-streams ones integers)))
:::

This defines `integers` to be a stream whose first element is 1 and the
rest of which is the sum of `ones` and `integers`. Thus, the second
element of `integers` is 1 plus the first element of `integers`, or 2;
the third element of `integers` is 1 plus the second element of
`integers`, or 3; and so on. This definition works because, at any
point, enough of the `integers` stream has been generated so that we can
feed it back into the definition to produce the next integer.

We can define the Fibonacci numbers in the same style:

::: scheme
(define fibs (cons-stream 0 (cons-stream 1 (add-streams (stream-cdr
fibs) fibs))))
:::

This definition says that `fibs` is a stream beginning with 0 and 1,
such that the rest of the stream can be generated by adding `fibs` to
itself shifted by one place:

::: scheme
1 1 2 3 5 8 13 21  $\dots$  =  `(stream/cdr fibs)`  0 1 1 2 3 5 8 13
 $\dots$  =  `fibs`  0 1 1 2 3 5 8 13 21 34  $\dots$  =  `fibs`
:::

`scale/stream` is another useful procedure in formulating such stream
definitions. This multiplies each item in a stream by a given constant:

::: scheme
(define (scale-stream stream factor) (stream-map (lambda (x) (\* x
factor)) stream))
:::

For example,

::: scheme
(define double (cons-stream 1 (scale-stream double 2)))
:::

produces the stream of powers of 2: 1, 2, 4, 8, 16, 32, $\dots$.

An alternate definition of the stream of primes can be given by starting
with the integers and filtering them by testing for primality. We will
need the first prime, 2, to get started:

::: scheme
(define primes (cons-stream 2 (stream-filter prime?
(integers-starting-from 3))))
:::

This definition is not so straightforward as it appears, because we will
test whether a number $n$ is prime by checking whether $n$ is divisible
by a prime (not by just any integer) less than or equal to $\sqrt{n}$:

::: scheme
(define (prime? n) (define (iter ps) (cond ((\> (square (stream-car ps))
n) true) ((divisible? n (stream-car ps)) false) (else (iter (stream-cdr
ps))))) (iter primes))
:::

This is a recursive definition, since `primes` is defined in terms of
the `prime?` predicate, which itself uses the `primes` stream. The
reason this procedure works is that, at any point, enough of the
`primes` stream has been generated to test the primality of the numbers
we need to check next. That is, for every $n$ we test for primality,
either $n$ is not prime (in which case there is a prime already
generated that divides it) or $n$ is prime (in which case there is a
prime already generated---i.e., a prime less than $n$---that is greater
than $\sqrt{n}$).[^191]

> **[]{#Exercise 3.53 label="Exercise 3.53"}Exercise 3.53:** Without
> running the program, describe the elements of the stream defined by
>
> ::: scheme
> (define s (cons-stream 1 (add-streams s s)))
> :::

> **[]{#Exercise 3.54 label="Exercise 3.54"}Exercise 3.54:** Define a
> procedure `mul/streams`, analogous to `add/streams`, that produces the
> elementwise product of its two input streams. Use this together with
> the stream of `integers` to complete the following definition of the
> stream whose $n^{\mathrm{th}}$ element (counting from 0) is $n + 1$
> factorial:
>
> ::: scheme
> (define factorials (cons-stream 1 (mul-streams
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ )))
> :::

> **[]{#Exercise 3.55 label="Exercise 3.55"}Exercise 3.55:** Define a
> procedure `partial/sums` that takes as argument a stream $S$ and
> returns the stream whose elements are $S_0$, $S_0 + S_1$,
> $S_0 + S_1 + S_2, \dots$. For example, `(partial/sums integers)`
> should be the stream 1, 3, 6, 10, 15, $\dots$.

> **[]{#Exercise 3.56 label="Exercise 3.56"}Exercise 3.56:** A famous
> problem, first raised by R. Hamming, is to enumerate, in ascending
> order with no repetitions, all positive integers with no prime factors
> other than 2, 3, or 5. One obvious way to do this is to simply test
> each integer in turn to see whether it has any factors other than 2,
> 3, and 5. But this is very inefficient, since, as the integers get
> larger, fewer and fewer of them fit the requirement. As an
> alternative, let us call the required stream of numbers `S` and notice
> the following facts about it.
>
> -   `S` begins with 1.
>
> -   The elements of `(scale/stream S 2)` are also elements of `S`.
>
> -   The same is true for `(scale/stream S 3)` and
>     `(scale/stream 5 S)`.
>
> -   These are all the elements of `S`.
>
> Now all we have to do is combine elements from these sources. For this
> we define a procedure `merge` that combines two ordered streams into
> one ordered result stream, eliminating repetitions:
>
> ::: scheme
> (define (merge s1 s2) (cond ((stream-null? s1) s2) ((stream-null? s2)
> s1) (else (let ((s1car (stream-car s1)) (s2car (stream-car s2))) (cond
> ((\< s1car s2car) (cons-stream s1car (merge (stream-cdr s1) s2))) ((\>
> s1car s2car) (cons-stream s2car (merge s1 (stream-cdr s2)))) (else
> (cons-stream s1car (merge (stream-cdr s1) (stream-cdr s2)))))))))
> :::
>
> Then the required stream may be constructed with `merge`, as follows:
>
> ::: scheme
> (define S (cons-stream 1 (merge
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ )))
> :::
>
> Fill in the missing expressions in the places marked
> $\langle$??$\kern0.08em\rangle$ above.

> **[]{#Exercise 3.57 label="Exercise 3.57"}Exercise 3.57:** How many
> additions are performed when we compute the $n^{\mathrm{th}}$
> Fibonacci number using the definition of `fibs` based on the
> `add/streams` procedure? Show that the number of additions would be
> exponentially greater if we had implemented
> `(delay `$\langle$*`exp`*$\rangle$`)` simply as
> `(lambda () `$\langle$*`exp`*$\rangle$`)`, without using the
> optimization provided by the `memo/proc` procedure described in
> [Section 3.5.1](#Section 3.5.1).[^192]

> **[]{#Exercise 3.58 label="Exercise 3.58"}Exercise 3.58:** Give an
> interpretation of the stream computed by the following procedure:
>
> ::: scheme
> (define (expand num den radix) (cons-stream (quotient (\* num radix)
> den) (expand (remainder (\* num radix) den) den radix)))
> :::
>
> (`quotient` is a primitive that returns the integer quotient of two
> integers.) What are the successive elements produced by
> `(expand 1 7 10)`? What is produced by `(expand 3 8 10)`?

> **[]{#Exercise 3.59 label="Exercise 3.59"}Exercise 3.59:** In [Section
> 2.5.3](#Section 2.5.3) we saw how to implement a polynomial arithmetic
> system representing polynomials as lists of terms. In a similar way,
> we can work with *power series*, such as
>
> $$e^x = 1 + x + \displaystyle\frac{x^2}{2} + \displaystyle\frac{x^3}{3 \cdot 2} + \displaystyle\frac{x^4}{4 \cdot 3 \cdot 2} + \dots,$$
>
> $$\cos x = 1 - \displaystyle\frac{x^2}{2} + \displaystyle\frac{x^4}{4 \cdot 3 \cdot 2} - \dots,$$
>
> $$\sin x = x - \displaystyle\frac{x^3}{3 \cdot 2} + \displaystyle\frac{x^5}{5 \cdot 4 \cdot 3 \cdot 2} - \dots$$
>
> represented as infinite streams. We will represent the series $a_0 +
> a_1 x + a_2 x^2 + a_3 x^3 + \dots$ as the stream whose elements are
> the coefficients $a_0$, $a_1$, $a_2$, $a_3$, $\dots$.
>
> a.  The integral of the series
>     $a_0 + a_1 x + a_2 x^2 + a_3 x^3 + \dots$ is the series
>
>     $$c + a_0 x + {1\over2} a_1 x^2 + {1\over3} a_2 x^3 + {1\over4} a_3 x^4 + \dots,$$
>
>     where $c$ is any constant. Define a procedure `integrate/series`
>     that takes as input a stream $a_0$, $a_1$, $a_2$, $\dots$
>     representing a power series and returns the stream $a_0$,
>     ${1\over2}a_1$, ${1\over3}a_2$, $\dots$ of coefficients of the
>     non-constant terms of the integral of the series. (Since the
>     result has no constant term, it doesn't represent a power series;
>     when we use `integrate/series`, we will `cons` on the appropriate
>     constant.)
>
> b.  The function $x \mapsto e^x$ is its own derivative. This implies
>     that $e^x$ and the integral of $e^x$ are the same series, except
>     for the constant term, which is $e^0 = 1$. Accordingly, we can
>     generate the series for $e^x$ as
>
>     ::: scheme
>     (define exp-series (cons-stream 1 (integrate-series exp-series)))
>     :::
>
>     Show how to generate the series for sine and cosine, starting from
>     the facts that the derivative of sine is cosine and the derivative
>     of cosine is the negative of sine:
>
>     ::: scheme
>     (define cosine-series (cons-stream 1
>      $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ ))
>     (define sine-series (cons-stream 0
>      $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ ))
>     :::

> **[]{#Exercise 3.60 label="Exercise 3.60"}Exercise 3.60:** With power
> series represented as streams of coefficients as in [Exercise
> 3.59](#Exercise 3.59), adding series is implemented by `add/streams`.
> Complete the definition of the following procedure for multiplying
> series:
>
> ::: scheme
> (define (mul-series s1 s2) (cons-stream
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
> (add-streams
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ )))
> :::
>
> You can test your procedure by verifying that
> $\sin^2\!x + \cos^2\!x = 1$, using the series from [Exercise
> 3.59](#Exercise 3.59).

> **[]{#Exercise 3.61 label="Exercise 3.61"}Exercise 3.61:** Let $S$ be
> a power series ([Exercise 3.59](#Exercise 3.59)) whose constant term
> is 1. Suppose we want to find the power series $1 / S$, that is, the
> series $X$ such that $SX = 1$. Write $S = 1 + S_R$ where $S_R$ is the
> part of $S$ after the constant term. Then we can solve for $X$ as
> follows:
>
> $$\begin{array}{r@{{}={}}l}
>             S \cdot X   & 1, \\
>     (1 + S_R) \cdot X   & 1, \\
>       X + S_R \cdot X   & 1, \\
>                     X   & 1 - S_R \cdot X.
> \end{array}$$
>
> In other words, $X$ is the power series whose constant term is 1 and
> whose higher-order terms are given by the negative of $S_R$ times $X$.
> Use this idea to write a procedure `invert/unit/series` that computes
> $1 / S$ for a power series $S$ with constant term 1. You will need to
> use `mul/series` from [Exercise 3.60](#Exercise 3.60).

> **[]{#Exercise 3.62 label="Exercise 3.62"}Exercise 3.62:** Use the
> results of [Exercise 3.60](#Exercise 3.60) and [Exercise
> 3.61](#Exercise 3.61) to define a procedure `div/series` that divides
> two power series. `div/series` should work for any two series,
> provided that the denominator series begins with a nonzero constant
> term. (If the denominator has a zero constant term, then `div/series`
> should signal an error.) Show how to use `div/series` together with
> the result of [Exercise 3.59](#Exercise 3.59) to generate the power
> series for tangent.

### Exploiting the Stream Paradigm {#Section 3.5.3}

Streams with delayed evaluation can be a powerful modeling tool,
providing many of the benefits of local state and assignment. Moreover,
they avoid some of the theoretical tangles that accompany the
introduction of assignment into a programming language.

The stream approach can be illuminating because it allows us to build
systems with different module boundaries than systems organized around
assignment to state variables. For example, we can think of an entire
time series (or signal) as a focus of interest, rather than the values
of the state variables at individual moments. This makes it convenient
to combine and compare components of state from different moments.

#### Formulating iterations as stream processes {#formulating-iterations-as-stream-processes .unnumbered}

In [Section 1.2.1](#Section 1.2.1), we introduced iterative processes,
which proceed by updating state variables. We know now that we can
represent state as a "timeless" stream of values rather than as a set of
variables to be updated. Let's adopt this perspective in revisiting the
square-root procedure from [Section 1.1.7](#Section 1.1.7). Recall that
the idea is to generate a sequence of better and better guesses for the
square root of $x$ by applying over and over again the procedure that
improves guesses:

::: scheme
(define (sqrt-improve guess x) (average guess (/ x guess)))
:::

In our original `sqrt` procedure, we made these guesses be the
successive values of a state variable. Instead we can generate the
infinite stream of guesses, starting with an initial guess of 1:[^193]

::: scheme
(define (sqrt-stream x) (define guesses (cons-stream 1.0 (stream-map
(lambda (guess) (sqrt-improve guess x)) guesses))) guesses)

(display-stream (sqrt-stream 2))  *1.*   *1.5*   *1.4166666666666665*
 *1.4142156862745097*   *1.4142135623746899*   $\dots$
:::

We can generate more and more terms of the stream to get better and
better guesses. If we like, we can write a procedure that keeps
generating terms until the answer is good enough. (See [Exercise
3.64](#Exercise 3.64).)

Another iteration that we can treat in the same way is to generate an
approximation to $\pi$, based upon the alternating series that we saw in
[Section 1.3.1](#Section 1.3.1):

$${\pi\over4} = 1 - {1\over3} + {1\over5} - {1\over7} + \dots.$$

We first generate the stream of summands of the series (the reciprocals
of the odd integers, with alternating signs). Then we take the stream of
sums of more and more terms (using the `partial/sums` procedure of
[Exercise 3.55](#Exercise 3.55)) and scale the result by 4:

::: scheme
(define (pi-summands n) (cons-stream (/ 1.0 n) (stream-map -
(pi-summands (+ n 2))))) (define pi-stream (scale-stream (partial-sums
(pi-summands 1)) 4))

(display-stream pi-stream)  *4.*   *2.666666666666667*
 *3.466666666666667*   *2.8952380952380956*   *3.3396825396825403*
 *2.9760461760461765*   *3.2837384837384844*   *3.017071817071818*
 $\dots$
:::

This gives us a stream of better and better approximations to $\pi$,
although the approximations converge rather slowly. Eight terms of the
sequence bound the value of $\pi$ between 3.284 and 3.017.

So far, our use of the stream of states approach is not much different
from updating state variables. But streams give us an opportunity to do
some interesting tricks. For example, we can transform a stream with a
*sequence accelerator* that converts a sequence of approximations to a
new sequence that converges to the same value as the original, only
faster.

One such accelerator, due to the eighteenth-century Swiss mathematician
Leonhard Euler, works well with sequences that are partial sums of
alternating series (series of terms with alternating signs). In Euler's
technique, if $S_n$ is the $n^{\mathrm{th}}$ term of the original sum
sequence, then the accelerated sequence has terms

$$S_{n+1} - {(S_{n+1} - S_n)^2 \over S_{n-1} - 2S_n + S_{n+1}}\,.$$

Thus, if the original sequence is represented as a stream of values, the
transformed sequence is given by

::: scheme
(define (euler-transform s) (let ((s0 (stream-ref s 0))  [;
$S_{n-1}$]{.roman}  (s1 (stream-ref s 1))  [; $S_n$]{.roman}  (s2
(stream-ref s 2)))  [; $S_{n+1}$]{.roman}  (cons-stream (- s2 (/ (square
(- s2 s1)) (+ s0 (\* -2 s1) s2))) (euler-transform (stream-cdr s)))))
:::

We can demonstrate Euler acceleration with our sequence of
approximations to $\pi$:

::: scheme
(display-stream (euler-transform pi-stream))  *3.166666666666667*
 *3.1333333333333337*   *3.1452380952380956*   *3.13968253968254*
 *3.1427128427128435*   *3.1408813408813416*   *3.142071817071818*
 *3.1412548236077655*   $\dots$
:::

Even better, we can accelerate the accelerated sequence, and recursively
accelerate that, and so on. Namely, we create a stream of streams (a
structure we'll call a *tableau*) in which each stream is the transform
of the preceding one:

::: scheme
(define (make-tableau transform s) (cons-stream s (make-tableau
transform (transform s))))
:::

The tableau has the form

$$\vbox{
\offinterlineskip
\halign{
\strut  \hfil \  #\  \hfil &
    \hfil \  #\  \hfil &
    \hfil \  #\  \hfil &
    \hfil \  #\  \hfil &
    \hfil \  #\  \hfil &
    \hfil \  #\  \hfil \cr

$ s_{00} $  &  $ s_{01} $   &  $ s_{02} $   &  $ s_{03} $   &  $ s_{04} $   &  $ \dots $ \cr
        &  $ s_{10} $   &  $ s_{11} $   &  $ s_{12} $   &  $ s_{13} $   &  $ \dots $ \cr
        &       &  $ s_{20} $   &  $ s_{21} $   &  $ s_{22} $   &  $ \dots $ \cr
        &       &       &  $ \dots $    &       &  \cr }
}$$

Finally, we form a sequence by taking the first term in each row of the
tableau:

::: scheme
(define (accelerated-sequence transform s) (stream-map stream-car
(make-tableau transform s)))
:::

We can demonstrate this kind of "super-acceleration" of the $\pi$
sequence:

::: scheme
(display-stream (accelerated-sequence euler-transform pi-stream))  *4.*
 *3.166666666666667*   *3.142105263157895*   *3.141599357319005*
 *3.1415927140337785*   *3.1415926539752927*   *3.1415926535911765*
 *3.141592653589778*   $\dots$
:::

The result is impressive. Taking eight terms of the sequence yields the
correct value of $\pi$ to 14 decimal places. If we had used only the
original $\pi$ sequence, we would need to compute on the order of
$10^{13}$ terms (i.e., expanding the series far enough so that the
individual terms are less than $10^{-13}$) to get that much accuracy!

We could have implemented these acceleration techniques without using
streams. But the stream formulation is particularly elegant and
convenient because the entire sequence of states is available to us as a
data structure that can be manipulated with a uniform set of operations.

> **[]{#Exercise 3.63 label="Exercise 3.63"}Exercise 3.63:** Louis
> Reasoner asks why the `sqrt/stream` procedure was not written in the
> following more straightforward way, without the local variable
> `guesses`:
>
> ::: scheme
> (define (sqrt-stream x) (cons-stream 1.0 (stream-map (lambda (guess)
> (sqrt-improve guess x)) (sqrt-stream x))))
> :::
>
> Alyssa P. Hacker replies that this version of the procedure is
> considerably less efficient because it performs redundant computation.
> Explain Alyssa's answer. Would the two versions still differ in
> efficiency if our implementation of `delay` used only
> `(lambda () `$\langle$*`exp`*$\rangle$`)` without using the
> optimization provided by `memo/proc` ([Section
> 3.5.1](#Section 3.5.1))?

> **[]{#Exercise 3.64 label="Exercise 3.64"}Exercise 3.64:** Write a
> procedure `stream/limit` that takes as arguments a stream and a number
> (the tolerance). It should examine the stream until it finds two
> successive elements that differ in absolute value by less than the
> tolerance, and return the second of the two elements. Using this, we
> could compute square roots up to a given tolerance by
>
> ::: scheme
> (define (sqrt x tolerance) (stream-limit (sqrt-stream x) tolerance))
> :::

> **[]{#Exercise 3.65 label="Exercise 3.65"}Exercise 3.65:** Use the
> series
>
> $$\ln 2 = 1 - {1\over2} + {1\over3} - {1\over4} + \dots$$
>
> to compute three sequences of approximations to the natural logarithm
> of 2, in the same way we did above for $\pi$. How rapidly do these
> sequences converge?

#### Infinite streams of pairs {#infinite-streams-of-pairs .unnumbered}

In [Section 2.2.3](#Section 2.2.3), we saw how the sequence paradigm
handles traditional nested loops as processes defined on sequences of
pairs. If we generalize this technique to infinite streams, then we can
write programs that are not easily represented as loops, because the
"looping" must range over an infinite set.

For example, suppose we want to generalize the `prime/sum/pairs`
procedure of [Section 2.2.3](#Section 2.2.3) to produce the stream of
pairs of *all* integers $(i, j)$ with $i \le j$ such that $i + j$ is
prime. If `int/pairs` is the sequence of all pairs of integers $(i, j)$
with $i \le j$, then our required stream is simply[^194]

::: scheme
(stream-filter (lambda (pair) (prime? (+ (car pair) (cadr pair))))
int-pairs)
:::

Our problem, then, is to produce the stream `int/pairs`. More generally,
suppose we have two streams $S = (S_i)$ and $T = (T_j)$, and imagine the
infinite rectangular array

$$\vbox{
\offinterlineskip
\halign{
\strut  \hfil \  #\  \hfil &
    \hfil \  #\  \hfil &
    \hfil \  #\  \hfil &
    \hfil \  #\  \hfil \cr

    $ (S_0, T_0) $ & $ (S_0, T_1) $ & $ (S_0, T_2) $ & $ \dots $ \cr
    $ (S_1, T_0) $ & $ (S_1, T_1) $ & $ (S_1, T_2) $ & $ \dots $ \cr
    $ (S_2, T_0) $ & $ (S_2, T_1) $ & $ (S_2, T_2) $ & $ \dots $ \cr
    $ \dots $ & & & \cr }
}$$

We wish to generate a stream that contains all the pairs in the array
that lie on or above the diagonal, i.e., the pairs

$$\vbox{
\offinterlineskip
\halign{
\strut  \hfil \  #\  \hfil &
    \hfil \  #\  \hfil &
    \hfil \  #\  \hfil &
    \hfil \  #\  \hfil \cr

$ (S_0, T_0) $  & $ (S_0, T_1) $    & $ (S_0, T_2) $    & $ \dots $ \cr
        & $ (S_1, T_1) $    & $ (S_1, T_2) $    & $ \dots $ \cr
        &           & $ (S_2, T_2) $    & $ \dots $ \cr
        &           &           & $ \dots $ \cr }
}$$

(If we take both $S$ and $T$ to be the stream of integers, then this
will be our desired stream `int/pairs`.)

Call the general stream of pairs `(pairs S T)`, and consider it to be
composed of three parts: the pair $(S_0, T_0)$, the rest of the pairs in
the first row, and the remaining pairs:[^195]

$$\vbox{
\offinterlineskip
\halign{
\strut  \hfil \  #\  \hfil & \vrule
    \hfil \  #\  \hfil &
    \hfil \  #\  \hfil &
    \hfil \  #\  \hfil \cr

$ (S_0, T_0) $  & $ (S_0, T_1) $    & $ (S_0, T_2) $    & $ \dots $ \cr
\noalign{\hrule}
        & $ (S_1, T_1) $    & $ (S_1, T_2) $    & $ \dots $ \cr
        &           & $ (S_2, T_2) $    & $ \dots $ \cr
        &           &           & $ \dots $ \cr }
}$$

Observe that the third piece in this decomposition (pairs that are not
in the first row) is (recursively) the pairs formed from
`(stream/cdr S)` and `(stream/cdr T)`. Also note that the second piece
(the rest of the first row) is

::: scheme
(stream-map (lambda (x) (list (stream-car s) x)) (stream-cdr t))
:::

Thus we can form our stream of pairs as follows:

::: scheme
(define (pairs s t) (cons-stream (list (stream-car s) (stream-car t))
( $\color{SchemeDark}\langle$  *combine-in-some-way*  $\color{SchemeDark}\rangle$
(stream-map (lambda (x) (list (stream-car s) x)) (stream-cdr t)) (pairs
(stream-cdr s) (stream-cdr t)))))
:::

In order to complete the procedure, we must choose some way to combine
the two inner streams. One idea is to use the stream analog of the
`append` procedure from [Section 2.2.1](#Section 2.2.1):

::: scheme
(define (stream-append s1 s2) (if (stream-null? s1) s2 (cons-stream
(stream-car s1) (stream-append (stream-cdr s1) s2))))
:::

This is unsuitable for infinite streams, however, because it takes all
the elements from the first stream before incorporating the second
stream. In particular, if we try to generate all pairs of positive
integers using

::: scheme
(pairs integers integers)
:::

our stream of results will first try to run through all pairs with the
first integer equal to 1, and hence will never produce pairs with any
other value of the first integer.

To handle infinite streams, we need to devise an order of combination
that ensures that every element will eventually be reached if we let our
program run long enough. An elegant way to accomplish this is with the
following `interleave` procedure:[^196]

::: scheme
(define (interleave s1 s2) (if (stream-null? s1) s2 (cons-stream
(stream-car s1) (interleave s2 (stream-cdr s1)))))
:::

Since `interleave` takes elements alternately from the two streams,
every element of the second stream will eventually find its way into the
interleaved stream, even if the first stream is infinite.

We can thus generate the required stream of pairs as

::: scheme
(define (pairs s t) (cons-stream (list (stream-car s) (stream-car t))
(interleave (stream-map (lambda (x) (list (stream-car s) x)) (stream-cdr
t)) (pairs (stream-cdr s) (stream-cdr t)))))
:::

> **[]{#Exercise 3.66 label="Exercise 3.66"}Exercise 3.66:** Examine the
> stream `(pairs integers integers)`. Can you make any general comments
> about the order in which the pairs are placed into the stream? For
> example, approximately how many pairs precede the pair (1, 100)? the
> pair (99, 100)? the pair (100, 100)? (If you can make precise
> mathematical statements here, all the better. But feel free to give
> more qualitative answers if you find yourself getting bogged down.)

> **[]{#Exercise 3.67 label="Exercise 3.67"}Exercise 3.67:** Modify the
> `pairs` procedure so that `(pairs integers integers)` will produce the
> stream of *all* pairs of integers $(i, j)$ (without the condition
> $i \le j$). Hint: You will need to mix in an additional stream.

> **[]{#Exercise 3.68 label="Exercise 3.68"}Exercise 3.68:** Louis
> Reasoner thinks that building a stream of pairs from three parts is
> unnecessarily complicated. Instead of separating the pair $(S_0, T_0)$
> from the rest of the pairs in the first row, he proposes to work with
> the whole first row, as follows:
>
> ::: scheme
> (define (pairs s t) (interleave (stream-map (lambda (x) (list
> (stream-car s) x)) t) (pairs (stream-cdr s) (stream-cdr t))))
> :::
>
> Does this work? Consider what happens if we evaluate
> `(pairs integers integers)` using Louis's definition of `pairs`.

> **[]{#Exercise 3.69 label="Exercise 3.69"}Exercise 3.69:** Write a
> procedure `triples` that takes three infinite streams, $S$, $T$, and
> $U$, and produces the stream of triples $(S_i, T_j, U_k)$ such that
> $i \le j \le k$. Use `triples` to generate the stream of all
> Pythagorean triples of positive integers, i.e., the triples
> $(i, j, k)$ such that $i \le j$ and $i^2 + j^2 = k^2$.

> **[]{#Exercise 3.70 label="Exercise 3.70"}Exercise 3.70:** It would be
> nice to be able to generate streams in which the pairs appear in some
> useful order, rather than in the order that results from an *ad hoc*
> interleaving process. We can use a technique similar to the `merge`
> procedure of [Exercise 3.56](#Exercise 3.56), if we define a way to
> say that one pair of integers is "less than" another. One way to do
> this is to define a "weighting function" $W(i, j)$ and stipulate that
> $(i_1, j_1)$ is less than $(i_2, j_2)$ if $W(i_1, j_1) < W(i_2, j_2)$.
> Write a procedure `merge/weighted` that is like `merge`, except that
> `merge/weighted` takes an additional argument `weight`, which is a
> procedure that computes the weight of a pair, and is used to determine
> the order in which elements should appear in the resulting merged
> stream.[^197] Using this, generalize `pairs` to a procedure
> `weighted/pairs` that takes two streams, together with a procedure
> that computes a weighting function, and generates the stream of pairs,
> ordered according to weight. Use your procedure to generate
>
> a.  the stream of all pairs of positive integers $(i, j)$ with
>     $i \le j$ ordered according to the sum $i + j$,
>
> b.  the stream of all pairs of positive integers $(i, j)$ with
>     $i \le j$, where neither $i$ nor $j$ is divisible by 2, 3, or 5,
>     and the pairs are ordered according to the sum
>     $2i + 3\!j + 5i\!j$.

> **[]{#Exercise 3.71 label="Exercise 3.71"}Exercise 3.71:** Numbers
> that can be expressed as the sum of two cubes in more than one way are
> sometimes called *Ramanujan numbers*, in honor of the mathematician
> Srinivasa Ramanujan.[^198] Ordered streams of pairs provide an elegant
> solution to the problem of computing these numbers. To find a number
> that can be written as the sum of two cubes in two different ways, we
> need only generate the stream of pairs of integers $(i, j)$ weighted
> according to the sum $i^3 + j^3$ (see [Exercise
> 3.70](#Exercise 3.70)), then search the stream for two consecutive
> pairs with the same weight. Write a procedure to generate the
> Ramanujan numbers. The first such number is 1,729. What are the next
> five?

> **[]{#Exercise 3.72 label="Exercise 3.72"}Exercise 3.72:** In a
> similar way to [Exercise 3.71](#Exercise 3.71) generate a stream of
> all numbers that can be written as the sum of two squares in three
> different ways (showing how they can be so written).

#### Streams as signals {#streams-as-signals .unnumbered}

We began our discussion of streams by describing them as computational
analogs of the "signals" in signal-processing systems. In fact, we can
use streams to model signal-processing systems in a very direct way,
representing the values of a signal at successive time intervals as
consecutive elements of a stream. For instance, we can implement an
*integrator* or *summer* that, for an input stream $x = (x_i)$, an
initial value $C$, and a small increment $dt$, accumulates the sum

$$S_i = C + \sum_{j=1}^i x_{\kern-0.07em j} \kern0.1em dt$$

and returns the stream of values $S = (S_i)$. The following `integral`
procedure is reminiscent of the "implicit style" definition of the
stream of integers ([Section 3.5.2](#Section 3.5.2)):

::: scheme
(define (integral integrand initial-value dt) (define int (cons-stream
initial-value (add-streams (scale-stream integrand dt) int))) int)
:::

[Figure 3.32](#Figure 3.32) is a picture of a signal-processing system
that corresponds to the `integral` procedure. The input stream is scaled
by $dt$ and passed through an adder, whose output is passed back through
the same adder. The self-reference in the definition of `int` is
reflected in the figure by the feedback loop that connects the output of
the adder to one of the inputs.

[]{#Figure 3.32 label="Figure 3.32"}

![image](fig/chap3/Fig3.32.pdf){width="102mm"}

> **Figure 3.32:** The `integral` procedure viewed as a
> signal-processing system.

> **[]{#Exercise 3.73 label="Exercise 3.73"}Exercise 3.73:** We can
> model electrical circuits using streams to represent the values of
> currents or voltages at a sequence of times. For instance, suppose we
> have an *RC circuit* consisting of a resistor of resistance $R$ and a
> capacitor of capacitance $C$ in series. The voltage response $v$ of
> the circuit to an injected current $i$ is determined by the formula in
> [Figure 3.33](#Figure 3.33), whose structure is shown by the
> accompanying signal-flow diagram.
>
> []{#Figure 3.33 label="Figure 3.33"}
>
> ![image](fig/chap3/Fig3.33.pdf){width="94mm"}
>
> **Figure 3.33:** An RC circuit and the associated signal-flow diagram.
>
> Write a procedure `RC` that models this circuit. `RC` should take as
> inputs the values of $R$, $C$, and $dt$ and should return a procedure
> that takes as inputs a stream representing the current $i$ and an
> initial value for the capacitor voltage $v_0$ and produces as output
> the stream of voltages $v$. For example, you should be able to use
> `RC` to model an RC circuit with $R$ = 5 ohms, $C$ = 1 farad, and a
> 0.5-second time step by evaluating `(define RC1 (RC 5 1 0.5))`. This
> defines `RC1` as a procedure that takes a stream representing the time
> sequence of currents and an initial capacitor voltage and produces the
> output stream of voltages.

> **[]{#Exercise 3.74 label="Exercise 3.74"}Exercise 3.74:** Alyssa P.
> Hacker is designing a system to process signals coming from physical
> sensors. One important feature she wishes to produce is a signal that
> describes the *zero crossings* of the input signal. That is, the
> resulting signal should be $+1$ whenever the input signal changes from
> negative to positive, $-1$ whenever the input signal changes from
> positive to negative, and 0 otherwise. (Assume that the sign of a 0
> input is positive.) For example, a typical input signal with its
> associated zero-crossing signal would be
>
> ::: scheme
>  $\dots$  1 2 1.5 1 0.5 -0.1 -2 -3 -2 -0.5 0.2 3 4  $\dots$   $\dots$
> 0 0 0 0 0 -1 0 0 0 0 1 0 0  $\dots$
> :::
>
> In Alyssa's system, the signal from the sensor is represented as a
> stream `sense/data` and the stream `zero/crossings` is the
> corresponding stream of zero crossings. Alyssa first writes a
> procedure `sign/change/detector` that takes two values as arguments
> and compares the signs of the values to produce an appropriate 0, 1,
> or - 1. She then constructs her zero-crossing stream as follows:
>
> ::: scheme
> (define (make-zero-crossings input-stream last-value) (cons-stream
> (sign-change-detector (stream-car input-stream) last-value)
> (make-zero-crossings (stream-cdr input-stream) (stream-car
> input-stream)))) (define zero-crossings (make-zero-crossings
> sense-data 0))
> :::
>
> Alyssa's boss, Eva Lu Ator, walks by and suggests that this program is
> approximately equivalent to the following one, which uses the
> generalized version of `stream/map` from [Exercise
> 3.50](#Exercise 3.50):
>
> ::: scheme
> (define zero-crossings (stream-map sign-change-detector sense-data
>  $\color{SchemeDark}\langle$  *expression*  $\color{SchemeDark}\rangle$ ))
> :::
>
> Complete the program by supplying the indicated
> $\langle$*expression*$\rangle$.

> **[]{#Exercise 3.75 label="Exercise 3.75"}Exercise 3.75:**
> Unfortunately, Alyssa's zero-crossing detector in [Exercise
> 3.74](#Exercise 3.74) proves to be insufficient, because the noisy
> signal from the sensor leads to spurious zero crossings. Lem E.
> Tweakit, a hardware specialist, suggests that Alyssa smooth the signal
> to filter out the noise before extracting the zero crossings. Alyssa
> takes his advice and decides to extract the zero crossings from the
> signal constructed by averaging each value of the sense data with the
> previous value. She explains the problem to her assistant, Louis
> Reasoner, who attempts to implement the idea, altering Alyssa's
> program as follows:
>
> ::: scheme
> (define (make-zero-crossings input-stream last-value) (let ((avpt (/
> (+ (stream-car input-stream) last-value) 2))) (cons-stream
> (sign-change-detector avpt last-value) (make-zero-crossings
> (stream-cdr input-stream) avpt))))
> :::
>
> This does not correctly implement Alyssa's plan. Find the bug that
> Louis has installed and fix it without changing the structure of the
> program. (Hint: You will need to increase the number of arguments to
> `make/zero/crossings`.)

> **[]{#Exercise 3.76 label="Exercise 3.76"}Exercise 3.76:** Eva Lu Ator
> has a criticism of Louis's approach in [Exercise
> 3.75](#Exercise 3.75). The program he wrote is not modular, because it
> intermixes the operation of smoothing with the zero-crossing
> extraction. For example, the extractor should not have to be changed
> if Alyssa finds a better way to condition her input signal. Help Louis
> by writing a procedure `smooth` that takes a stream as input and
> produces a stream in which each element is the average of two
> successive input stream elements. Then use `smooth` as a component to
> implement the zero-crossing detector in a more modular style.

### Streams and Delayed Evaluation {#Section 3.5.4}

The `integral` procedure at the end of the preceding section shows how
we can use streams to model signal-processing systems that contain
feedback loops. The feedback loop for the adder shown in [Figure
3.32](#Figure 3.32) is modeled by the fact that `integral`'s internal
stream `int` is defined in terms of itself:

::: scheme
(define int (cons-stream initial-value (add-streams (scale-stream
integrand dt) int)))
:::

The interpreter's ability to deal with such an implicit definition
depends on the `delay` that is incorporated into `cons/stream`. Without
this `delay`, the interpreter could not construct `int` before
evaluating both arguments to `cons/stream`, which would require that
`int` already be defined. In general, `delay` is crucial for using
streams to model signal-processing systems that contain loops. Without
`delay`, our models would have to be formulated so that the inputs to
any signal-processing component would be fully evaluated before the
output could be produced. This would outlaw loops.

[]{#Figure 3.34 label="Figure 3.34"}

![image](fig/chap3/Fig3.34.pdf){width="67mm"}

> **Figure 3.34:** An "analog computer circuit" that solves the equation
> $dy / dt = f(y)$.

Unfortunately, stream models of systems with loops may require uses of
`delay` beyond the "hidden" `delay` supplied by `cons/stream`. For
instance, [Figure 3.34](#Figure 3.34) shows a signal-processing system
for solving the differential equation $dy / dt = f(y)$ where $f$ is a
given function. The figure shows a mapping component, which applies $f$
to its input signal, linked in a feedback loop to an integrator in a
manner very similar to that of the analog computer circuits that are
actually used to solve such equations.

Assuming we are given an initial value $y_0$ for $y$, we could try to
model this system using the procedure

::: scheme
(define (solve f y0 dt) (define y (integral dy y0 dt)) (define dy
(stream-map f y)) y)
:::

This procedure does not work, because in the first line of `solve` the
call to `integral` requires that the input `dy` be defined, which does
not happen until the second line of `solve`.

On the other hand, the intent of our definition does make sense, because
we can, in principle, begin to generate the `y` stream without knowing
`dy`. Indeed, `integral` and many other stream operations have
properties similar to those of `cons/stream`, in that we can generate
part of the answer given only partial information about the arguments.
For `integral`, the first element of the output stream is the specified
`initial/value`. Thus, we can generate the first element of the output
stream without evaluating the integrand `dy`. Once we know the first
element of `y`, the `stream/map` in the second line of `solve` can begin
working to generate the first element of `dy`, which will produce the
next element of `y`, and so on.

To take advantage of this idea, we will redefine `integral` to expect
the integrand stream to be a *delayed argument*. `integral` will `force`
the integrand to be evaluated only when it is required to generate more
than the first element of the output stream:

::: scheme
(define (integral delayed-integrand initial-value dt) (define int
(cons-stream initial-value (let ((integrand (force delayed-integrand)))
(add-streams (scale-stream integrand dt) int)))) int)
:::

Now we can implement our `solve` procedure by delaying the evaluation of
`dy` in the definition of `y`:[^199]

::: scheme
(define (solve f y0 dt) (define y (integral (delay dy) y0 dt)) (define
dy (stream-map f y)) y)
:::

In general, every caller of `integral` must now `delay` the integrand
argument. We can demonstrate that the `solve` procedure works by
approximating $e \approx 2.718$ by computing the value at $y = 1$ of the
solution to the differential equation $dy / dt = y$ with initial
condition $y(0) = 1$:

::: scheme
(stream-ref (solve (lambda (y) y) 1 0.001) 1000)  *2.716924*
:::

> **[]{#Exercise 3.77 label="Exercise 3.77"}Exercise 3.77:** The
> `integral` procedure used above was analogous to the "implicit"
> definition of the infinite stream of integers in [Section
> 3.5.2](#Section 3.5.2). Alternatively, we can give a definition of
> `integral` that is more like `integers/starting/from` (also in
> [Section 3.5.2](#Section 3.5.2)):
>
> ::: smallscheme
> (define (integral integrand initial-value dt) (cons-stream
> initial-value (if (stream-null? integrand) the-empty-stream (integral
> (stream-cdr integrand) (+ (\* dt (stream-car integrand))
> initial-value) dt))))
> :::
>
> When used in systems with loops, this procedure has the same problem
> as does our original version of `integral`. Modify the procedure so
> that it expects the `integrand` as a delayed argument and hence can be
> used in the `solve` procedure shown above.

[]{#Figure 3.35 label="Figure 3.35"}

![image](fig/chap3/Fig3.35a.pdf){width="91mm"}

> **Figure 3.35:** Signal-flow diagram for the solution to a
> second-order linear differential equation.

> **[]{#Exercise 3.78 label="Exercise 3.78"}Exercise 3.78:** Consider
> the problem of designing a signal-processing system to study the
> homogeneous second-order linear differential equation
>
> $${d^2\!y \over dt^2} - a {dy \over dt} - by = 0.$$
>
> The output stream, modeling $y$, is generated by a network that
> contains a loop. This is because the value of $d^2\!y / dt^2$ depends
> upon the values of $y$ and $dy / dt$ and both of these are determined
> by integrating $d^2\!y / dt^2$. The diagram we would like to encode is
> shown in [Figure 3.35](#Figure 3.35). Write a procedure `solve/2nd`
> that takes as arguments the constants $a$, $b$, and $dt$ and the
> initial values $y_0$ and $dy_0$ for $y$ and $dy / dt$ and generates
> the stream of successive values of $y$.

> **[]{#Exercise 3.79 label="Exercise 3.79"}Exercise 3.79:** Generalize
> the `solve/2nd` procedure of [Exercise 3.78](#Exercise 3.78) so that
> it can be used to solve general second-order differential equations
> $d^2\!y / dt^2 =
> f(dy / dt, y)$.

[]{#Figure 3.36 label="Figure 3.36"}

![image](fig/chap3/Fig3.36.pdf){width="60mm"}

**Figure 3.36:** A series RLC circuit.

> **[]{#Exercise 3.80 label="Exercise 3.80"}Exercise 3.80:** A *series
> RLC circuit* consists of a resistor, a capacitor, and an inductor
> connected in series, as shown in [Figure 3.36](#Figure 3.36). If $R$,
> $L$, and $C$ are the resistance, inductance, and capacitance, then the
> relations between voltage ($v$) and current ($i$) for the three
> components are described by the equations
>
> $$v_R     =   i_R R, \qquad\quad
>     v_L     =   L {di_L \over dt}\,, \qquad\quad
>     i_C     =   C {dv_C \over dt}\,,$$
>
> and the circuit connections dictate the relations
>
> $$i_R     =   i_L = -i_C\,, \qquad\quad
>     v_C     =   v_L +  v_R\,.$$
>
> Combining these equations shows that the state of the circuit
> (summarized by $v_C$, the voltage across the capacitor, and $i_L$, the
> current in the inductor) is described by the pair of differential
> equations
>
> $${dv_C \over dt}  =  -{i_L \over C}\,, \qquad\quad
>     {di_L \over dt}  =   {1   \over L} v_C - {R \over L} i_L\,.$$
>
> The signal-flow diagram representing this system of differential
> equations is shown in [Figure 3.37](#Figure 3.37).

[]{#Figure 3.37 label="Figure 3.37"}

![image](fig/chap3/Fig3.37a.pdf){width="68mm"}

> **Figure 3.37:** A signal-flow diagram for the solution to a series
> RLC circuit.

> Write a procedure `RLC` that takes as arguments the parameters $R$,
> $L$, and $C$ of the circuit and the time increment $dt$. In a manner
> similar to that of the `RC` procedure of [Exercise
> 3.73](#Exercise 3.73), `RLC` should produce a procedure that takes the
> initial values of the state variables, $v_{C_0}$ and $i_{L_0}$, and
> produces a pair (using `cons`) of the streams of states $v_C$ and
> $i_L$. Using `RLC`, generate the pair of streams that models the
> behavior of a series RLC circuit with $R$ = 1 ohm, $C$ = 0.2 farad,
> $L$ = 1 henry, $dt$ = 0.1 second, and initial values $i_{L_0}$ = 0
> amps and $v_{C_0}$ = 10 volts.

#### Normal-order evaluation {#normal-order-evaluation .unnumbered}

The examples in this section illustrate how the explicit use of `delay`
and `force` provides great programming flexibility, but the same
examples also show how this can make our programs more complex. Our new
`integral` procedure, for instance, gives us the power to model systems
with loops, but we must now remember that `integral` should be called
with a delayed integrand, and every procedure that uses `integral` must
be aware of this. In effect, we have created two classes of procedures:
ordinary procedures and procedures that take delayed arguments. In
general, creating separate classes of procedures forces us to create
separate classes of higher-order procedures as well.[^200]

One way to avoid the need for two different classes of procedures is to
make all procedures take delayed arguments. We could adopt a model of
evaluation in which all arguments to procedures are automatically
delayed and arguments are forced only when they are actually needed (for
example, when they are required by a primitive operation). This would
transform our language to use normal-order evaluation, which we first
described when we introduced the substitution model for evaluation in
[Section 1.1.5](#Section 1.1.5). Converting to normal-order evaluation
provides a uniform and elegant way to simplify the use of delayed
evaluation, and this would be a natural strategy to adopt if we were
concerned only with stream processing. In [Section 4.2](#Section 4.2),
after we have studied the evaluator, we will see how to transform our
language in just this way. Unfortunately, including delays in procedure
calls wreaks havoc with our ability to design programs that depend on
the order of events, such as programs that use assignment, mutate data,
or perform input or output. Even the single `delay` in `cons/stream` can
cause great confusion, as illustrated by [Exercise 3.51](#Exercise 3.51)
and [Exercise 3.52](#Exercise 3.52). As far as anyone knows, mutability
and delayed evaluation do not mix well in programming languages, and
devising ways to deal with both of these at once is an active area of
research.

### Modularity of Functional Programs and Modularity of Objects {#Section 3.5.5}

As we saw in [Section 3.1.2](#Section 3.1.2), one of the major benefits
of introducing assignment is that we can increase the modularity of our
systems by encapsulating, or "hiding," parts of the state of a large
system within local variables. Stream models can provide an equivalent
modularity without the use of assignment. As an illustration, we can
reimplement the Monte Carlo estimation of $\pi$, which we examined in
[Section 3.1.2](#Section 3.1.2), from a stream-processing point of view.

The key modularity issue was that we wished to hide the internal state
of a random-number generator from programs that used random numbers. We
began with a procedure `rand/update`, whose successive values furnished
our supply of random numbers, and used this to produce a random-number
generator:

::: scheme
(define rand (let ((x random-init)) (lambda () (set! x (rand-update x))
x)))
:::

In the stream formulation there is no random-number generator *per se*,
just a stream of random numbers produced by successive calls to
`rand/update`:

::: scheme
(define random-numbers (cons-stream random-init (stream-map rand-update
random-numbers)))
:::

We use this to construct the stream of outcomes of the Cesàro experiment
performed on consecutive pairs in the `random/numbers` stream:

::: scheme
(define cesaro-stream (map-successive-pairs (lambda (r1 r2) (= (gcd r1
r2) 1)) random-numbers)) (define (map-successive-pairs f s) (cons-stream
(f (stream-car s) (stream-car (stream-cdr s))) (map-successive-pairs f
(stream-cdr (stream-cdr s)))))
:::

The `cesaro/stream` is now fed to a `monte/carlo` procedure, which
produces a stream of estimates of probabilities. The results are then
converted into a stream of estimates of $\pi$. This version of the
program doesn't need a parameter telling how many trials to perform.
Better estimates of $\pi$ (from performing more experiments) are
obtained by looking farther into the `pi` stream:

::: scheme
(define (monte-carlo experiment-stream passed failed) (define (next
passed failed) (cons-stream (/ passed (+ passed failed)) (monte-carlo
(stream-cdr experiment-stream) passed failed))) (if (stream-car
experiment-stream) (next (+ passed 1) failed) (next passed (+ failed
1)))) (define pi (stream-map (lambda (p) (sqrt (/ 6 p))) (monte-carlo
cesaro-stream 0 0)))
:::

There is considerable modularity in this approach, because we still can
formulate a general `monte/carlo` procedure that can deal with arbitrary
experiments. Yet there is no assignment or local state.

> **[]{#Exercise 3.81 label="Exercise 3.81"}Exercise 3.81:** [Exercise
> 3.6](#Exercise 3.6) discussed generalizing the random-number generator
> to allow one to reset the random-number sequence so as to produce
> repeatable sequences of "random" numbers. Produce a stream formulation
> of this same generator that operates on an input stream of requests to
> `generate` a new random number or to `reset` the sequence to a
> specified value and that produces the desired stream of random
> numbers. Don't use assignment in your solution.

> **[]{#Exercise 3.82 label="Exercise 3.82"}Exercise 3.82:** Redo
> [Exercise 3.5](#Exercise 3.5) on Monte Carlo integration in terms of
> streams. The stream version of `estimate/integral` will not have an
> argument telling how many trials to perform. Instead, it will produce
> a stream of estimates based on successively more trials.

#### A functional-programming view of time {#a-functional-programming-view-of-time .unnumbered}

Let us now return to the issues of objects and state that were raised at
the beginning of this chapter and examine them in a new light. We
introduced assignment and mutable objects to provide a mechanism for
modular construction of programs that model systems with state. We
constructed computational objects with local state variables and used
assignment to modify these variables. We modeled the temporal behavior
of the objects in the world by the temporal behavior of the
corresponding computational objects.

Now we have seen that streams provide an alternative way to model
objects with local state. We can model a changing quantity, such as the
local state of some object, using a stream that represents the time
history of successive states. In essence, we represent time explicitly,
using streams, so that we decouple time in our simulated world from the
sequence of events that take place during evaluation. Indeed, because of
the presence of `delay` there may be little relation between simulated
time in the model and the order of events during the evaluation.

In order to contrast these two approaches to modeling, let us reconsider
the implementation of a "withdrawal processor" that monitors the balance
in a bank account. In [Section 3.1.3](#Section 3.1.3) we implemented a
simplified version of such a processor:

::: scheme
(define (make-simplified-withdraw balance) (lambda (amount) (set!
balance (- balance amount)) balance))
:::

Calls to `make/simplified/withdraw` produce computational objects, each
with a local state variable `balance` that is decremented by successive
calls to the object. The object takes an `amount` as an argument and
returns the new balance. We can imagine the user of a bank account
typing a sequence of inputs to such an object and observing the sequence
of returned values shown on a display screen.

Alternatively, we can model a withdrawal processor as a procedure that
takes as input a balance and a stream of amounts to withdraw and
produces the stream of successive balances in the account:

::: scheme
(define (stream-withdraw balance amount-stream) (cons-stream balance
(stream-withdraw (- balance (stream-car amount-stream)) (stream-cdr
amount-stream))))
:::

`stream/withdraw` implements a well-defined mathematical function whose
output is fully determined by its input. Suppose, however, that the
input `amount/stream` is the stream of successive values typed by the
user and that the resulting stream of balances is displayed. Then, from
the perspective of the user who is typing values and watching results,
the stream process has the same behavior as the object created by
`make/simplified/withdraw`. However, with the stream version, there is
no assignment, no local state variable, and consequently none of the
theoretical difficulties that we encountered in [Section
3.1.3](#Section 3.1.3). Yet the system has state!

This is really remarkable. Even though `stream/withdraw` implements a
well-defined mathematical function whose behavior does not change, the
user's perception here is one of interacting with a system that has a
changing state. One way to resolve this paradox is to realize that it is
the user's temporal existence that imposes state on the system. If the
user could step back from the interaction and think in terms of streams
of balances rather than individual transactions, the system would appear
stateless.[^201]

From the point of view of one part of a complex process, the other parts
appear to change with time. They have hidden time-varying local state.
If we wish to write programs that model this kind of natural
decomposition in our world (as we see it from our viewpoint as a part of
that world) with structures in our computer, we make computational
objects that are not functional---they must change with time. We model
state with local state variables, and we model the changes of state with
assignments to those variables. By doing this we make the time of
execution of a computation model time in the world that we are part of,
and thus we get "objects" in our computer.

Modeling with objects is powerful and intuitive, largely because this
matches the perception of interacting with a world of which we are part.
However, as we've seen repeatedly throughout this chapter, these models
raise thorny problems of constraining the order of events and of
synchronizing multiple processes. The possibility of avoiding these
problems has stimulated the development of *functional programming
languages*, which do not include any provision for assignment or mutable
data. In such a language, all procedures implement well-defined
mathematical functions of their arguments, whose behavior does not
change. The functional approach is extremely attractive for dealing with
concurrent systems.[^202]

On the other hand, if we look closely, we can see time-related problems
creeping into functional models as well. One particularly troublesome
area arises when we wish to design interactive systems, especially ones
that model interactions between independent entities. For instance,
consider once more the implementation a banking system that permits
joint bank accounts. In a conventional system using assignment and
objects, we would model the fact that Peter and Paul share an account by
having both Peter and Paul send their transaction requests to the same
bank-account object, as we saw in [Section 3.1.3](#Section 3.1.3). From
the stream point of view, where there are no "objects" *per se*, we have
already indicated that a bank account can be modeled as a process that
operates on a stream of transaction requests to produce a stream of
responses. Accordingly, we could model the fact that Peter and Paul have
a joint bank account by merging Peter's stream of transaction requests
with Paul's stream of requests and feeding the result to the
bank-account stream process, as shown in [Figure 3.38](#Figure 3.38).

[]{#Figure 3.38 label="Figure 3.38"}

![image](fig/chap3/Fig3.38.pdf){width="88mm"}

> **Figure 3.38:** A joint bank account, modeled by merging two streams
> of transaction requests.

The trouble with this formulation is in the notion of *merge*. It will
not do to merge the two streams by simply taking alternately one request
from Peter and one request from Paul. Suppose Paul accesses the account
only very rarely. We could hardly force Peter to wait for Paul to access
the account before he could issue a second transaction. However such a
merge is implemented, it must interleave the two transaction streams in
some way that is constrained by "real time" as perceived by Peter and
Paul, in the sense that, if Peter and Paul meet, they can agree that
certain transactions were processed before the meeting, and other
transactions were processed after the meeting.[^203] This is precisely
the same constraint that we had to deal with in [Section
3.4.1](#Section 3.4.1), where we found the need to introduce explicit
synchronization to ensure a "correct" order of events in concurrent
processing of objects with state. Thus, in an attempt to support the
functional style, the need to merge inputs from different agents
reintroduces the same problems that the functional style was meant to
eliminate.

We began this chapter with the goal of building computational models
whose structure matches our perception of the real world we are trying
to model. We can model the world as a collection of separate,
time-bound, interacting objects with state, or we can model the world as
a single, timeless, stateless unity. Each view has powerful advantages,
but neither view alone is completely satisfactory. A grand unification
has yet to emerge.[^204]

# Metalinguistic Abstraction {#Chapter 4}

> $\dots$ It's in words that the magic is---Abracadabra, Open Sesame,
> and the rest---but the magic words in one story aren't magical in the
> next. The real magic is to understand which words work, and when, and
> for what; the trick is to learn the trick.
>
> $\dots$ And those words are made from the letters of our alphabet: a
> couple-dozen squiggles we can draw with the pen. This is the key! And
> the treasure, too, if we can only get our hands on it! It's as if---as
> if the key to the treasure *is* the treasure!
>
> ---John Barth, *Chimera*

In our study of program design, we have seen
that expert programmers control the complexity of their designs with the
same general techniques used by designers of all complex systems. They
combine primitive elements to form compound objects, they abstract
compound objects to form higher-level building blocks, and they preserve
modularity by adopting appropriate large-scale views of system
structure. In illustrating these techniques, we have used Lisp as a
language for describing processes and for constructing computational
data objects and processes to model complex phenomena in the real world.
However, as we confront increasingly complex problems, we will find that
Lisp, or indeed any fixed programming language, is not sufficient for
our needs. We must constantly turn to new languages in order to express
our ideas more effectively. Establishing new languages is a powerful
strategy for controlling complexity in engineering design; we can often
enhance our ability to deal with a complex problem by adopting a new
language that enables us to describe (and hence to think about) the
problem in a different way, using primitives, means of combination, and
means of abstraction that are particularly well suited to the problem at
hand.[^205]

Programming is endowed with a multitude of languages. There are physical
languages, such as the machine languages for particular computers. These
languages are concerned with the representation of data and control in
terms of individual bits of storage and primitive machine instructions.
The machine-language programmer is concerned with using the given
hardware to erect systems and utilities for the efficient implementation
of resource-limited computations. High-level languages, erected on a
machine-language substrate, hide concerns about the representation of
data as collections of bits and the representation of programs as
sequences of primitive instructions. These languages have means of
combination and abstraction, such as procedure definition, that are
appropriate to the larger-scale organization of systems.

*Metalinguistic abstraction*---establishing new languages---plays an
important role in all branches of engineering design. It is particularly
important to computer programming, because in programming not only can
we formulate new languages but we can also implement these languages by
constructing evaluators. An *evaluator* (or *interpreter*) for a
programming language is a procedure that, when applied to an expression
of the language, performs the actions required to evaluate that
expression.

It is no exaggeration to regard this as the most fundamental idea in
programming:

> The evaluator, which determines the meaning of expressions in a
> programming language, is just another program.

To appreciate this point is to change our images of ourselves as
programmers. We come to see ourselves as designers of languages, rather
than only users of languages designed by others.

In fact, we can regard almost any program as the evaluator for some
language. For instance, the polynomial manipulation system of [Section
2.5.3](#Section 2.5.3) embodies the rules of polynomial arithmetic and
implements them in terms of operations on list-structured data. If we
augment this system with procedures to read and print polynomial
expressions, we have the core of a special-purpose language for dealing
with problems in symbolic mathematics. The digital-logic simulator of
[Section 3.3.4](#Section 3.3.4) and the constraint propagator of
[Section 3.3.5](#Section 3.3.5) are legitimate languages in their own
right, each with its own primitives, means of combination, and means of
abstraction. Seen from this perspective, the technology for coping with
large-scale computer systems merges with the technology for building new
computer languages, and computer science itself becomes no more (and no
less) than the discipline of constructing appropriate descriptive
languages.

We now embark on a tour of the technology by which languages are
established in terms of other languages. In this chapter we shall use
Lisp as a base, implementing evaluators as Lisp procedures. Lisp is
particularly well suited to this task, because of its ability to
represent and manipulate symbolic expressions. We will take the first
step in understanding how languages are implemented by building an
evaluator for Lisp itself. The language implemented by our evaluator
will be a subset of the Scheme dialect of Lisp that we use in this book.
Although the evaluator described in this chapter is written for a
particular dialect of Lisp, it contains the essential structure of an
evaluator for any expression-oriented language designed for writing
programs for a sequential machine. (In fact, most language processors
contain, deep within them, a little "Lisp" evaluator.) The evaluator has
been simplified for the purposes of illustration and discussion, and
some features have been left out that would be important to include in a
production-quality Lisp system. Nevertheless, this simple evaluator is
adequate to execute most of the programs in this book.[^206]

An important advantage of making the evaluator accessible as a Lisp
program is that we can implement alternative evaluation rules by
describing these as modifications to the evaluator program. One place
where we can use this power to good effect is to gain extra control over
the ways in which computational models embody the notion of time, which
was so central to the discussion in [Chapter 3](#Chapter 3). There, we
mitigated some of the complexities of state and assignment by using
streams to decouple the representation of time in the world from time in
the computer. Our stream programs, however, were sometimes cumbersome,
because they were constrained by the applicative-order evaluation of
Scheme. In [Section 4.2](#Section 4.2), we'll change the underlying
language to provide for a more elegant approach, by modifying the
evaluator to provide for *normal-order evaluation*.

[Section 4.3](#Section 4.3) implements a more ambitious linguistic
change, whereby expressions have many values, rather than just a single
value. In this language of *nondeterministic computing*, it is natural
to express processes that generate all possible values for expressions
and then search for those values that satisfy certain constraints. In
terms of models of computation and time, this is like having time branch
into a set of "possible futures" and then searching for appropriate time
lines. With our nondeterministic evaluator, keeping track of multiple
values and performing searches are handled automatically by the
underlying mechanism of the language.

In [Section 4.4](#Section 4.4) we implement a *logic-programming*
language in which knowledge is expressed in terms of relations, rather
than in terms of computations with inputs and outputs. Even though this
makes the language drastically different from Lisp, or indeed from any
conventional language, we will see that the logic-programming evaluator
shares the essential structure of the Lisp evaluator.

## The Metacircular Evaluator {#Section 4.1}

Our evaluator for Lisp will be implemented as a Lisp program. It may
seem circular to think about evaluating Lisp programs using an evaluator
that is itself implemented in Lisp. However, evaluation is a process, so
it is appropriate to describe the evaluation process using Lisp, which,
after all, is our tool for describing processes.[^207] An evaluator that
is written in the same language that it evaluates is said to be
*metacircular*.

The metacircular evaluator is essentially a Scheme formulation of the
environment model of evaluation described in [Section
3.2](#Section 3.2). Recall that the model has two basic parts:

1.  To evaluate a combination (a compound expression other than a
    special form), evaluate the subexpressions and then apply the value
    of the operator subexpression to the values of the operand
    subexpressions.

2.  To apply a compound procedure to a set of arguments, evaluate the
    body of the procedure in a new environment. To construct this
    environment, extend the environment part of the procedure object by
    a frame in which the formal parameters of the procedure are bound to
    the arguments to which the procedure is applied.

These two rules describe the essence of the evaluation process, a basic
cycle in which expressions to be evaluated in environments are reduced
to procedures to be applied to arguments, which in turn are reduced to
new expressions to be evaluated in new environments, and so on, until we
get down to symbols, whose values are looked up in the environment, and
to primitive procedures, which are applied directly (see [Figure
4.1](#Figure 4.1)).[^208] This evaluation cycle will be embodied by the
interplay between the two critical procedures in the evaluator, `eval`
and `apply`, which are described in [Section 4.1.1](#Section 4.1.1) (see
[Figure 4.1](#Figure 4.1)).

The implementation of the evaluator will depend upon procedures that
define the *syntax* of the expressions to be evaluated. We will use data
abstraction to make the evaluator independent of the representation of
the language. For example, rather than committing to a choice that an
assignment is to be represented by a list beginning with the symbol
`set!` we use an abstract predicate `assignment?` to test for an
assignment, and we use abstract selectors `assignment/variable` and
`assignment/value` to access the parts of an assignment. Implementation
of expressions will be described in detail in [Section
4.1.2](#Section 4.1.2). There are also operations, described in [Section
4.1.3](#Section 4.1.3), that specify the representation of procedures
and environments. For example, `make/procedure` constructs compound
procedures, `lookup/variable/value` accesses the values of variables,
and `apply/primitive/procedure` applies a primitive procedure to a given
list of arguments.

[]{#Figure 4.1 label="Figure 4.1"}

![image](fig/chap4/Fig4.1.pdf){width="100mm"}

> **Figure 4.1:** The `eval`-`apply` cycle exposes the essence of a
> computer language.

### The Core of the Evaluator {#Section 4.1.1}

The evaluation process can be described as the interplay between two
procedures: `eval` and `apply`.

#### Eval {#eval .unnumbered}

`eval` takes as arguments an expression and an environment. It
classifies the expression and directs its evaluation. `eval` is
structured as a case analysis of the syntactic type of the expression to
be evaluated. In order to keep the procedure general, we express the
determination of the type of an expression abstractly, making no
commitment to any particular representation for the various types of
expressions. Each type of expression has a predicate that tests for it
and an abstract means for selecting its parts. This *abstract syntax*
makes it easy to see how we can change the syntax of the language by
using the same evaluator, but with a different collection of syntax
procedures.

**Primitive expressions**

-   For self-evaluating expressions, such as numbers, `eval` returns the
    expression itself.

-   `eval` must look up variables in the environment to find their
    values.

**Special forms**

-   For quoted expressions, `eval` returns the expression that was
    quoted.

-   An assignment to (or a definition of) a variable must recursively
    call `eval` to compute the new value to be associated with the
    variable. The environment must be modified to change (or create) the
    binding of the variable.

-   An `if` expression requires special processing of its parts, so as
    to evaluate the consequent if the predicate is true, and otherwise
    to evaluate the alternative.

-   A `lambda` expression must be transformed into an applicable
    procedure by packaging together the parameters and body specified by
    the `lambda` expression with the environment of the evaluation.

-   A `begin` expression requires evaluating its sequence of expressions
    in the order in which they appear.

-   A case analysis (`cond`) is transformed into a nest of `if`
    expressions and then evaluated.

**Combinations**

-   For a procedure application, `eval` must recursively evaluate the
    operator part and the operands of the combination. The resulting
    procedure and arguments are passed to `apply`, which handles the
    actual procedure application.

Here is the definition of `eval`:

::: scheme
(define (eval exp env) (cond ((self-evaluating? exp) exp) ((variable?
exp) (lookup-variable-value exp env)) ((quoted? exp) (text-of-quotation
exp)) ((assignment? exp) (eval-assignment exp env)) ((definition? exp)
(eval-definition exp env)) ((if? exp) (eval-if exp env)) ((lambda? exp)
(make-procedure (lambda-parameters exp) (lambda-body exp) env)) ((begin?
exp) (eval-sequence (begin-actions exp) env)) ((cond? exp) (eval
(cond-\>if exp) env)) ((application? exp) (apply (eval (operator exp)
env) (list-of-values (operands exp) env))) (else (error \"Unknown
expression type: EVAL\" exp))))
:::

For clarity, `eval` has been implemented as a case analysis using
`cond`. The disadvantage of this is that our procedure handles only a
few distinguishable types of expressions, and no new ones can be defined
without editing the definition of `eval`. In most Lisp implementations,
dispatching on the type of an expression is done in a data-directed
style. This allows a user to add new types of expressions that `eval`
can distinguish, without modifying the definition of `eval` itself. (See
[Exercise 4.3](#Exercise 4.3).)

#### Apply {#apply .unnumbered}

`apply` takes two arguments, a procedure and a list of arguments to
which the procedure should be applied. `apply` classifies procedures
into two kinds: It calls `apply/primitive/procedure` to apply
primitives; it applies compound procedures by sequentially evaluating
the expressions that make up the body of the procedure. The environment
for the evaluation of the body of a compound procedure is constructed by
extending the base environment carried by the procedure to include a
frame that binds the parameters of the procedure to the arguments to
which the procedure is to be applied. Here is the definition of `apply`:

::: scheme
(define (apply procedure arguments) (cond ((primitive-procedure?
procedure) (apply-primitive-procedure procedure arguments))
((compound-procedure? procedure) (eval-sequence (procedure-body
procedure) (extend-environment (procedure-parameters procedure)
arguments (procedure-environment procedure)))) (else (error \"Unknown
procedure type: APPLY\" procedure))))
:::

#### Procedure arguments {#procedure-arguments .unnumbered}

When `eval` processes a procedure application, it uses `list/of/values`
to produce the list of arguments to which the procedure is to be
applied. `list/of/values` takes as an argument the operands of the
combination. It evaluates each operand and returns a list of the
corresponding values:[^209]

::: scheme
(define (list-of-values exps env) (if (no-operands? exps) '() (cons
(eval (first-operand exps) env) (list-of-values (rest-operands exps)
env))))
:::

#### Conditionals {#conditionals .unnumbered}

`eval/if` evaluates the predicate part of an `if` expression in the
given environment. If the result is true, `eval/if` evaluates the
consequent, otherwise it evaluates the alternative:

::: scheme
(define (eval-if exp env) (if (true? (eval (if-predicate exp) env))
(eval (if-consequent exp) env) (eval (if-alternative exp) env)))
:::

The use of `true?` in `eval/if` highlights the issue of the connection
between an implemented language and an implementation language. The
`if/predicate` is evaluated in the language being implemented and thus
yields a value in that language. The interpreter predicate `true?`
translates that value into a value that can be tested by the `if` in the
implementation language: The metacircular representation of truth might
not be the same as that of the underlying Scheme.[^210]

#### Sequences {#sequences .unnumbered}

`eval/sequence` is used by `apply` to evaluate the sequence of
expressions in a procedure body and by `eval` to evaluate the sequence
of expressions in a `begin` expression. It takes as arguments a sequence
of expressions and an environment, and evaluates the expressions in the
order in which they occur. The value returned is the value of the final
expression.

::: scheme
(define (eval-sequence exps env) (cond ((last-exp? exps) (eval
(first-exp exps) env)) (else (eval (first-exp exps) env) (eval-sequence
(rest-exps exps) env))))
:::

#### Assignments and definitions {#assignments-and-definitions .unnumbered}

The following procedure handles assignments to variables. It calls
`eval` to find the value to be assigned and transmits the variable and
the resulting value to `set/variable/value!` to be installed in the
designated environment.

::: scheme
(define (eval-assignment exp env) (set-variable-value!
(assignment-variable exp) (eval (assignment-value exp) env) env) 'ok)
:::

Definitions of variables are handled in a similar manner.[^211]

::: scheme
(define (eval-definition exp env) (define-variable! (definition-variable
exp) (eval (definition-value exp) env) env) 'ok)
:::

We have chosen here to return the symbol `ok` as the value of an
assignment or a definition.[^212]

> **[]{#Exercise 4.1 label="Exercise 4.1"}Exercise 4.1:** Notice that we
> cannot tell whether the metacircular evaluator evaluates operands from
> left to right or from right to left. Its evaluation order is inherited
> from the underlying Lisp: If the arguments to `cons` in
> `list/of/values` are evaluated from left to right, then
> `list/of/values` will evaluate operands from left to right; and if the
> arguments to `cons` are evaluated from right to left, then
> `list/of/values` will evaluate operands from right to left.
>
> Write a version of `list/of/values` that evaluates operands from left
> to right regardless of the order of evaluation in the underlying Lisp.
> Also write a version of `list/of/values` that evaluates operands from
> right to left.

### Representing Expressions {#Section 4.1.2}

The evaluator is reminiscent of the symbolic differentiation program
discussed in [Section 2.3.2](#Section 2.3.2). Both programs operate on
symbolic expressions. In both programs, the result of operating on a
compound expression is determined by operating recursively on the pieces
of the expression and combining the results in a way that depends on the
type of the expression. In both programs we used data abstraction to
decouple the general rules of operation from the details of how
expressions are represented. In the differentiation program this meant
that the same differentiation procedure could deal with algebraic
expressions in prefix form, in infix form, or in some other form. For
the evaluator, this means that the syntax of the language being
evaluated is determined solely by the procedures that classify and
extract pieces of expressions.

Here is the specification of the syntax of our language:

-   The only self-evaluating items are numbers and strings:

    ::: scheme
    (define (self-evaluating? exp) (cond ((number? exp) true) ((string?
    exp) true) (else false)))
    :::

-   Variables are represented by symbols:

    ::: scheme
    (define (variable? exp) (symbol? exp))
    :::

-   Quotations have the form
    `(quote `$\langle$*`text/of/quotation`*$\rangle$`)`:[^213]

    ::: scheme
    (define (quoted? exp) (tagged-list? exp 'quote)) (define
    (text-of-quotation exp) (cadr exp))
    :::

    `quoted?` is defined in terms of the procedure `tagged/list?`, which
    identifies lists beginning with a designated symbol:

    ::: scheme
    (define (tagged-list? exp tag) (if (pair? exp) (eq? (car exp) tag)
    false))
    :::

-   Assignments have the form
    `(set! `$\langle$*`var`*$\rangle$` `$\langle$*`value`*$\rangle$`)`:

    ::: scheme
    (define (assignment? exp) (tagged-list? exp 'set!)) (define
    (assignment-variable exp) (cadr exp)) (define (assignment-value exp)
    (caddr exp))
    :::

-   Definitions have the form

    ::: scheme
    (define
     $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}\rangle$
     $\color{SchemeDark}\langle$  *value*  $\color{SchemeDark}\rangle$ )
    :::

    or the form

    ::: scheme
    (define
    ( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}\rangle$
     $\color{SchemeDark}\langle$  *parameter*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
     $\dots$
     $\color{SchemeDark}\langle$  *parameter*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
     $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
    :::

    The latter form (standard procedure definition) is syntactic sugar
    for

    ::: scheme
    (define
     $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}\rangle$
    (lambda
    ( $\color{SchemeDark}\langle$  *parameter*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
     $\dots$
     $\color{SchemeDark}\langle$  *parameter*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
     $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ ))
    :::

    The corresponding syntax procedures are the following:

    ::: scheme
    (define (definition? exp) (tagged-list? exp 'define)) (define
    (definition-variable exp) (if (symbol? (cadr exp)) (cadr exp) (caadr
    exp))) (define (definition-value exp) (if (symbol? (cadr exp))
    (caddr exp) (make-lambda (cdadr exp)  [; formal parameters]{.roman}
    (cddr exp))))  [; body]{.roman}
    :::

-   `lambda` expressions are lists that begin with the symbol `lambda`:

    ::: scheme
    (define (lambda? exp) (tagged-list? exp 'lambda)) (define
    (lambda-parameters exp) (cadr exp)) (define (lambda-body exp) (cddr
    exp))
    :::

    We also provide a constructor for `lambda` expressions, which is
    used by `definition/value`, above:

    ::: scheme
    (define (make-lambda parameters body) (cons 'lambda (cons parameters
    body)))
    :::

-   Conditionals begin with `if` and have a predicate, a consequent, and
    an (optional) alternative. If the expression has no alternative
    part, we provide `false` as the alternative.[^214]

    ::: scheme
    (define (if? exp) (tagged-list? exp 'if)) (define (if-predicate exp)
    (cadr exp)) (define (if-consequent exp) (caddr exp)) (define
    (if-alternative exp) (if (not (null? (cdddr exp))) (cadddr exp)
    'false))
    :::

    We also provide a constructor for `if` expressions, to be used by
    `cond/>if` to transform `cond` expressions into `if` expressions:

    ::: scheme
    (define (make-if predicate consequent alternative) (list 'if
    predicate consequent alternative))
    :::

-   `begin` packages a sequence of expressions into a single expression.
    We include syntax operations on `begin` expressions to extract the
    actual sequence from the `begin` expression, as well as selectors
    that return the first expression and the rest of the expressions in
    the sequence.[^215]

    ::: scheme
    (define (begin? exp) (tagged-list? exp 'begin)) (define
    (begin-actions exp) (cdr exp)) (define (last-exp? seq) (null? (cdr
    seq))) (define (first-exp seq) (car seq)) (define (rest-exps seq)
    (cdr seq))
    :::

    We also include a constructor `sequence/>exp` (for use by
    `cond/>if`) that transforms a sequence into a single expression,
    using `begin` if necessary:

    ::: scheme
    (define (sequence-\>exp seq) (cond ((null? seq) seq) ((last-exp?
    seq) (first-exp seq)) (else (make-begin seq)))) (define (make-begin
    seq) (cons 'begin seq))
    :::

-   A procedure application is any compound expression that is not one
    of the above expression types. The `car` of the expression is the
    operator, and the `cdr` is the list of operands:

    ::: scheme
    (define (application? exp) (pair? exp)) (define (operator exp) (car
    exp)) (define (operands exp) (cdr exp)) (define (no-operands? ops)
    (null? ops)) (define (first-operand ops) (car ops)) (define
    (rest-operands ops) (cdr ops))
    :::

#### Derived expressions {#derived-expressions .unnumbered}

Some special forms in our language can be defined in terms of
expressions involving other special forms, rather than being implemented
directly. One example is `cond`, which can be implemented as a nest of
`if` expressions. For example, we can reduce the problem of evaluating
the expression

::: scheme
(cond ((\> x 0) x) ((= x 0) (display 'zero) 0) (else (- x)))
:::

to the problem of evaluating the following expression involving `if` and
`begin` expressions:

::: scheme
(if (\> x 0) x (if (= x 0) (begin (display 'zero) 0) (- x)))
:::

Implementing the evaluation of `cond` in this way simplifies the
evaluator because it reduces the number of special forms for which the
evaluation process must be explicitly specified.

We include syntax procedures that extract the parts of a `cond`
expression, and a procedure `cond/>if` that transforms `cond`
expressions into `if` expressions. A case analysis begins with `cond`
and has a list of predicate-action clauses. A clause is an `else` clause
if its predicate is the symbol `else`.[^216]

::: scheme
(define (cond? exp) (tagged-list? exp 'cond)) (define (cond-clauses exp)
(cdr exp)) (define (cond-else-clause? clause) (eq? (cond-predicate
clause) 'else)) (define (cond-predicate clause) (car clause)) (define
(cond-actions clause) (cdr clause)) (define (cond-\>if exp)
(expand-clauses (cond-clauses exp))) (define (expand-clauses clauses)
(if (null? clauses) 'false  [; no `else` clause]{.roman}  (let ((first
(car clauses)) (rest (cdr clauses))) (if (cond-else-clause? first) (if
(null? rest) (sequence-\>exp (cond-actions first)) (error \"ELSE clause
isn't last: COND-\>IF\" clauses)) (make-if (cond-predicate first)
(sequence-\>exp (cond-actions first)) (expand-clauses rest))))))
:::

Expressions (such as `cond`) that we choose to implement as syntactic
transformations are called *derived expressions*. `let` expressions are
also derived expressions (see [Exercise 4.6](#Exercise 4.6)).[^217]

> **[]{#Exercise 4.2 label="Exercise 4.2"}Exercise 4.2:** Louis Reasoner
> plans to reorder the `cond` clauses in `eval` so that the clause for
> procedure applications appears before the clause for assignments. He
> argues that this will make the interpreter more efficient: Since
> programs usually contain more applications than assignments,
> definitions, and so on, his modified `eval` will usually check fewer
> clauses than the original `eval` before identifying the type of an
> expression.
>
> a.  What is wrong with Louis's plan? (Hint: What will Louis's
>     evaluator do with the expression `(define x 3)`?)
>
> b.  Louis is upset that his plan didn't work. He is willing to go to
>     any lengths to make his evaluator recognize procedure applications
>     before it checks for most other kinds of expressions. Help him by
>     changing the syntax of the evaluated language so that procedure
>     applications start with `call`. For example, instead of
>     `(factorial 3)` we will now have to write `(call factorial 3)` and
>     instead of `(+ 1 2)` we will have to write `(call + 1 2)`.

> **[]{#Exercise 4.3 label="Exercise 4.3"}Exercise 4.3:** Rewrite `eval`
> so that the dispatch is done in data-directed style. Compare this with
> the data-directed differentiation procedure of [Exercise
> 2.73](#Exercise 2.73). (You may use the `car` of a compound expression
> as the type of the expression, as is appropriate for the syntax
> implemented in this section.)

> **[]{#Exercise 4.4 label="Exercise 4.4"}Exercise 4.4:** Recall the
> definitions of the special forms `and` and `or` from [Chapter
> 1](#Chapter 1):
>
> -   `and`: The expressions are evaluated from left to right. If any
>     expression evaluates to false, false is returned; any remaining
>     expressions are not evaluated. If all the expressions evaluate to
>     true values, the value of the last expression is returned. If
>     there are no expressions then true is returned.
>
> -   `or`: The expressions are evaluated from left to right. If any
>     expression evaluates to a true value, that value is returned; any
>     remaining expressions are not evaluated. If all expressions
>     evaluate to false, or if there are no expressions, then false is
>     returned.
>
> Install `and` and `or` as new special forms for the evaluator by
> defining appropriate syntax procedures and evaluation procedures
> `eval/and` and `eval/or`. Alternatively, show how to implement `and`
> and `or` as derived expressions.

> **[]{#Exercise 4.5 label="Exercise 4.5"}Exercise 4.5:** Scheme allows
> an additional syntax for `cond` clauses,
> `(`$\langle$*`test`*$\rangle$` => `$\langle$*`recipient`*$\rangle$`)`.
> If $\langle$*test*$\kern0.08em\rangle$ evaluates to a true value, then
> $\langle$*recipient*$\kern0.08em\rangle$ is evaluated. Its value must
> be a procedure of one argument; this procedure is then invoked on the
> value of the $\langle$*test*$\kern0.08em\rangle$, and the result is
> returned as the value of the `cond` expression. For example
>
> ::: scheme
> (cond ((assoc 'b '((a 1) (b 2))) =\> cadr) (else false))
> :::
>
> returns 2. Modify the handling of `cond` so that it supports this
> extended syntax.

> **[]{#Exercise 4.6 label="Exercise 4.6"}Exercise 4.6:** `let`
> expressions are derived expressions, because
>
> ::: scheme
> (let
> (( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
>  $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$ )
>  $\dots$
> ( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$
>  $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ ))
>  $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
> :::
>
> is equivalent to
>
> ::: scheme
> ((lambda
> ( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
>  $\dots$
>  $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
>  $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
>  $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
>  $\dots$
>  $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
> :::
>
> Implement a syntactic transformation `let/>combination` that reduces
> evaluating `let` expressions to evaluating combinations of the type
> shown above, and add the appropriate clause to `eval` to handle `let`
> expressions.

> **[]{#Exercise 4.7 label="Exercise 4.7"}Exercise 4.7:** `let*` is
> similar to `let`, except that the bindings of the `let*` variables are
> performed sequentially from left to right, and each binding is made in
> an environment in which all of the preceding bindings are visible. For
> example
>
> ::: scheme
> (let\* ((x 3) (y (+ x 2)) (z (+ x y 5))) (\* x z))
> :::
>
> returns 39. Explain how a `let*` expression can be rewritten as a set
> of nested `let` expressions, and write a procedure `let*/>nested/lets`
> that performs this transformation. If we have already implemented
> `let` ([Exercise 4.6](#Exercise 4.6)) and we want to extend the
> evaluator to handle `let*`, is it sufficient to add a clause to `eval`
> whose action is
>
> ::: scheme
> (eval (let\*-\>nested-lets exp) env)
> :::
>
> or must we explicitly expand `let*` in terms of non-derived
> expressions?

> **[]{#Exercise 4.8 label="Exercise 4.8"}Exercise 4.8:** "Named `let`"
> is a variant of `let` that has the form
>
> ::: scheme
> (let  $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *bindings*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
> :::
>
> The $\langle$*bindings*$\kern0.08em\rangle$ and
> $\langle$*body*$\kern0.08em\rangle$ are just as in ordinary `let`,
> except that $\langle$*var*$\kern0.08em\rangle$ is bound within
> $\langle$*body*$\kern0.08em\rangle$ to a procedure whose body is
> $\langle$*body*$\kern0.08em\rangle$ and whose parameters are the
> variables in the $\langle$*bindings*$\kern0.08em\rangle$. Thus, one
> can repeatedly execute the $\langle$*body*$\kern0.08em\rangle$ by
> invoking the procedure named $\langle$*var*$\kern0.08em\rangle$. For
> example, the iterative Fibonacci procedure ([Section
> 1.2.2](#Section 1.2.2)) can be rewritten using named `let` as follows:
>
> ::: scheme
> (define (fib n) (let fib-iter ((a 1) (b 0) (count n)) (if (= count 0)
> b (fib-iter (+ a b) a (- count 1)))))
> :::
>
> Modify `let/>combination` of [Exercise 4.6](#Exercise 4.6) to also
> support named `let`.

> **[]{#Exercise 4.9 label="Exercise 4.9"}Exercise 4.9:** Many languages
> support a variety of iteration constructs, such as `do`, `for`,
> `while`, and `until`. In Scheme, iterative processes can be expressed
> in terms of ordinary procedure calls, so special iteration constructs
> provide no essential gain in computational power. On the other hand,
> such constructs are often convenient. Design some iteration
> constructs, give examples of their use, and show how to implement them
> as derived expressions.

> **[]{#Exercise 4.10 label="Exercise 4.10"}Exercise 4.10:** By using
> data abstraction, we were able to write an `eval` procedure that is
> independent of the particular syntax of the language to be evaluated.
> To illustrate this, design and implement a new syntax for Scheme by
> modifying the procedures in this section, without changing `eval` or
> `apply`.

### Evaluator Data Structures {#Section 4.1.3}

In addition to defining the external syntax of expressions, the
evaluator implementation must also define the data structures that the
evaluator manipulates internally, as part of the execution of a program,
such as the representation of procedures and environments and the
representation of true and false.

#### Testing of predicates {#testing-of-predicates .unnumbered}

For conditionals, we accept anything to be true that is not the explicit
`false` object.

::: scheme
(define (true? x) (not (eq? x false))) (define (false? x) (eq? x false))
:::

#### Representing procedures {#representing-procedures .unnumbered}

To handle primitives, we assume that we have available the following
procedures:

-   `(apply/primitive/procedure `$\langle$*`proc`*$\rangle$` `$\langle$*`args`*$\rangle$`)`

    applies the given primitive procedure to the argument values in the
    list $\langle$*args*$\kern0.08em\rangle$ and returns the result of
    the application.

-   `(primitive/procedure? `$\langle$*`proc`*$\rangle$`)`

    tests whether $\langle$*proc*$\kern0.08em\rangle$ is a primitive
    procedure.

These mechanisms for handling primitives are further described in
[Section 4.1.4](#Section 4.1.4).

Compound procedures are constructed from parameters, procedure bodies,
and environments using the constructor `make/procedure`:

::: scheme
(define (make-procedure parameters body env) (list 'procedure parameters
body env)) (define (compound-procedure? p) (tagged-list? p 'procedure))
(define (procedure-parameters p) (cadr p)) (define (procedure-body p)
(caddr p)) (define (procedure-environment p) (cadddr p))
:::

#### Operations on Environments {#operations-on-environments .unnumbered}

The evaluator needs operations for manipulating environments. As
explained in [Section 3.2](#Section 3.2), an environment is a sequence
of frames, where each frame is a table of bindings that associate
variables with their corresponding values. We use the following
operations for manipulating environments:

-   `(lookup/variable/value `$\langle$*`var`*$\rangle$` `$\langle$*`env`*$\rangle$`)`

    returns the value that is bound to the symbol
    $\langle$*var*$\kern0.08em\rangle$ in the environment
    $\langle$*env*$\kern0.08em\rangle$, or signals an error if the
    variable is unbound.

-   `(extend/environment `$\langle$*`variables`*$\rangle$` `$\langle$*`values`*$\rangle$` `$\langle$*`base/env`*$\rangle$`)`

    returns a new environment, consisting of a new frame in which the
    symbols in the list $\langle$*variables*$\kern0.08em\rangle$ are
    bound to the corresponding elements in the list
    $\langle$*values*$\kern0.08em\rangle$, where the enclosing
    environment is the environment
    $\langle$*base-env*$\kern0.08em\rangle$.

-   `(define/variable! `$\langle$*`var`*$\rangle$` `$\langle$*`value`*$\rangle$` `$\langle$*`env`*$\rangle$`)`

    adds to the first frame in the environment
    $\langle$*env*$\kern0.08em\rangle$ a new binding that associates the
    variable $\langle$*var*$\kern0.08em\rangle$ with the value
    $\langle$*value*$\kern0.08em\rangle$.

-   `(set/variable/value! `$\langle$*`var`*$\rangle$` `$\langle$*`value`*$\rangle$` `$\langle$*`env`*$\rangle$`)`

    changes the binding of the variable
    $\langle$*var*$\kern0.08em\rangle$ in the environment
    $\langle$*env*$\kern0.08em\rangle$ so that the variable is now bound
    to the value $\langle$*value*$\kern0.08em\rangle$, or signals an
    error if the variable is unbound.

To implement these operations we represent an environment as a list of
frames. The enclosing environment of an environment is the `cdr` of the
list. The empty environment is simply the empty list.

::: scheme
(define (enclosing-environment env) (cdr env)) (define (first-frame env)
(car env)) (define the-empty-environment '())
:::

Each frame of an environment is represented as a pair of lists: a list
of the variables bound in that frame and a list of the associated
values.[^218]

::: scheme
(define (make-frame variables values) (cons variables values)) (define
(frame-variables frame) (car frame)) (define (frame-values frame) (cdr
frame)) (define (add-binding-to-frame! var val frame) (set-car! frame
(cons var (car frame))) (set-cdr! frame (cons val (cdr frame))))
:::

To extend an environment by a new frame that associates variables with
values, we make a frame consisting of the list of variables and the list
of values, and we adjoin this to the environment. We signal an error if
the number of variables does not match the number of values.

::: scheme
(define (extend-environment vars vals base-env) (if (= (length vars)
(length vals)) (cons (make-frame vars vals) base-env) (if (\< (length
vars) (length vals)) (error \"Too many arguments supplied\" vars vals)
(error \"Too few arguments supplied\" vars vals))))
:::

To look up a variable in an environment, we scan the list of variables
in the first frame. If we find the desired variable, we return the
corresponding element in the list of values. If we do not find the
variable in the current frame, we search the enclosing environment, and
so on. If we reach the empty environment, we signal an "unbound
variable" error.

::: scheme
(define (lookup-variable-value var env) (define (env-loop env) (define
(scan vars vals) (cond ((null? vars) (env-loop (enclosing-environment
env))) ((eq? var (car vars)) (car vals)) (else (scan (cdr vars) (cdr
vals))))) (if (eq? env the-empty-environment) (error \"Unbound
variable\" var) (let ((frame (first-frame env))) (scan (frame-variables
frame) (frame-values frame))))) (env-loop env))
:::

To set a variable to a new value in a specified environment, we scan for
the variable, just as in `lookup/variable/value`, and change the
corresponding value when we find it.

::: scheme
(define (set-variable-value! var val env) (define (env-loop env) (define
(scan vars vals) (cond ((null? vars) (env-loop (enclosing-environment
env))) ((eq? var (car vars)) (set-car! vals val)) (else (scan (cdr vars)
(cdr vals))))) (if (eq? env the-empty-environment) (error \"Unbound
variable: SET!\" var) (let ((frame (first-frame env))) (scan
(frame-variables frame) (frame-values frame))))) (env-loop env))
:::

To define a variable, we search the first frame for a binding for the
variable, and change the binding if it exists (just as in
`set/variable/value!`). If no such binding exists, we adjoin one to the
first frame.

::: scheme
(define (define-variable! var val env) (let ((frame (first-frame env)))
(define (scan vars vals) (cond ((null? vars) (add-binding-to-frame! var
val frame)) ((eq? var (car vars)) (set-car! vals val)) (else (scan (cdr
vars) (cdr vals))))) (scan (frame-variables frame) (frame-values
frame))))
:::

The method described here is only one of many plausible ways to
represent environments. Since we used data abstraction to isolate the
rest of the evaluator from the detailed choice of representation, we
could change the environment representation if we wanted to. (See
[Exercise 4.11](#Exercise 4.11).) In a production-quality Lisp system,
the speed of the evaluator's environment operations---especially that of
variable lookup---has a major impact on the performance of the system.
The representation described here, although conceptually simple, is not
efficient and would not ordinarily be used in a production system.[^219]

> **[]{#Exercise 4.11 label="Exercise 4.11"}Exercise 4.11:** Instead of
> representing a frame as a pair of lists, we can represent a frame as a
> list of bindings, where each binding is a name-value pair. Rewrite the
> environment operations to use this alternative representation.

> **[]{#Exercise 4.12 label="Exercise 4.12"}Exercise 4.12:** The
> procedures `set/variable/value!`, `define/variable!` and
> `lookup/variable/value` can be expressed in terms of more abstract
> procedures for traversing the environment structure. Define
> abstractions that capture the common patterns and redefine the three
> procedures in terms of these abstractions.

> **[]{#Exercise 4.13 label="Exercise 4.13"}Exercise 4.13:** Scheme
> allows us to create new bindings for variables by means of `define`,
> but provides no way to get rid of bindings. Implement for the
> evaluator a special form `make/unbound!` that removes the binding of a
> given symbol from the environment in which the `make/unbound!`
> expression is evaluated. This problem is not completely specified. For
> example, should we remove only the binding in the first frame of the
> environment? Complete the specification and justify any choices you
> make.

### Running the Evaluator as a Program {#Section 4.1.4}

Given the evaluator, we have in our hands a description (expressed in
Lisp) of the process by which Lisp expressions are evaluated. One
advantage of expressing the evaluator as a program is that we can run
the program. This gives us, running within Lisp, a working model of how
Lisp itself evaluates expressions. This can serve as a framework for
experimenting with evaluation rules, as we shall do later in this
chapter.

Our evaluator program reduces expressions ultimately to the application
of primitive procedures. Therefore, all that we need to run the
evaluator is to create a mechanism that calls on the underlying Lisp
system to model the application of primitive procedures.

There must be a binding for each primitive procedure name, so that when
`eval` evaluates the operator of an application of a primitive, it will
find an object to pass to `apply`. We thus set up a global environment
that associates unique objects with the names of the primitive
procedures that can appear in the expressions we will be evaluating. The
global environment also includes bindings for the symbols `true` and
`false`, so that they can be used as variables in expressions to be
evaluated.

::: scheme
(define (setup-environment) (let ((initial-env (extend-environment
(primitive-procedure-names) (primitive-procedure-objects)
the-empty-environment))) (define-variable! 'true true initial-env)
(define-variable! 'false false initial-env) initial-env)) (define
the-global-environment (setup-environment))
:::

It does not matter how we represent the primitive procedure objects, so
long as `apply` can identify and apply them by using the procedures
`primitive/procedure?` and `apply/primitive/procedure`. We have chosen
to represent a primitive procedure as a list beginning with the symbol
`primitive` and containing a procedure in the underlying Lisp that
implements that primitive.

::: scheme
(define (primitive-procedure? proc) (tagged-list? proc 'primitive))
(define (primitive-implementation proc) (cadr proc))
:::

`setup/environment` will get the primitive names and implementation
procedures from a list:[^220]

::: scheme
(define primitive-procedures (list (list 'car car) (list 'cdr cdr) (list
'cons cons) (list 'null? null?)  $\color{SchemeDark}\langle$  *more
primitives*  $\color{SchemeDark}\rangle$  )) (define
(primitive-procedure-names) (map car primitive-procedures)) (define
(primitive-procedure-objects) (map (lambda (proc) (list 'primitive (cadr
proc))) primitive-procedures))
:::

To apply a primitive procedure, we simply apply the implementation
procedure to the arguments, using the underlying Lisp system:[^221]

::: scheme
(define (apply-primitive-procedure proc args)
(apply-in-underlying-scheme (primitive-implementation proc) args))
:::

For convenience in running the metacircular evaluator, we provide a
*driver loop* that models the read-eval-print loop of the underlying
Lisp system. It prints a *prompt*, reads an input expression, evaluates
this expression in the global environment, and prints the result. We
precede each printed result by an *output prompt* so as to distinguish
the value of the expression from other output that may be printed.[^222]

::: scheme
(define input-prompt \";;; M-Eval input:\") (define output-prompt \";;;
M-Eval value:\") (define (driver-loop) (prompt-for-input input-prompt)
(let ((input (read))) (let ((output (eval input
the-global-environment))) (announce-output output-prompt) (user-print
output))) (driver-loop)) (define (prompt-for-input string) (newline)
(newline) (display string) (newline)) (define (announce-output string)
(newline) (display string) (newline))
:::

We use a special printing procedure, `user/print`, to avoid printing the
environment part of a compound procedure, which may be a very long list
(or may even contain cycles).

::: scheme
(define (user-print object) (if (compound-procedure? object) (display
(list 'compound-procedure (procedure-parameters object) (procedure-body
object) '\<procedure-env\>)) (display object)))
:::

Now all we need to do to run the evaluator is to initialize the global
environment and start the driver loop. Here is a sample interaction:

::: scheme
(define the-global-environment (setup-environment)) (driver-loop)

 *;;; M-Eval input:*  (define (append x y) (if (null? x) y (cons (car x)
(append (cdr x) y))))  *;;; M-Eval value:*   *ok*   *;;; M-Eval input:*
(append '(a b c) '(d e f))  *;;; M-Eval value:*   *(a b c d e f)*
:::

> **[]{#Exercise 4.14 label="Exercise 4.14"}Exercise 4.14:** Eva Lu Ator
> and Louis Reasoner are each experimenting with the metacircular
> evaluator. Eva types in the definition of `map`, and runs some test
> programs that use it. They work fine. Louis, in contrast, has
> installed the system version of `map` as a primitive for the
> metacircular evaluator. When he tries it, things go terribly wrong.
> Explain why Louis's `map` fails even though Eva's works.

### Data as Programs {#Section 4.1.5}

In thinking about a Lisp program that evaluates Lisp expressions, an
analogy might be helpful. One operational view of the meaning of a
program is that a program is a description of an abstract (perhaps
infinitely large) machine. For example, consider the familiar program to
compute factorials:

::: scheme
(define (factorial n) (if (= n 1) 1 (\* (factorial (- n 1)) n)))
:::

We may regard this program as the description of a machine containing
parts that decrement, multiply, and test for equality, together with a
two-position switch and another factorial machine. (The factorial
machine is infinite because it contains another factorial machine within
it.) [Figure 4.2](#Figure 4.2) is a flow diagram for the factorial
machine, showing how the parts are wired together.

In a similar way, we can regard the evaluator as a very special machine
that takes as input a description of a machine. Given this input, the
evaluator configures itself to emulate the machine described. For
example, if we feed our evaluator the definition of `factorial`, as
shown in [Figure 4.3](#Figure 4.3), the evaluator will be able to
compute factorials.

[]{#Figure 4.2 label="Figure 4.2"}

![image](fig/chap4/Fig4.2.pdf){width="84mm"}

> **Figure 4.2:** The factorial program, viewed as an abstract machine.

From this perspective, our evaluator is seen to be a *universal
machine*. It mimics other machines when these are described as Lisp
programs.[^223] This is striking. Try to imagine an analogous evaluator
for electrical circuits. This would be a circuit that takes as input a
signal encoding the plans for some other circuit, such as a filter.
Given this input, the circuit evaluator would then behave like a filter
with the same description. Such a universal electrical circuit is almost
unimaginably complex. It is remarkable that the program evaluator is a
rather simple program.[^224]

[]{#Figure 4.3 label="Figure 4.3"}

![image](fig/chap4/Fig4.3.pdf){width="69mm"}

**Figure 4.3:** The evaluator emulating a factorial machine.

Another striking aspect of the evaluator is that it acts as a bridge
between the data objects that are manipulated by our programming
language and the programming language itself. Imagine that the evaluator
program (implemented in Lisp) is running, and that a user is typing
expressions to the evaluator and observing the results. From the
perspective of the user, an input expression such as `(* x x)` is an
expression in the programming language, which the evaluator should
execute. From the perspective of the evaluator, however, the expression
is simply a list (in this case, a list of three symbols: `*`, `x`, and
`x`) that is to be manipulated according to a well-defined set of rules.

That the user's programs are the evaluator's data need not be a source
of confusion. In fact, it is sometimes convenient to ignore this
distinction, and to give the user the ability to explicitly evaluate a
data object as a Lisp expression, by making `eval` available for use in
programs. Many Lisp dialects provide a primitive `eval` procedure that
takes as arguments an expression and an environment and evaluates the
expression relative to the environment.[^225] Thus,

::: scheme
(eval '(\* 5 5) user-initial-environment)
:::

and

::: scheme
(eval (cons '\* (list 5 5)) user-initial-environment)
:::

will both return 25.[^226]

> **[]{#Exercise 4.15 label="Exercise 4.15"}Exercise 4.15:** Given a
> one-argument procedure `p` and an object `a`, `p` is said to "halt" on
> `a` if evaluating the expression `(p a)` returns a value (as opposed
> to terminating with an error message or running forever). Show that it
> is impossible to write a procedure `halts?` that correctly determines
> whether `p` halts on `a` for any procedure `p` and object `a`. Use the
> following reasoning: If you had such a procedure `halts?`, you could
> implement the following program:
>
> ::: scheme
> (define (run-forever) (run-forever)) (define (try p) (if (halts? p p)
> (run-forever) 'halted))
> :::
>
> Now consider evaluating the expression `(try try)` and show that any
> possible outcome (either halting or running forever) violates the
> intended behavior of `halts?`.[^227]

### Internal Definitions {#Section 4.1.6}

Our environment model of evaluation and our metacircular evaluator
execute definitions in sequence, extending the environment frame one
definition at a time. This is particularly convenient for interactive
program development, in which the programmer needs to freely mix the
application of procedures with the definition of new procedures.
However, if we think carefully about the internal definitions used to
implement block structure (introduced in [Section
1.1.8](#Section 1.1.8)), we will find that name-by-name extension of the
environment may not be the best way to define local variables.

Consider a procedure with internal definitions, such as

::: scheme
(define (f x) (define (even? n) (if (= n 0) true (odd? (- n 1))))
(define (odd? n) (if (= n 0) false (even? (- n 1))))
 $\color{SchemeDark}\langle$  *rest of body of
`f`*  $\color{SchemeDark}\rangle$ )
:::

Our intention here is that the name `odd?` in the body of the procedure
`even?` should refer to the procedure `odd?` that is defined after
`even?`. The scope of the name `odd?` is the entire body of `f`, not
just the portion of the body of `f` starting at the point where the
`define` for `odd?` occurs. Indeed, when we consider that `odd?` is
itself defined in terms of `even?`---so that `even?` and `odd?` are
mutually recursive procedures---we see that the only satisfactory
interpretation of the two `define`s is to regard them as if the names
`even?` and `odd?` were being added to the environment simultaneously.
More generally, in block structure, the scope of a local name is the
entire procedure body in which the `define` is evaluated.

As it happens, our interpreter will evaluate calls to `f` correctly, but
for an "accidental" reason: Since the definitions of the internal
procedures come first, no calls to these procedures will be evaluated
until all of them have been defined. Hence, `odd?` will have been
defined by the time `even?` is executed. In fact, our sequential
evaluation mechanism will give the same result as a mechanism that
directly implements simultaneous definition for any procedure in which
the internal definitions come first in a body and evaluation of the
value expressions for the defined variables doesn't actually use any of
the defined variables. (For an example of a procedure that doesn't obey
these restrictions, so that sequential definition isn't equivalent to
simultaneous definition, see [Exercise 4.19](#Exercise 4.19).)[^228]

There is, however, a simple way to treat definitions so that internally
defined names have truly simultaneous scope---just create all local
variables that will be in the current environment before evaluating any
of the value expressions. One way to do this is by a syntax
transformation on `lambda` expressions. Before evaluating the body of a
`lambda` expression, we "scan out" and eliminate all the internal
definitions in the body. The internally defined variables will be
created with a `let` and then set to their values by assignment. For
example, the procedure

::: scheme
(lambda
 $\color{SchemeDark}\langle$  *vars*  $\color{SchemeDark}\rangle$
(define u
 $\color{SchemeDark}\langle$  *e1*  $\color{SchemeDark}\rangle$ )
(define v
 $\color{SchemeDark}\langle$  *e2*  $\color{SchemeDark}\rangle$ )
 $\color{SchemeDark}\langle$  *e3*  $\color{SchemeDark}\rangle$ )
:::

would be transformed into

::: scheme
(lambda
 $\color{SchemeDark}\langle$  *vars*  $\color{SchemeDark}\rangle$  (let
((u '\*unassigned\*) (v '\*unassigned\*)) (set! u
 $\color{SchemeDark}\langle$  *e1*  $\color{SchemeDark}\rangle$ ) (set!
v  $\color{SchemeDark}\langle$  *e2*  $\color{SchemeDark}\rangle$ )
 $\color{SchemeDark}\langle$  *e3*  $\color{SchemeDark}\rangle$ ))
:::

where `*unassigned*` is a special symbol that causes looking up a
variable to signal an error if an attempt is made to use the value of
the not-yet-assigned variable.

An alternative strategy for scanning out internal definitions is shown
in [Exercise 4.18](#Exercise 4.18). Unlike the transformation shown
above, this enforces the restriction that the defined variables' values
can be evaluated without using any of the variables' values.[^229]

> **[]{#Exercise 4.16 label="Exercise 4.16"}Exercise 4.16:** In this
> exercise we implement the method just described for interpreting
> internal definitions. We assume that the evaluator supports `let` (see
> [Exercise 4.6](#Exercise 4.6)).
>
> a.  Change `lookup/variable/value` ([Section 4.1.3](#Section 4.1.3))
>     to signal an error if the value it finds is the symbol
>     `*unassigned*`.
>
> b.  Write a procedure `scan/out/defines` that takes a procedure body
>     and returns an equivalent one that has no internal definitions, by
>     making the transformation described above.
>
> c.  Install `scan/out/defines` in the interpreter, either in
>     `make/procedure` or in `procedure/body` (see [Section
>     4.1.3](#Section 4.1.3)). Which place is better? Why?

> **[]{#Exercise 4.17 label="Exercise 4.17"}Exercise 4.17:** Draw
> diagrams of the environment in effect when evaluating the expression
> $\langle$*e3*$\kern0.1em\rangle$ in the procedure in the text,
> comparing how this will be structured when definitions are interpreted
> sequentially with how it will be structured if definitions are scanned
> out as described. Why is there an extra frame in the transformed
> program? Explain why this difference in environment structure can
> never make a difference in the behavior of a correct program. Design a
> way to make the interpreter implement the "simultaneous" scope rule
> for internal definitions without constructing the extra frame.

> **[]{#Exercise 4.18 label="Exercise 4.18"}Exercise 4.18:** Consider an
> alternative strategy for scanning out definitions that translates the
> example in the text to
>
> ::: scheme
> (lambda
>  $\color{SchemeDark}\langle$  *vars*  $\color{SchemeDark}\rangle$
> (let ((u '\*unassigned\*) (v '\*unassigned\*)) (let ((a
>  $\color{SchemeDark}\langle$  *e1*  $\color{SchemeDark}\rangle$ ) (b
>  $\color{SchemeDark}\langle$  *e2*  $\color{SchemeDark}\rangle$ ))
> (set! u a) (set! v b))
>  $\color{SchemeDark}\langle$  *e3*  $\color{SchemeDark}\rangle$ ))
> :::
>
> Here `a` and `b` are meant to represent new variable names, created by
> the interpreter, that do not appear in the user's program. Consider
> the `solve` procedure from [Section 3.5.4](#Section 3.5.4):
>
> ::: scheme
> (define (solve f y0 dt) (define y (integral (delay dy) y0 dt)) (define
> dy (stream-map f y)) y)
> :::
>
> Will this procedure work if internal definitions are scanned out as
> shown in this exercise? What if they are scanned out as shown in the
> text? Explain.

> **[]{#Exercise 4.19 label="Exercise 4.19"}Exercise 4.19:** Ben
> Bitdiddle, Alyssa P. Hacker, and Eva Lu Ator are arguing about the
> desired result of evaluating the expression
>
> ::: scheme
> (let ((a 1)) (define (f x) (define b (+ a x)) (define a 5) (+ a b)) (f
> 10))
> :::
>
> Ben asserts that the result should be obtained using the sequential
> rule for `define`: `b` is defined to be 11, then `a` is defined to be
> 5, so the result is 16. Alyssa objects that mutual recursion requires
> the simultaneous scope rule for internal procedure definitions, and
> that it is unreasonable to treat procedure names differently from
> other names. Thus, she argues for the mechanism implemented in
> [Exercise 4.16](#Exercise 4.16). This would lead to `a` being
> unassigned at the time that the value for `b` is to be computed.
> Hence, in Alyssa's view the procedure should produce an error. Eva has
> a third opinion. She says that if the definitions of `a` and `b` are
> truly meant to be simultaneous, then the value 5 for `a` should be
> used in evaluating `b`. Hence, in Eva's view `a` should be 5, `b`
> should be 15, and the result should be 20. Which (if any) of these
> viewpoints do you support? Can you devise a way to implement internal
> definitions so that they behave as Eva prefers?[^230]

> **[]{#Exercise 4.20 label="Exercise 4.20"}Exercise 4.20:** Because
> internal definitions look sequential but are actually simultaneous,
> some people prefer to avoid them entirely, and use the special form
> `letrec` instead. `letrec` looks like `let`, so it is not surprising
> that the variables it binds are bound simultaneously and have the same
> scope as each other. The sample procedure `f` above can be written
> without internal definitions, but with exactly the same meaning, as
>
> ::: scheme
> (define (f x) (letrec ((even? (lambda (n) (if (= n 0) true (odd? (- n
> 1))))) (odd? (lambda (n) (if (= n 0) false (even? (- n 1))))))
>  $\color{SchemeDark}\langle$  *rest of body of
> `f`*  $\color{SchemeDark}\rangle$ ))
> :::
>
> `letrec` expressions, which have the form
>
> ::: scheme
> (letrec
> (( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
>  $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$ )
>  $\dots$
> ( $\color{SchemeDark}\langle$  *var*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$
>  $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ ))
>  $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
> :::
>
> are a variation on `let` in which the expressions
> $\langle$*exp*$_k\rangle$ that provide the initial values for the
> variables $\langle$*var*$_k\rangle$ are evaluated in an environment
> that includes all the `letrec` bindings. This permits recursion in the
> bindings, such as the mutual recursion of `even?` and `odd?` in the
> example above, or the evaluation of 10 factorial with
>
> ::: scheme
> (letrec ((fact (lambda (n) (if (= n 1) 1 (\* n (fact (- n 1)))))))
> (fact 10))
> :::
>
> a.  Implement `letrec` as a derived expression, by transforming a
>     `letrec` expression into a `let` expression as shown in the text
>     above or in [Exercise 4.18](#Exercise 4.18). That is, the `letrec`
>     variables should be created with a `let` and then be assigned
>     their values with `set!`.
>
> b.  Louis Reasoner is confused by all this fuss about internal
>     definitions. The way he sees it, if you don't like to use `define`
>     inside a procedure, you can just use `let`. Illustrate what is
>     loose about his reasoning by drawing an environment diagram that
>     shows the environment in which the $\langle$*rest of body of
>     `f`*$\kern0.08em\rangle$ is evaluated during evaluation of the
>     expression `(f 5)`, with `f` defined as in this exercise. Draw an
>     environment diagram for the same evaluation, but with `let` in
>     place of `letrec` in the definition of `f`.

> **[]{#Exercise 4.21 label="Exercise 4.21"}Exercise 4.21:** Amazingly,
> Louis's intuition in [Exercise 4.20](#Exercise 4.20) is correct. It is
> indeed possible to specify recursive procedures without using `letrec`
> (or even `define`), although the method for accomplishing this is much
> more subtle than Louis imagined. The following expression computes 10
> factorial by applying a recursive factorial procedure:[^231]
>
> ::: scheme
> ((lambda (n) ((lambda (fact) (fact fact n)) (lambda (ft k) (if (= k 1)
> 1 (\* k (ft ft (- k 1))))))) 10)
> :::
>
> a.  Check (by evaluating the expression) that this really does compute
>     factorials. Devise an analogous expression for computing Fibonacci
>     numbers.
>
> b.  Consider the following procedure, which includes mutually
>     recursive internal definitions:
>
>     ::: scheme
>     (define (f x) (define (even? n) (if (= n 0) true (odd? (- n 1))))
>     (define (odd? n) (if (= n 0) false (even? (- n 1)))) (even? x))
>     :::
>
>     Fill in the missing expressions to complete an alternative
>     definition of `f`, which uses neither internal definitions nor
>     `letrec`:
>
>     ::: scheme
>     (define (f x) ((lambda (even? odd?) (even? even? odd? x)) (lambda
>     (ev? od? n) (if (= n 0) true (od?
>      $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>      $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>      $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ )))
>     (lambda (ev? od? n) (if (= n 0) false (ev?
>      $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>      $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>      $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$ )))))
>     :::

### Separating Syntactic Analysis from Execution {#Section 4.1.7}

The evaluator implemented above is simple, but it is very inefficient,
because the syntactic analysis of expressions is interleaved with their
execution. Thus if a program is executed many times, its syntax is
analyzed many times. Consider, for example, evaluating `(factorial 4)`
using the following definition of `factorial`:

::: scheme
(define (factorial n) (if (= n 1) 1 (\* (factorial (- n 1)) n)))
:::

Each time `factorial` is called, the evaluator must determine that the
body is an `if` expression and extract the predicate. Only then can it
evaluate the predicate and dispatch on its value. Each time it evaluates
the expression `(* (factorial (- n 1)) n)`, or the subexpressions
`(factorial (- n 1))` and `(- n 1)`, the evaluator must perform the case
analysis in `eval` to determine that the expression is an application,
and must extract its operator and operands. This analysis is expensive.
Performing it repeatedly is wasteful.

We can transform the evaluator to be significantly more efficient by
arranging things so that syntactic analysis is performed only
once.[^232] We split `eval`, which takes an expression and an
environment, into two parts. The procedure `analyze` takes only the
expression. It performs the syntactic analysis and returns a new
procedure, the *execution procedure*, that encapsulates the work to be
done in executing the analyzed expression. The execution procedure takes
an environment as its argument and completes the evaluation. This saves
work because `analyze` will be called only once on an expression, while
the execution procedure may be called many times.

With the separation into analysis and execution, `eval` now becomes

::: scheme
(define (eval exp env) ((analyze exp) env))
:::

The result of calling `analyze` is the execution procedure to be applied
to the environment. The `analyze` procedure is the same case analysis as
performed by the original `eval` of [Section 4.1.1](#Section 4.1.1),
except that the procedures to which we dispatch perform only analysis,
not full evaluation:

::: scheme
(define (analyze exp) (cond ((self-evaluating? exp)
(analyze-self-evaluating exp)) ((quoted? exp) (analyze-quoted exp))
((variable? exp) (analyze-variable exp)) ((assignment? exp)
(analyze-assignment exp)) ((definition? exp) (analyze-definition exp))
((if? exp) (analyze-if exp)) ((lambda? exp) (analyze-lambda exp))
((begin? exp) (analyze-sequence (begin-actions exp))) ((cond? exp)
(analyze (cond-\>if exp))) ((application? exp) (analyze-application
exp)) (else (error \"Unknown expression type: ANALYZE\" exp))))
:::

Here is the simplest syntactic analysis procedure, which handles
self-evaluating expressions. It returns an execution procedure that
ignores its environment argument and just returns the expression:

::: scheme
(define (analyze-self-evaluating exp) (lambda (env) exp))
:::

For a quoted expression, we can gain a little efficiency by extracting
the text of the quotation only once, in the analysis phase, rather than
in the execution phase.

::: scheme
(define (analyze-quoted exp) (let ((qval (text-of-quotation exp)))
(lambda (env) qval)))
:::

Looking up a variable value must still be done in the execution phase,
since this depends upon knowing the environment.[^233]

::: scheme
(define (analyze-variable exp) (lambda (env) (lookup-variable-value exp
env)))
:::

`analyze/assignment` also must defer actually setting the variable until
the execution, when the environment has been supplied. However, the fact
that the `assignment/value` expression can be analyzed (recursively)
during analysis is a major gain in efficiency, because the
`assignment/value` expression will now be analyzed only once. The same
holds true for definitions.

::: scheme
(define (analyze-assignment exp) (let ((var (assignment-variable exp))
(vproc (analyze (assignment-value exp)))) (lambda (env)
(set-variable-value! var (vproc env) env) 'ok))) (define
(analyze-definition exp) (let ((var (definition-variable exp)) (vproc
(analyze (definition-value exp)))) (lambda (env) (define-variable! var
(vproc env) env) 'ok)))
:::

For `if` expressions, we extract and analyze the predicate, consequent,
and alternative at analysis time.

::: scheme
(define (analyze-if exp) (let ((pproc (analyze (if-predicate exp)))
(cproc (analyze (if-consequent exp))) (aproc (analyze (if-alternative
exp)))) (lambda (env) (if (true? (pproc env)) (cproc env) (aproc
env)))))
:::

Analyzing a `lambda` expression also achieves a major gain in
efficiency: We analyze the `lambda` body only once, even though
procedures resulting from evaluation of the `lambda` may be applied many
times.

::: scheme
(define (analyze-lambda exp) (let ((vars (lambda-parameters exp)) (bproc
(analyze-sequence (lambda-body exp)))) (lambda (env) (make-procedure
vars bproc env))))
:::

Analysis of a sequence of expressions (as in a `begin` or the body of a
`lambda` expression) is more involved.[^234] Each expression in the
sequence is analyzed, yielding an execution procedure. These execution
procedures are combined to produce an execution procedure that takes an
environment as argument and sequentially calls each individual execution
procedure with the environment as argument.

::: scheme
(define (analyze-sequence exps) (define (sequentially proc1 proc2)
(lambda (env) (proc1 env) (proc2 env))) (define (loop first-proc
rest-procs) (if (null? rest-procs) first-proc (loop (sequentially
first-proc (car rest-procs)) (cdr rest-procs)))) (let ((procs (map
analyze exps))) (if (null? procs) (error \"Empty sequence: ANALYZE\"))
(loop (car procs) (cdr procs))))
:::

To analyze an application, we analyze the operator and operands and
construct an execution procedure that calls the operator execution
procedure (to obtain the actual procedure to be applied) and the operand
execution procedures (to obtain the actual arguments). We then pass
these to `execute/application`, which is the analog of `apply` in
[Section 4.1.1](#Section 4.1.1). `execute/application` differs from
`apply` in that the procedure body for a compound procedure has already
been analyzed, so there is no need to do further analysis. Instead, we
just call the execution procedure for the body on the extended
environment.

::: scheme
(define (analyze-application exp) (let ((fproc (analyze (operator exp)))
(aprocs (map analyze (operands exp)))) (lambda (env)
(execute-application (fproc env) (map (lambda (aproc) (aproc env))
aprocs))))) (define (execute-application proc args) (cond
((primitive-procedure? proc) (apply-primitive-procedure proc args))
((compound-procedure? proc) ((procedure-body proc) (extend-environment
(procedure-parameters proc) args (procedure-environment proc)))) (else
(error \"Unknown procedure type: EXECUTE-APPLICATION\" proc))))
:::

Our new evaluator uses the same data structures, syntax procedures, and
run-time support procedures as in sections [Section
4.1.2](#Section 4.1.2), [Section 4.1.3](#Section 4.1.3), and [Section
4.1.4](#Section 4.1.4).

> **[]{#Exercise 4.22 label="Exercise 4.22"}Exercise 4.22:** Extend the
> evaluator in this section to support the special form `let`. (See
> [Exercise 4.6](#Exercise 4.6).)

> **[]{#Exercise 4.23 label="Exercise 4.23"}Exercise 4.23:** Alyssa P.
> Hacker doesn't understand why `analyze/sequence` needs to be so
> complicated. All the other analysis procedures are straightforward
> transformations of the corresponding evaluation procedures (or `eval`
> clauses) in [Section 4.1.1](#Section 4.1.1). She expected
> `analyze/sequence` to look like this:
>
> ::: scheme
> (define (analyze-sequence exps) (define (execute-sequence procs env)
> (cond ((null? (cdr procs)) ((car procs) env)) (else ((car procs) env)
> (execute-sequence (cdr procs) env)))) (let ((procs (map analyze
> exps))) (if (null? procs) (error \"Empty sequence: ANALYZE\")) (lambda
> (env) (execute-sequence procs env))))
> :::
>
> Eva Lu Ator explains to Alyssa that the version in the text does more
> of the work of evaluating a sequence at analysis time. Alyssa's
> sequence-execution procedure, rather than having the calls to the
> individual execution procedures built in, loops through the procedures
> in order to call them: In effect, although the individual expressions
> in the sequence have been analyzed, the sequence itself has not been.
>
> Compare the two versions of `analyze/sequence`. For example, consider
> the common case (typical of procedure bodies) where the sequence has
> just one expression. What work will the execution procedure produced
> by Alyssa's program do? What about the execution procedure produced by
> the program in the text above? How do the two versions compare for a
> sequence with two expressions?

> **[]{#Exercise 4.24 label="Exercise 4.24"}Exercise 4.24:** Design and
> carry out some experiments to compare the speed of the original
> metacircular evaluator with the version in this section. Use your
> results to estimate the fraction of time that is spent in analysis
> versus execution for various procedures.

## Variations on a Scheme --- Lazy Evaluation {#Section 4.2}

Now that we have an evaluator expressed as a Lisp program, we can
experiment with alternative choices in language design simply by
modifying the evaluator. Indeed, new languages are often invented by
first writing an evaluator that embeds the new language within an
existing high-level language. For example, if we wish to discuss some
aspect of a proposed modification to Lisp with another member of the
Lisp community, we can supply an evaluator that embodies the change. The
recipient can then experiment with the new evaluator and send back
comments as further modifications. Not only does the high-level
implementation base make it easier to test and debug the evaluator; in
addition, the embedding enables the designer to snarf [^235] features
from the underlying language, just as our embedded Lisp evaluator uses
primitives and control structure from the underlying Lisp. Only later
(if ever) need the designer go to the trouble of building a complete
implementation in a low-level language or in hardware. In this section
and the next we explore some variations on Scheme that provide
significant additional expressive power.

### Normal Order and Applicative Order {#Section 4.2.1}

In [Section 1.1](#Section 1.1), where we began our discussion of models
of evaluation, we noted that Scheme is an *applicative-order* language,
namely, that all the arguments to Scheme procedures are evaluated when
the procedure is applied. In contrast, *normal-order* languages delay
evaluation of procedure arguments until the actual argument values are
needed. Delaying evaluation of procedure arguments until the last
possible moment (e.g., until they are required by a primitive operation)
is called *lazy evaluation*.[^236] Consider the procedure

::: scheme
(define (try a b) (if (= a 0) 1 b))
:::

Evaluating `(try 0 (/ 1 0))` generates an error in Scheme. With lazy
evaluation, there would be no error. Evaluating the expression would
return 1, because the argument `(/ 1 0)` would never be evaluated.

An example that exploits lazy evaluation is the definition of a
procedure `unless`

::: scheme
(define (unless condition usual-value exceptional-value) (if condition
exceptional-value usual-value))
:::

that can be used in expressions such as

::: scheme
(unless (= b 0) (/ a b) (begin (display \"exception: returning 0\") 0))
:::

This won't work in an applicative-order language because both the usual
value and the exceptional value will be evaluated before `unless` is
called (compare [Exercise 1.6](#Exercise 1.6)). An advantage of lazy
evaluation is that some procedures, such as `unless`, can do useful
computation even if evaluation of some of their arguments would produce
errors or would not terminate.

If the body of a procedure is entered before an argument has been
evaluated we say that the procedure is *non-strict* in that argument. If
the argument is evaluated before the body of the procedure is entered we
say that the procedure is *strict* in that argument.[^237] In a purely
applicative-order language, all procedures are strict in each argument.
In a purely normal-order language, all compound procedures are
non-strict in each argument, and primitive procedures may be either
strict or non-strict. There are also languages (see [Exercise
4.31](#Exercise 4.31)) that give programmers detailed control over the
strictness of the procedures they define.

A striking example of a procedure that can usefully be made non-strict
is `cons` (or, in general, almost any constructor for data structures).
One can do useful computation, combining elements to form data
structures and operating on the resulting data structures, even if the
values of the elements are not known. It makes perfect sense, for
instance, to compute the length of a list without knowing the values of
the individual elements in the list. We will exploit this idea in
[Section 4.2.3](#Section 4.2.3) to implement the streams of [Chapter
3](#Chapter 3) as lists formed of non-strict `cons` pairs.

> **[]{#Exercise 4.25 label="Exercise 4.25"}Exercise 4.25:** Suppose
> that (in ordinary applicative-order Scheme) we define `unless` as
> shown above and then define `factorial` in terms of `unless` as
>
> ::: scheme
> (define (factorial n) (unless (= n 1) (\* n (factorial (- n 1))) 1))
> :::
>
> What happens if we attempt to evaluate `(factorial 5)`? Will our
> definitions work in a normal-order language?

> **[]{#Exercise 4.26 label="Exercise 4.26"}Exercise 4.26:** Ben
> Bitdiddle and Alyssa P. Hacker disagree over the importance of lazy
> evaluation for implementing things such as `unless`. Ben points out
> that it's possible to implement `unless` in applicative order as a
> special form. Alyssa counters that, if one did that, `unless` would be
> merely syntax, not a procedure that could be used in conjunction with
> higher-order procedures. Fill in the details on both sides of the
> argument. Show how to implement `unless` as a derived expression (like
> `cond` or `let`), and give an example of a situation where it might be
> useful to have `unless` available as a procedure, rather than as a
> special form.

### An Interpreter with Lazy Evaluation {#Section 4.2.2}

In this section we will implement a normal-order language that is the
same as Scheme except that compound procedures are non-strict in each
argument. Primitive procedures will still be strict. It is not difficult
to modify the evaluator of [Section 4.1.1](#Section 4.1.1) so that the
language it interprets behaves this way. Almost all the required changes
center around procedure application.

The basic idea is that, when applying a procedure, the interpreter must
determine which arguments are to be evaluated and which are to be
delayed. The delayed arguments are not evaluated; instead, they are
transformed into objects called *thunks*.[^238] The thunk must contain
the information required to produce the value of the argument when it is
needed, as if it had been evaluated at the time of the application.
Thus, the thunk must contain the argument expression and the environment
in which the procedure application is being evaluated.

The process of evaluating the expression in a thunk is called
*forcing*.[^239] In general, a thunk will be forced only when its value
is needed: when it is passed to a primitive procedure that will use the
value of the thunk; when it is the value of a predicate of a
conditional; and when it is the value of an operator that is about to be
applied as a procedure. One design choice we have available is whether
or not to *memoize* thunks, as we did with delayed objects in [Section
3.5.1](#Section 3.5.1). With memoization, the first time a thunk is
forced, it stores the value that is computed. Subsequent forcings simply
return the stored value without repeating the computation. We'll make
our interpreter memoize, because this is more efficient for many
applications. There are tricky considerations here, however.[^240]

#### Modifying the evaluator {#modifying-the-evaluator .unnumbered}

The main difference between the lazy evaluator and the one in [Section
4.1](#Section 4.1) is in the handling of procedure applications in
`eval` and `apply`.

The `application?` clause of `eval` becomes

::: scheme
((application? exp) (apply (actual-value (operator exp) env) (operands
exp) env))
:::

This is almost the same as the `application?` clause of `eval` in
[Section 4.1.1](#Section 4.1.1). For lazy evaluation, however, we call
`apply` with the operand expressions, rather than the arguments produced
by evaluating them. Since we will need the environment to construct
thunks if the arguments are to be delayed, we must pass this as well. We
still evaluate the operator, because `apply` needs the actual procedure
to be applied in order to dispatch on its type (primitive versus
compound) and apply it.

Whenever we need the actual value of an expression, we use

::: scheme
(define (actual-value exp env) (force-it (eval exp env)))
:::

instead of just `eval`, so that if the expression's value is a thunk, it
will be forced.

Our new version of `apply` is also almost the same as the version in
[Section 4.1.1](#Section 4.1.1). The difference is that `eval` has
passed in unevaluated operand expressions: For primitive procedures
(which are strict), we evaluate all the arguments before applying the
primitive; for compound procedures (which are non-strict) we delay all
the arguments before applying the procedure.

::: scheme
(define (apply procedure arguments env) (cond ((primitive-procedure?
procedure) (apply-primitive-procedure procedure (list-of-arg-values
arguments env)))  [; changed]{.roman}  ((compound-procedure? procedure)
(eval-sequence (procedure-body procedure) (extend-environment
(procedure-parameters procedure) (list-of-delayed-args arguments env)
 [; changed]{.roman}  (procedure-environment procedure)))) (else (error
\"Unknown procedure type: APPLY\" procedure))))
:::

The procedures that process the arguments are just like `list/of/values`
from [Section 4.1.1](#Section 4.1.1), except that `list/of/delayed/args`
delays the arguments instead of evaluating them, and
`list/of/arg/values` uses `actual/value` instead of `eval`:

::: scheme
(define (list-of-arg-values exps env) (if (no-operands? exps) '() (cons
(actual-value (first-operand exps) env) (list-of-arg-values
(rest-operands exps) env)))) (define (list-of-delayed-args exps env) (if
(no-operands? exps) '() (cons (delay-it (first-operand exps) env)
(list-of-delayed-args (rest-operands exps) env))))
:::

The other place we must change the evaluator is in the handling of `if`,
where we must use `actual/value` instead of `eval` to get the value of
the predicate expression before testing whether it is true or false:

::: scheme
(define (eval-if exp env) (if (true? (actual-value (if-predicate exp)
env)) (eval (if-consequent exp) env) (eval (if-alternative exp) env)))
:::

Finally, we must change the `driver/loop` procedure ([Section
4.1.4](#Section 4.1.4)) to use `actual/value` instead of `eval`, so that
if a delayed value is propagated back to the read-eval-print loop, it
will be forced before being printed. We also change the prompts to
indicate that this is the lazy evaluator:

::: scheme
(define input-prompt \";;; L-Eval input:\") (define output-prompt \";;;
L-Eval value:\") (define (driver-loop) (prompt-for-input input-prompt)
(let ((input (read))) (let ((output (actual-value input
the-global-environment))) (announce-output output-prompt) (user-print
output))) (driver-loop))
:::

With these changes made, we can start the evaluator and test it. The
successful evaluation of the `try` expression discussed in [Section
4.2.1](#Section 4.2.1) indicates that the interpreter is performing lazy
evaluation:

::: scheme
(define the-global-environment (setup-environment)) (driver-loop)  *;;;
L-Eval input:*  (define (try a b) (if (= a 0) 1 b))  *;;; L-Eval
value:*   *ok*   *;;; L-Eval input:*  (try 0 (/ 1 0))  *;;; L-Eval
value:*   *1*
:::

#### Representing thunks {#representing-thunks .unnumbered}

Our evaluator must arrange to create thunks when procedures are applied
to arguments and to force these thunks later. A thunk must package an
expression together with the environment, so that the argument can be
produced later. To force the thunk, we simply extract the expression and
environment from the thunk and evaluate the expression in the
environment. We use `actual/value` rather than `eval` so that in case
the value of the expression is itself a thunk, we will force that, and
so on, until we reach something that is not a thunk:

::: scheme
(define (force-it obj) (if (thunk? obj) (actual-value (thunk-exp obj)
(thunk-env obj)) obj))
:::

One easy way to package an expression with an environment is to make a
list containing the expression and the environment. Thus, we create a
thunk as follows:

::: scheme
(define (delay-it exp env) (list 'thunk exp env)) (define (thunk? obj)
(tagged-list? obj 'thunk)) (define (thunk-exp thunk) (cadr thunk))
(define (thunk-env thunk) (caddr thunk))
:::

Actually, what we want for our interpreter is not quite this, but rather
thunks that have been memoized. When a thunk is forced, we will turn it
into an evaluated thunk by replacing the stored expression with its
value and changing the `thunk` tag so that it can be recognized as
already evaluated.[^241]

::: scheme
(define (evaluated-thunk? obj) (tagged-list? obj 'evaluated-thunk))
(define (thunk-value evaluated-thunk) (cadr evaluated-thunk)) (define
(force-it obj) (cond ((thunk? obj) (let ((result (actual-value
(thunk-exp obj) (thunk-env obj)))) (set-car! obj 'evaluated-thunk)
(set-car! (cdr obj) result)  [; replace `exp` with its value]{.roman}
(set-cdr! (cdr obj) '())  [; forget unneeded `env`]{.roman}  result))
((evaluated-thunk? obj) (thunk-value obj)) (else obj)))
:::

Notice that the same `delay/it` procedure works both with and without
memoization.

> **[]{#Exercise 4.27 label="Exercise 4.27"}Exercise 4.27:** Suppose we
> type in the following definitions to the lazy evaluator:
>
> ::: scheme
> (define count 0) (define (id x) (set! count (+ count 1)) x)
> :::
>
> Give the missing values in the following sequence of interactions, and
> explain your answers.[^242]
>
> ::: scheme
> (define w (id (id 10)))  *;;; L-Eval input:*  count  *;;; L-Eval
> value:*
>  $\color{SchemeDark}\langle$  *response*  $\color{SchemeDark}\rangle$
>  *;;; L-Eval input:*  w  *;;; L-Eval value:*
>  $\color{SchemeDark}\langle$  *response*  $\color{SchemeDark}\rangle$
>  *;;; L-Eval input:*  count  *;;; L-Eval value:*
>  $\color{SchemeDark}\langle$  *response*  $\color{SchemeDark}\rangle$
> :::

> **[]{#Exercise 4.28 label="Exercise 4.28"}Exercise 4.28:** `eval` uses
> `actual/value` rather than `eval` to evaluate the operator before
> passing it to `apply`, in order to force the value of the operator.
> Give an example that demonstrates the need for this forcing.
>
> **[]{#Exercise 4.29 label="Exercise 4.29"}Exercise 4.29:** Exhibit a
> program that you would expect to run much more slowly without
> memoization than with memoization. Also, consider the following
> interaction, where the `id` procedure is defined as in [Exercise
> 4.27](#Exercise 4.27) and `count` starts at 0:
>
> ::: scheme
> (define (square x) (\* x x))  *;;; L-Eval input:*  (square (id 10))
>  *;;; L-Eval value:*
>  $\color{SchemeDark}\langle$  *response*  $\color{SchemeDark}\rangle$
>  *;;; L-Eval input:*  count  *;;; L-Eval value:*
>  $\color{SchemeDark}\langle$  *response*  $\color{SchemeDark}\rangle$
> :::
>
> Give the responses both when the evaluator memoizes and when it does
> not.

> **[]{#Exercise 4.30 label="Exercise 4.30"}Exercise 4.30:** Cy D. Fect,
> a reformed C programmer, is worried that some side effects may never
> take place, because the lazy evaluator doesn't force the expressions
> in a sequence. Since the value of an expression in a sequence other
> than the last one is not used (the expression is there only for its
> effect, such as assigning to a variable or printing), there can be no
> subsequent use of this value (e.g., as an argument to a primitive
> procedure) that will cause it to be forced. Cy thus thinks that when
> evaluating sequences, we must force all expressions in the sequence
> except the final one. He proposes to modify `eval/sequence` from
> [Section 4.1.1](#Section 4.1.1) to use `actual/value` rather than
> `eval`:
>
> ::: scheme
> (define (eval-sequence exps env) (cond ((last-exp? exps) (eval
> (first-exp exps) env)) (else (actual-value (first-exp exps) env)
> (eval-sequence (rest-exps exps) env))))
> :::
>
> a.  Ben Bitdiddle thinks Cy is wrong. He shows Cy the `for/each`
>     procedure described in [Exercise 2.23](#Exercise 2.23), which
>     gives an important example of a sequence with side effects:
>
>     ::: scheme
>     (define (for-each proc items) (if (null? items) 'done (begin (proc
>     (car items)) (for-each proc (cdr items)))))
>     :::
>
>     He claims that the evaluator in the text (with the original
>     `eval/sequence`) handles this correctly:
>
>     ::: scheme
>      *;;; L-Eval input:*  (for-each (lambda (x) (newline) (display x))
>     (list 57 321 88))  *57*   *321*   *88*   *;;; L-Eval value:*
>      *done*
>     :::
>
>     Explain why Ben is right about the behavior of `for/each`.
>
> b.  Cy agrees that Ben is right about the `for/each` example, but says
>     that that's not the kind of program he was thinking about when he
>     proposed his change to `eval/sequence`. He defines the following
>     two procedures in the lazy evaluator:
>
>     ::: scheme
>     (define (p1 x) (set! x (cons x '(2))) x) (define (p2 x) (define
>     (p e) e x) (p (set! x (cons x '(2)))))
>     :::
>
>     What are the values of `(p1 1)` and `(p2 1)` with the original
>     `eval/sequence`? What would the values be with Cy's proposed
>     change to `eval/sequence`?
>
> c.  Cy also points out that changing `eval/sequence` as he proposes
>     does not affect the behavior of the example in part a. Explain why
>     this is true.
>
> d.  How do you think sequences ought to be treated in the lazy
>     evaluator? Do you like Cy's approach, the approach in the text, or
>     some other approach?

> **[]{#Exercise 4.31 label="Exercise 4.31"}Exercise 4.31:** The
> approach taken in this section is somewhat unpleasant, because it
> makes an incompatible change to Scheme. It might be nicer to implement
> lazy evaluation as an *upward-compatible extension*, that is, so that
> ordinary Scheme programs will work as before. We can do this by
> extending the syntax of procedure declarations to let the user control
> whether or not arguments are to be delayed. While we're at it, we may
> as well also give the user the choice between delaying with and
> without memoization. For example, the definition
>
> ::: scheme
> (define (f a (b lazy) c (d lazy-memo))  $\dots$ )
> :::
>
> would define `f` to be a procedure of four arguments, where the first
> and third arguments are evaluated when the procedure is called, the
> second argument is delayed, and the fourth argument is both delayed
> and memoized. Thus, ordinary procedure definitions will produce the
> same behavior as ordinary Scheme, while adding the `lazy/memo`
> declaration to each parameter of every compound procedure will produce
> the behavior of the lazy evaluator defined in this section. Design and
> implement the changes required to produce such an extension to Scheme.
> You will have to implement new syntax procedures to handle the new
> syntax for `define`. You must also arrange for `eval` or `apply` to
> determine when arguments are to be delayed, and to force or delay
> arguments accordingly, and you must arrange for forcing to memoize or
> not, as appropriate.

### Streams as Lazy Lists {#Section 4.2.3}

In [Section 3.5.1](#Section 3.5.1), we showed how to implement streams
as delayed lists. We introduced special forms `delay` and `cons/stream`,
which allowed us to construct a "promise" to compute the `cdr` of a
stream, without actually fulfilling that promise until later. We could
use this general technique of introducing special forms whenever we need
more control over the evaluation process, but this is awkward. For one
thing, a special form is not a first-class object like a procedure, so
we cannot use it together with higher-order procedures.[^243]
Additionally, we were forced to create streams as a new kind of data
object similar but not identical to lists, and this required us to
reimplement many ordinary list operations (`map`, `append`, and so on)
for use with streams.

With lazy evaluation, streams and lists can be identical, so there is no
need for special forms or for separate list and stream operations. All
we need to do is to arrange matters so that `cons` is non-strict. One
way to accomplish this is to extend the lazy evaluator to allow for
non-strict primitives, and to implement `cons` as one of these. An
easier way is to recall ([Section 2.1.3](#Section 2.1.3)) that there is
no fundamental need to implement `cons` as a primitive at all. Instead,
we can represent pairs as procedures:[^244]

::: scheme
(define (cons x y) (lambda (m) (m x y))) (define (car z) (z (lambda (p
q) p))) (define (cdr z) (z (lambda (p q) q)))
:::

In terms of these basic operations, the standard definitions of the list
operations will work with infinite lists (streams) as well as finite
ones, and the stream operations can be implemented as list operations.
Here are some examples:

::: scheme
(define (list-ref items n) (if (= n 0) (car items) (list-ref (cdr items)
(- n 1)))) (define (map proc items) (if (null? items) '() (cons (proc
(car items)) (map proc (cdr items))))) (define (scale-list items factor)
(map (lambda (x) (\* x factor)) items)) (define (add-lists list1 list2)
(cond ((null? list1) list2) ((null? list2) list1) (else (cons (+ (car
list1) (car list2)) (add-lists (cdr list1) (cdr list2)))))) (define ones
(cons 1 ones)) (define integers (cons 1 (add-lists ones integers)))
 *;;; L-Eval input:*  (list-ref integers 17)  *;;; L-Eval value:*
 *18*
:::

Note that these lazy lists are even lazier than the streams of [Chapter
3](#Chapter 3): The `car` of the list, as well as the `cdr`, is
delayed.[^245] In fact, even accessing the `car` or `cdr` of a lazy pair
need not force the value of a list element. The value will be forced
only when it is really needed---e.g., for use as the argument of a
primitive, or to be printed as an answer.

Lazy pairs also help with the problem that arose with streams in
[Section 3.5.4](#Section 3.5.4), where we found that formulating stream
models of systems with loops may require us to sprinkle our programs
with explicit `delay` operations, beyond the ones supplied by
`cons/stream`. With lazy evaluation, all arguments to procedures are
delayed uniformly. For instance, we can implement procedures to
integrate lists and solve differential equations as we originally
intended in [Section 3.5.4](#Section 3.5.4):

::: scheme
(define (integral integrand initial-value dt) (define int (cons
initial-value (add-lists (scale-list integrand dt) int))) int) (define
(solve f y0 dt) (define y (integral dy y0 dt)) (define dy (map f y)) y)
 *;;; L-Eval input:*  (list-ref (solve (lambda (x) x) 1 0.001) 1000)
 *;;; L-Eval value:*   *2.716924*
:::

> **[]{#Exercise 4.32 label="Exercise 4.32"}Exercise 4.32:** Give some
> examples that illustrate the difference between the streams of
> [Chapter 3](#Chapter 3) and the "lazier" lazy lists described in this
> section. How can you take advantage of this extra laziness?

> **[]{#Exercise 4.33 label="Exercise 4.33"}Exercise 4.33:** Ben
> Bitdiddle tests the lazy list implementation given above by evaluating
> the expression:
>
> ::: scheme
> (car '(a b c))
> :::
>
> To his surprise, this produces an error. After some thought, he
> realizes that the "lists" obtained by reading in quoted expressions
> are different from the lists manipulated by the new definitions of
> `cons`, `car`, and `cdr`. Modify the evaluator's treatment of quoted
> expressions so that quoted lists typed at the driver loop will produce
> true lazy lists.

> **[]{#Exercise 4.34 label="Exercise 4.34"}Exercise 4.34:** Modify the
> driver loop for the evaluator so that lazy pairs and lists will print
> in some reasonable way. (What are you going to do about infinite
> lists?) You may also need to modify the representation of lazy pairs
> so that the evaluator can identify them in order to print them.

## Variations on a Scheme --- Nondeterministic Computing {#Section 4.3}

In this section, we extend the Scheme evaluator to support a programming
paradigm called *nondeterministic computing* by building into the
evaluator a facility to support automatic search. This is a much more
profound change to the language than the introduction of lazy evaluation
in [Section 4.2](#Section 4.2).

Nondeterministic computing, like stream processing, is useful for
"generate and test" applications. Consider the task of starting with two
lists of positive integers and finding a pair of integers---one from the
first list and one from the second list---whose sum is prime. We saw how
to handle this with finite sequence operations in [Section
2.2.3](#Section 2.2.3) and with infinite streams in [Section
3.5.3](#Section 3.5.3). Our approach was to generate the sequence of all
possible pairs and filter these to select the pairs whose sum is prime.
Whether we actually generate the entire sequence of pairs first as in
[Chapter 2](#Chapter 2), or interleave the generating and filtering as
in [Chapter 3](#Chapter 3), is immaterial to the essential image of how
the computation is organized.

The nondeterministic approach evokes a different image. Imagine simply
that we choose (in some way) a number from the first list and a number
from the second list and require (using some mechanism) that their sum
be prime. This is expressed by following procedure:

::: scheme
(define (prime-sum-pair list1 list2) (let ((a (an-element-of list1)) (b
(an-element-of list2))) (require (prime? (+ a b))) (list a b)))
:::

It might seem as if this procedure merely restates the problem, rather
than specifying a way to solve it. Nevertheless, this is a legitimate
nondeterministic program.[^246]

The key idea here is that expressions in a nondeterministic language can
have more than one possible value. For instance, `an/element/of` might
return any element of the given list. Our nondeterministic program
evaluator will work by automatically choosing a possible value and
keeping track of the choice. If a subsequent requirement is not met, the
evaluator will try a different choice, and it will keep trying new
choices until the evaluation succeeds, or until we run out of choices.
Just as the lazy evaluator freed the programmer from the details of how
values are delayed and forced, the nondeterministic program evaluator
will free the programmer from the details of how choices are made.

It is instructive to contrast the different images of time evoked by
nondeterministic evaluation and stream processing. Stream processing
uses lazy evaluation to decouple the time when the stream of possible
answers is assembled from the time when the actual stream elements are
produced. The evaluator supports the illusion that all the possible
answers are laid out before us in a timeless sequence. With
nondeterministic evaluation, an expression represents the exploration of
a set of possible worlds, each determined by a set of choices. Some of
the possible worlds lead to dead ends, while others have useful values.
The nondeterministic program evaluator supports the illusion that time
branches, and that our programs have different possible execution
histories. When we reach a dead end, we can revisit a previous choice
point and proceed along a different branch.

The nondeterministic program evaluator implemented below is called the
`amb` evaluator because it is based on a new special form called `amb`.
We can type the above definition of `prime/sum/pair` at the `amb`
evaluator driver loop (along with definitions of `prime?`,
`an/element/of`, and `require`) and run the procedure as follows:

::: scheme
 *;;; Amb-Eval input:*  (prime-sum-pair '(1 3 5 8) '(20 35 110))  *;;;
Starting a new problem*   *;;; Amb-Eval value:*   *(3 20)*
:::

The value returned was obtained after the evaluator repeatedly chose
elements from each of the lists, until a successful choice was made.

[Section 4.3.1](#Section 4.3.1) introduces `amb` and explains how it
supports nondeterminism through the evaluator's automatic search
mechanism. [Section 4.3.2](#Section 4.3.2) presents examples of
nondeterministic programs, and [Section 4.3.3](#Section 4.3.3) gives the
details of how to implement the `amb` evaluator by modifying the
ordinary Scheme evaluator.

### Amb and Search {#Section 4.3.1}

To extend Scheme to support nondeterminism, we introduce a new special
form called `amb`.[^247] The expression

::: scheme
(amb
 $\color{SchemeDark}\langle$  *e*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *e*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *e*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
:::

returns the value of one of the $n$ expressions $\langle$$e_i$$\rangle$
"ambiguously." For example, the expression

::: scheme
(list (amb 1 2 3) (amb 'a 'b))
:::

can have six possible values:

::: scheme
 `(1 a)`   `(1 b)`   `(2 a)`   `(2 b)`   `(3 a)`   `(3 b)`
:::

`amb` with a single choice produces an ordinary (single) value.

`amb` with no choices---the expression `(amb)`---is an expression with
no acceptable values. Operationally, we can think of `(amb)` as an
expression that when evaluated causes the computation to "fail": The
computation aborts and no value is produced. Using this idea, we can
express the requirement that a particular predicate expression `p` must
be true as follows:

::: scheme
(define (require p) (if (not p) (amb)))
:::

With `amb` and `require`, we can implement the `an/element/of` procedure
used above:

::: scheme
(define (an-element-of items) (require (not (null? items))) (amb (car
items) (an-element-of (cdr items))))
:::

`an/element/of` fails if the list is empty. Otherwise it ambiguously
returns either the first element of the list or an element chosen from
the rest of the list.

We can also express infinite ranges of choices. The following procedure
potentially returns any integer greater than or equal to some given $n$:

::: scheme
(define (an-integer-starting-from n) (amb n (an-integer-starting-from (+
n 1))))
:::

This is like the stream procedure `integers/starting/from` described in
[Section 3.5.2](#Section 3.5.2), but with an important difference: The
stream procedure returns an object that represents the sequence of all
integers beginning with $n$, whereas the `amb` procedure returns a
single integer.[^248]

Abstractly, we can imagine that evaluating an `amb` expression causes
time to split into branches, where the computation continues on each
branch with one of the possible values of the expression. We say that
`amb` represents a *nondeterministic choice point*. If we had a machine
with a sufficient number of processors that could be dynamically
allocated, we could implement the search in a straightforward way.
Execution would proceed as in a sequential machine, until an `amb`
expression is encountered. At this point, more processors would be
allocated and initialized to continue all of the parallel executions
implied by the choice. Each processor would proceed sequentially as if
it were the only choice, until it either terminates by encountering a
failure, or it further subdivides, or it finishes.[^249]

On the other hand, if we have a machine that can execute only one
process (or a few concurrent processes), we must consider the
alternatives sequentially. One could imagine modifying an evaluator to
pick at random a branch to follow whenever it encounters a choice point.
Random choice, however, can easily lead to failing values. We might try
running the evaluator over and over, making random choices and hoping to
find a non-failing value, but it is better to *systematically search*
all possible execution paths. The `amb` evaluator that we will develop
and work with in this section implements a systematic search as follows:
When the evaluator encounters an application of `amb`, it initially
selects the first alternative. This selection may itself lead to a
further choice. The evaluator will always initially choose the first
alternative at each choice point. If a choice results in a failure, then
the evaluator automagically[^250] *backtracks* to the most recent choice
point and tries the next alternative. If it runs out of alternatives at
any choice point, the evaluator will back up to the previous choice
point and resume from there. This process leads to a search strategy
known as *depth-first search* or *chronological backtracking*.[^251]

#### Driver loop {#driver-loop .unnumbered}

The driver loop for the `amb` evaluator has some unusual properties. It
reads an expression and prints the value of the first non-failing
execution, as in the `prime/sum/pair` example shown above. If we want to
see the value of the next successful execution, we can ask the
interpreter to backtrack and attempt to generate a second non-failing
execution. This is signaled by typing the symbol `try/again`. If any
expression except `try/again` is given, the interpreter will start a new
problem, discarding the unexplored alternatives in the previous problem.
Here is a sample interaction:

::: scheme
 *;;; Amb-Eval input:*  (prime-sum-pair '(1 3 5 8) '(20 35 110))  *;;;
Starting a new problem*   *;;; Amb-Eval value:*   *(3 20)*

 *;;; Amb-Eval input:*  try-again  *;;; Amb-Eval value:*   *(3 110)*

 *;;; Amb-Eval input:*  try-again  *;;; Amb-Eval value:*   *(8 35)*

 *;;; Amb-Eval input:*  try-again  *;;; There are no more values of*
 *(prime-sum-pair (quote (1 3 5 8)) (quote (20 35 110)))*

 *;;; Amb-Eval input:*  (prime-sum-pair '(19 27 30) '(11 36 58))  *;;;
Starting a new problem*   *;;; Amb-Eval value:*   *(30 11)*
:::

> **[]{#Exercise 4.35 label="Exercise 4.35"}Exercise 4.35:** Write a
> procedure `an/integer/between` that returns an integer between two
> given bounds. This can be used to implement a procedure that finds
> Pythagorean triples, i.e., triples of integers $(i, j, k)$ between the
> given bounds such that $i \le j$ and $i^2 + j^2 = k^2$, as follows:
>
> ::: scheme
> (define (a-pythagorean-triple-between low high) (let ((i
> (an-integer-between low high))) (let ((j (an-integer-between i high)))
> (let ((k (an-integer-between j high))) (require (= (+ (\* i i) (\* j
> j)) (\* k k))) (list i j k)))))
> :::

> **[]{#Exercise 4.36 label="Exercise 4.36"}Exercise 4.36:** [Exercise
> 3.69](#Exercise 3.69) discussed how to generate the stream of *all*
> Pythagorean triples, with no upper bound on the size of the integers
> to be searched. Explain why simply replacing `an/integer/between` by
> `an/integer/starting/from` in the procedure in [Exercise
> 4.35](#Exercise 4.35) is not an adequate way to generate arbitrary
> Pythagorean triples. Write a procedure that actually will accomplish
> this. (That is, write a procedure for which repeatedly typing
> `try/again` would in principle eventually generate all Pythagorean
> triples.)

> **[]{#Exercise 4.37 label="Exercise 4.37"}Exercise 4.37:** Ben
> Bitdiddle claims that the following method for generating Pythagorean
> triples is more efficient than the one in [Exercise
> 4.35](#Exercise 4.35). Is he correct? (Hint: Consider the number of
> possibilities that must be explored.)
>
> ::: scheme
> (define (a-pythagorean-triple-between low high) (let ((i
> (an-integer-between low high)) (hsq (\* high high))) (let ((j
> (an-integer-between i high))) (let ((ksq (+ (\* i i) (\* j j))))
> (require (\>= hsq ksq)) (let ((k (sqrt ksq))) (require (integer? k))
> (list i j k))))))
> :::

### Examples of Nondeterministic Programs {#Section 4.3.2}

[Section 4.3.3](#Section 4.3.3) describes the implementation of the
`amb` evaluator. First, however, we give some examples of how it can be
used. The advantage of nondeterministic programming is that we can
suppress the details of how search is carried out, thereby expressing
our programs at a higher level of abstraction.

#### Logic Puzzles {#logic-puzzles .unnumbered}

The following puzzle (taken from [Dinesman 1968](#Dinesman 1968)) is
typical of a large class of simple logic puzzles:

> Baker, Cooper, Fletcher, Miller, and Smith live on different floors of
> an apartment house that contains only five floors. Baker does not live
> on the top floor. Cooper does not live on the bottom floor. Fletcher
> does not live on either the top or the bottom floor. Miller lives on a
> higher floor than does Cooper. Smith does not live on a floor adjacent
> to Fletcher's. Fletcher does not live on a floor adjacent to Cooper's.
> Where does everyone live?

We can determine who lives on each floor in a straightforward way by
enumerating all the possibilities and imposing the given
restrictions:[^252]

::: scheme
(define (multiple-dwelling) (let ((baker (amb 1 2 3 4 5)) (cooper (amb 1
2 3 4 5)) (fletcher (amb 1 2 3 4 5)) (miller (amb 1 2 3 4 5)) (smith
(amb 1 2 3 4 5))) (require (distinct? (list baker cooper fletcher miller
smith))) (require (not (= baker 5))) (require (not (= cooper 1)))
(require (not (= fletcher 5))) (require (not (= fletcher 1))) (require
(\> miller cooper)) (require (not (= (abs (- smith fletcher)) 1)))
(require (not (= (abs (- fletcher cooper)) 1))) (list (list 'baker
baker) (list 'cooper cooper) (list 'fletcher fletcher) (list 'miller
miller) (list 'smith smith))))
:::

Evaluating the expression `(multiple/dwelling)` produces the result

::: scheme
((baker 3) (cooper 2) (fletcher 4) (miller 5) (smith 1))
:::

Although this simple procedure works, it is very slow. [Exercise
4.39](#Exercise 4.39) and [Exercise 4.40](#Exercise 4.40) discuss some
possible improvements.

> **[]{#Exercise 4.38 label="Exercise 4.38"}Exercise 4.38:** Modify the
> multiple-dwelling procedure to omit the requirement that Smith and
> Fletcher do not live on adjacent floors. How many solutions are there
> to this modified puzzle?

> **[]{#Exercise 4.39 label="Exercise 4.39"}Exercise 4.39:** Does the
> order of the restrictions in the multiple-dwelling procedure affect
> the answer? Does it affect the time to find an answer? If you think it
> matters, demonstrate a faster program obtained from the given one by
> reordering the restrictions. If you think it does not matter, argue
> your case.

> **[]{#Exercise 4.40 label="Exercise 4.40"}Exercise 4.40:** In the
> multiple dwelling problem, how many sets of assignments are there of
> people to floors, both before and after the requirement that floor
> assignments be distinct? It is very inefficient to generate all
> possible assignments of people to floors and then leave it to
> backtracking to eliminate them. For example, most of the restrictions
> depend on only one or two of the person-floor variables, and can thus
> be imposed before floors have been selected for all the people. Write
> and demonstrate a much more efficient nondeterministic procedure that
> solves this problem based upon generating only those possibilities
> that are not already ruled out by previous restrictions. (Hint: This
> will require a nest of `let` expressions.)

> **[]{#Exercise 4.41 label="Exercise 4.41"}Exercise 4.41:** Write an
> ordinary Scheme program to solve the multiple dwelling puzzle.

> **[]{#Exercise 4.42 label="Exercise 4.42"}Exercise 4.42:** Solve the
> following "Liars" puzzle (from [Phillips 1934](#Phillips 1934)):
>
> Five schoolgirls sat for an examination. Their parents---so they
> thought---showed an undue degree of interest in the result. They
> therefore agreed that, in writing home about the examination, each
> girl should make one true statement and one untrue one. The following
> are the relevant passages from their letters:
>
> -   Betty: "Kitty was second in the examination. I was only third."
>
> -   Ethel: "You'll be glad to hear that I was on top. Joan was 2nd."
>
> -   Joan: "I was third, and poor old Ethel was bottom."
>
> -   Kitty: "I came out second. Mary was only fourth."
>
> -   Mary: "I was fourth. Top place was taken by Betty."
>
> What in fact was the order in which the five girls were placed?

> **[]{#Exercise 4.43 label="Exercise 4.43"}Exercise 4.43:** Use the
> `amb` evaluator to solve the following puzzle:[^253]
>
> Mary Ann Moore's father has a yacht and so has each of his four
> friends: Colonel Downing, Mr. Hall, Sir Barnacle Hood, and Dr. Parker.
> Each of the five also has one daughter and each has named his yacht
> after a daughter of one of the others. Sir Barnacle's yacht is the
> Gabrielle, Mr. Moore owns the Lorna; Mr. Hall the Rosalind. The
> Melissa, owned by Colonel Downing, is named after Sir Barnacle's
> daughter. Gabrielle's father owns the yacht that is named after Dr.
> Parker's daughter. Who is Lorna's father?
>
> Try to write the program so that it runs efficiently (see [Exercise
> 4.40](#Exercise 4.40)). Also determine how many solutions there are if
> we are not told that Mary Ann's last name is Moore.

> **[]{#Exercise 4.44 label="Exercise 4.44"}Exercise 4.44:** [Exercise
> 2.42](#Exercise 2.42) described the "eight-queens puzzle" of placing
> queens on a chessboard so that no two attack each other. Write a
> nondeterministic program to solve this puzzle.

#### Parsing natural language {#parsing-natural-language .unnumbered}

Programs designed to accept natural language as input usually start by
attempting to *parse* the input, that is, to match the input against
some grammatical structure. For example, we might try to recognize
simple sentences consisting of an article followed by a noun followed by
a verb, such as "The cat eats." To accomplish such an analysis, we must
be able to identify the parts of speech of individual words. We could
start with some lists that classify various words:[^254]

::: scheme
(define nouns '(noun student professor cat class)) (define verbs '(verb
studies lectures eats sleeps)) (define articles '(article the a))
:::

We also need a *grammar*, that is, a set of rules describing how
grammatical elements are composed from simpler elements. A very simple
grammar might stipulate that a sentence always consists of two
pieces---a noun phrase followed by a verb---and that a noun phrase
consists of an article followed by a noun. With this grammar, the
sentence "The cat eats" is parsed as follows:

::: scheme
(sentence (noun-phrase (article the) (noun cat)) (verb eats))
:::

We can generate such a parse with a simple program that has separate
procedures for each of the grammatical rules. To parse a sentence, we
identify its two constituent pieces and return a list of these two
elements, tagged with the symbol `sentence`:

::: scheme
(define (parse-sentence) (list 'sentence (parse-noun-phrase) (parse-word
verbs)))
:::

A noun phrase, similarly, is parsed by finding an article followed by a
noun:

::: scheme
(define (parse-noun-phrase) (list 'noun-phrase (parse-word articles)
(parse-word nouns)))
:::

At the lowest level, parsing boils down to repeatedly checking that the
next unparsed word is a member of the list of words for the required
part of speech. To implement this, we maintain a global variable
`*unparsed*`, which is the input that has not yet been parsed. Each time
we check a word, we require that `*unparsed*` must be non-empty and that
it should begin with a word from the designated list. If so, we remove
that word from `*unparsed*` and return the word together with its part
of speech (which is found at the head of the list):[^255]

::: scheme
(define (parse-word word-list) (require (not (null? \*unparsed\*)))
(require (memq (car \*unparsed\*) (cdr word-list))) (let ((found-word
(car \*unparsed\*))) (set! \*unparsed\* (cdr \*unparsed\*)) (list (car
word-list) found-word)))
:::

To start the parsing, all we need to do is set `*unparsed*` to be the
entire input, try to parse a sentence, and check that nothing is left
over:

::: scheme
(define \*unparsed\* '()) (define (parse input) (set! \*unparsed\*
input) (let ((sent (parse-sentence))) (require (null? \*unparsed\*))
sent))
:::

We can now try the parser and verify that it works for our simple test
sentence:

::: scheme
 *;;; Amb-Eval input:*  (parse '(the cat eats))  *;;; Starting a new
problem*   *;;; Amb-Eval value:*
:::

::: smallscheme
 *(sentence (noun-phrase (article the) (noun cat)) (verb eats))*
:::

The `amb` evaluator is useful here because it is convenient to express
the parsing constraints with the aid of `require`. Automatic search and
backtracking really pay off, however, when we consider more complex
grammars where there are choices for how the units can be decomposed.

Let's add to our grammar a list of prepositions:

::: scheme
(define prepositions '(prep for to in by with))
:::

and define a prepositional phrase (e.g., "for the cat") to be a
preposition followed by a noun phrase:

::: scheme
(define (parse-prepositional-phrase) (list 'prep-phrase (parse-word
prepositions) (parse-noun-phrase)))
:::

Now we can define a sentence to be a noun phrase followed by a verb
phrase, where a verb phrase can be either a verb or a verb phrase
extended by a prepositional phrase:[^256]

::: scheme
(define (parse-sentence) (list 'sentence (parse-noun-phrase)
(parse-verb-phrase))) (define (parse-verb-phrase) (define (maybe-extend
verb-phrase) (amb verb-phrase (maybe-extend (list 'verb-phrase
verb-phrase (parse-prepositional-phrase))))) (maybe-extend (parse-word
verbs)))
:::

While we're at it, we can also elaborate the definition of noun phrases
to permit such things as "a cat in the class." What we used to call a
noun phrase, we'll now call a simple noun phrase, and a noun phrase will
now be either a simple noun phrase or a noun phrase extended by a
prepositional phrase:

::: scheme
(define (parse-simple-noun-phrase) (list 'simple-noun-phrase (parse-word
articles) (parse-word nouns))) (define (parse-noun-phrase) (define
(maybe-extend noun-phrase) (amb noun-phrase (maybe-extend (list
'noun-phrase noun-phrase (parse-prepositional-phrase))))) (maybe-extend
(parse-simple-noun-phrase)))
:::

Our new grammar lets us parse more complex sentences. For example

::: scheme
(parse '(the student with the cat sleeps in the class))
:::

produces

::: scheme
(sentence (noun-phrase (simple-noun-phrase (article the) (noun student))
(prep-phrase (prep with) (simple-noun-phrase (article the) (noun cat))))
(verb-phrase (verb sleeps) (prep-phrase (prep in) (simple-noun-phrase
(article the) (noun class)))))
:::

Observe that a given input may have more than one legal parse. In the
sentence "The professor lectures to the student with the cat," it may be
that the professor is lecturing with the cat, or that the student has
the cat. Our nondeterministic program finds both possibilities:

::: scheme
(parse '(the professor lectures to the student with the cat))
:::

produces

::: scheme
(sentence (simple-noun-phrase (article the) (noun professor))
(verb-phrase (verb-phrase (verb lectures) (prep-phrase (prep to)
(simple-noun-phrase (article the) (noun student)))) (prep-phrase (prep
with) (simple-noun-phrase (article the) (noun cat)))))
:::

Asking the evaluator to try again yields

::: scheme
(sentence (simple-noun-phrase (article the) (noun professor))
(verb-phrase (verb lectures) (prep-phrase (prep to) (noun-phrase
(simple-noun-phrase (article the) (noun student)) (prep-phrase (prep
with) (simple-noun-phrase (article the) (noun cat)))))))
:::

> **[]{#Exercise 4.45 label="Exercise 4.45"}Exercise 4.45:** With the
> grammar given above, the following sentence can be parsed in five
> different ways: "The professor lectures to the student in the class
> with the cat." Give the five parses and explain the differences in
> shades of meaning among them.

> **[]{#Exercise 4.46 label="Exercise 4.46"}Exercise 4.46:** The
> evaluators in [Section 4.1](#Section 4.1) and [Section
> 4.2](#Section 4.2) do not determine what order operands are evaluated
> in. We will see that the `amb` evaluator evaluates them from left to
> right. Explain why our parsing program wouldn't work if the operands
> were evaluated in some other order.

> **[]{#Exercise 4.47 label="Exercise 4.47"}Exercise 4.47:** Louis
> Reasoner suggests that, since a verb phrase is either a verb or a verb
> phrase followed by a prepositional phrase, it would be much more
> straightforward to define the procedure `parse/verb/phrase` as follows
> (and similarly for noun phrases):
>
> ::: scheme
> (define (parse-verb-phrase) (amb (parse-word verbs) (list 'verb-phrase
> (parse-verb-phrase) (parse-prepositional-phrase))))
> :::
>
> Does this work? Does the program's behavior change if we interchange
> the order of expressions in the `amb`?

> **[]{#Exercise 4.48 label="Exercise 4.48"}Exercise 4.48:** Extend the
> grammar given above to handle more complex sentences. For example, you
> could extend noun phrases and verb phrases to include adjectives and
> adverbs, or you could handle compound sentences.[^257]

> **[]{#Exercise 4.49 label="Exercise 4.49"}Exercise 4.49:** Alyssa P.
> Hacker is more interested in generating interesting sentences than in
> parsing them. She reasons that by simply changing the procedure
> `parse/word` so that it ignores the "input sentence" and instead
> always succeeds and generates an appropriate word, we can use the
> programs we had built for parsing to do generation instead. Implement
> Alyssa's idea, and show the first half-dozen or so sentences
> generated.[^258]

### Implementing the `amb` Evaluator {#Section 4.3.3}

The evaluation of an ordinary Scheme expression may return a value, may
never terminate, or may signal an error. In nondeterministic Scheme the
evaluation of an expression may in addition result in the discovery of a
dead end, in which case evaluation must backtrack to a previous choice
point. The interpretation of nondeterministic Scheme is complicated by
this extra case.

We will construct the `amb` evaluator for nondeterministic Scheme by
modifying the analyzing evaluator of [Section
4.1.7](#Section 4.1.7).[^259] As in the analyzing evaluator, evaluation
of an expression is accomplished by calling an execution procedure
produced by analysis of that expression. The difference between the
interpretation of ordinary Scheme and the interpretation of
nondeterministic Scheme will be entirely in the execution procedures.

#### Execution procedures and continuations {#execution-procedures-and-continuations .unnumbered}

Recall that the execution procedures for the ordinary evaluator take one
argument: the environment of execution. In contrast, the execution
procedures in the `amb` evaluator take three arguments: the environment,
and two procedures called *continuation procedures*. The evaluation of
an expression will finish by calling one of these two continuations: If
the evaluation results in a value, the *success continuation* is called
with that value; if the evaluation results in the discovery of a dead
end, the *failure continuation* is called. Constructing and calling
appropriate continuations is the mechanism by which the nondeterministic
evaluator implements backtracking.

It is the job of the success continuation to receive a value and proceed
with the computation. Along with that value, the success continuation is
passed another failure continuation, which is to be called subsequently
if the use of that value leads to a dead end.

It is the job of the failure continuation to try another branch of the
nondeterministic process. The essence of the nondeterministic language
is in the fact that expressions may represent choices among
alternatives. The evaluation of such an expression must proceed with one
of the indicated alternative choices, even though it is not known in
advance which choices will lead to acceptable results. To deal with
this, the evaluator picks one of the alternatives and passes this value
to the success continuation. Together with this value, the evaluator
constructs and passes along a failure continuation that can be called
later to choose a different alternative.

A failure is triggered during evaluation (that is, a failure
continuation is called) when a user program explicitly rejects the
current line of attack (for example, a call to `require` may result in
execution of `(amb)`, an expression that always fails---see [Section
4.3.1](#Section 4.3.1)). The failure continuation in hand at that point
will cause the most recent choice point to choose another alternative.
If there are no more alternatives to be considered at that choice point,
a failure at an earlier choice point is triggered, and so on. Failure
continuations are also invoked by the driver loop in response to a
`try/again` request, to find another value of the expression.

In addition, if a side-effect operation (such as assignment to a
variable) occurs on a branch of the process resulting from a choice, it
may be necessary, when the process finds a dead end, to undo the side
effect before making a new choice. This is accomplished by having the
side-effect operation produce a failure continuation that undoes the
side effect and propagates the failure.

In summary, failure continuations are constructed by

-   `amb` expressions---to provide a mechanism to make alternative
    choices if the current choice made by the `amb` expression leads to
    a dead end;

-   the top-level driver---to provide a mechanism to report failure when
    the choices are exhausted;

-   assignments---to intercept failures and undo assignments during
    backtracking.

Failures are initiated only when a dead end is encountered. This occurs

-   if the user program executes `(amb)`;

-   if the user types `try/again` at the top-level driver.

Failure continuations are also called during processing of a failure:

-   When the failure continuation created by an assignment finishes
    undoing a side effect, it calls the failure continuation it
    intercepted, in order to propagate the failure back to the choice
    point that led to this assignment or to the top level.

-   When the failure continuation for an `amb` runs out of choices, it
    calls the failure continuation that was originally given to the
    `amb`, in order to propagate the failure back to the previous choice
    point or to the top level.

#### Structure of the evaluator {#structure-of-the-evaluator .unnumbered}

The syntax- and data-representation procedures for the `amb` evaluator,
and also the basic `analyze` procedure, are identical to those in the
evaluator of [Section 4.1.7](#Section 4.1.7), except for the fact that
we need additional syntax procedures to recognize the `amb` special
form:[^260]

::: scheme
(define (amb? exp) (tagged-list? exp 'amb)) (define (amb-choices exp)
(cdr exp))
:::

We must also add to the dispatch in `analyze` a clause that will
recognize this special form and generate an appropriate execution
procedure:

::: scheme
((amb? exp) (analyze-amb exp))
:::

The top-level procedure `ambeval` (similar to the version of `eval`
given in [Section 4.1.7](#Section 4.1.7)) analyzes the given expression
and applies the resulting execution procedure to the given environment,
together with two given continuations:

::: scheme
(define (ambeval exp env succeed fail) ((analyze exp) env succeed fail))
:::

A success continuation is a procedure of two arguments: the value just
obtained and another failure continuation to be used if that value leads
to a subsequent failure. A failure continuation is a procedure of no
arguments. So the general form of an execution procedure is

::: scheme
(lambda (env succeed fail)  [;; `succeed` is
`(lambda (value fail) `$\dots$`)`]{.roman}   [;; `fail` is
`(lambda () `$\dots$`)`]{.roman}   $\dots$ )
:::

For example, executing

::: scheme
(ambeval
 $\color{SchemeDark}\langle$  *exp*  $\color{SchemeDark}\rangle$
the-global-environment (lambda (value fail) value) (lambda () 'failed))
:::

will attempt to evaluate the given expression and will return either the
expression's value (if the evaluation succeeds) or the symbol `failed`
(if the evaluation fails). The call to `ambeval` in the driver loop
shown below uses much more complicated continuation procedures, which
continue the loop and support the `try/again` request.

Most of the complexity of the `amb` evaluator results from the mechanics
of passing the continuations around as the execution procedures call
each other. In going through the following code, you should compare each
of the execution procedures with the corresponding procedure for the
ordinary evaluator given in [Section 4.1.7](#Section 4.1.7).

#### Simple expressions {#simple-expressions .unnumbered}

The execution procedures for the simplest kinds of expressions are
essentially the same as those for the ordinary evaluator, except for the
need to manage the continuations. The execution procedures simply
succeed with the value of the expression, passing along the failure
continuation that was passed to them.

::: scheme
(define (analyze-self-evaluating exp) (lambda (env succeed fail)
(succeed exp fail))) (define (analyze-quoted exp) (let ((qval
(text-of-quotation exp))) (lambda (env succeed fail) (succeed qval
fail)))) (define (analyze-variable exp) (lambda (env succeed fail)
(succeed (lookup-variable-value exp env) fail))) (define (analyze-lambda
exp) (let ((vars (lambda-parameters exp)) (bproc (analyze-sequence
(lambda-body exp)))) (lambda (env succeed fail) (succeed (make-procedure
vars bproc env) fail))))
:::

Notice that looking up a variable always 'succeeds.' If
`lookup/variable/value` fails to find the variable, it signals an error,
as usual. Such a "failure" indicates a program bug---a reference to an
unbound variable; it is not an indication that we should try another
nondeterministic choice instead of the one that is currently being
tried.

#### Conditionals and sequences {#conditionals-and-sequences .unnumbered}

Conditionals are also handled in a similar way as in the ordinary
evaluator. The execution procedure generated by `analyze/if` invokes the
predicate execution procedure `pproc` with a success continuation that
checks whether the predicate value is true and goes on to execute either
the consequent or the alternative. If the execution of `pproc` fails,
the original failure continuation for the `if` expression is called.

::: scheme
(define (analyze-if exp) (let ((pproc (analyze (if-predicate exp)))
(cproc (analyze (if-consequent exp))) (aproc (analyze (if-alternative
exp)))) (lambda (env succeed fail) (pproc env  [;; success continuation
for evaluating the predicate]{.roman}   [;; to obtain
`pred/value`]{.roman}  (lambda (pred-value fail2) (if (true? pred-value)
(cproc env succeed fail2) (aproc env succeed fail2)))  [;; failure
continuation for evaluating the predicate]{.roman}  fail))))
:::

Sequences are also handled in the same way as in the previous evaluator,
except for the machinations in the subprocedure `sequentially` that are
required for passing the continuations. Namely, to sequentially execute
`a` and then `b`, we call `a` with a success continuation that calls
`b`.

::: scheme
(define (analyze-sequence exps) (define (sequentially a b) (lambda (env
succeed fail) (a env  [;; success continuation for calling `a`]{.roman}
(lambda (a-value fail2) (b env succeed fail2))  [;; failure continuation
for calling `a`]{.roman}  fail))) (define (loop first-proc rest-procs)
(if (null? rest-procs) first-proc (loop (sequentially first-proc (car
rest-procs)) (cdr rest-procs)))) (let ((procs (map analyze exps))) (if
(null? procs) (error \"Empty sequence: ANALYZE\")) (loop (car procs)
(cdr procs))))
:::

#### Definitions and assignments {#definitions-and-assignments .unnumbered}

Definitions are another case where we must go to some trouble to manage
the continuations, because it is necessary to evaluate the
definition-value expression before actually defining the new variable.
To accomplish this, the definition-value execution procedure `vproc` is
called with the environment, a success continuation, and the failure
continuation. If the execution of `vproc` succeeds, obtaining a value
`val` for the defined variable, the variable is defined and the success
is propagated:

::: scheme
(define (analyze-definition exp) (let ((var (definition-variable exp))
(vproc (analyze (definition-value exp)))) (lambda (env succeed fail)
(vproc env (lambda (val fail2) (define-variable! var val env) (succeed
'ok fail2)) fail))))
:::

Assignments are more interesting. This is the first place where we
really use the continuations, rather than just passing them around. The
execution procedure for assignments starts out like the one for
definitions. It first attempts to obtain the new value to be assigned to
the variable. If this evaluation of `vproc` fails, the assignment fails.

If `vproc` succeeds, however, and we go on to make the assignment, we
must consider the possibility that this branch of the computation might
later fail, which will require us to backtrack out of the assignment.
Thus, we must arrange to undo the assignment as part of the backtracking
process.[^261]

This is accomplished by giving `vproc` a success continuation (marked
with the comment "\*1\*" below) that saves the old value of the variable
before assigning the new value to the variable and proceeding from the
assignment. The failure continuation that is passed along with the value
of the assignment (marked with the comment "\*2\*" below) restores the
old value of the variable before continuing the failure. That is, a
successful assignment provides a failure continuation that will
intercept a subsequent failure; whatever failure would otherwise have
called `fail2` calls this procedure instead, to undo the assignment
before actually calling `fail2`.

::: scheme
(define (analyze-assignment exp) (let ((var (assignment-variable exp))
(vproc (analyze (assignment-value exp)))) (lambda (env succeed fail)
(vproc env (lambda (val fail2)  [; \*1\*]{.roman}  (let ((old-value
(lookup-variable-value var env))) (set-variable-value! var val env)
(succeed 'ok (lambda ()  [; \*2\*]{.roman}  (set-variable-value! var
old-value env) (fail2))))) fail))))
:::

#### Procedure applications {#procedure-applications .unnumbered}

The execution procedure for applications contains no new ideas except
for the technical complexity of managing the continuations. This
complexity arises in `analyze/application`, due to the need to keep
track of the success and failure continuations as we evaluate the
operands. We use a procedure `get/args` to evaluate the list of
operands, rather than a simple `map` as in the ordinary evaluator.

::: scheme
(define (analyze-application exp) (let ((fproc (analyze (operator exp)))
(aprocs (map analyze (operands exp)))) (lambda (env succeed fail) (fproc
env (lambda (proc fail2) (get-args aprocs env (lambda (args fail3)
(execute-application proc args succeed fail3)) fail2)) fail))))
:::

In `get/args`, notice how `cdr`-ing down the list of `aproc` execution
procedures and `cons`ing up the resulting list of `args` is accomplished
by calling each `aproc` in the list with a success continuation that
recursively calls `get/args`. Each of these recursive calls to
`get/args` has a success continuation whose value is the `cons` of the
newly obtained argument onto the list of accumulated arguments:

::: scheme
(define (get-args aprocs env succeed fail) (if (null? aprocs) (succeed
'() fail) ((car aprocs) env  [;; success continuation for this
`aproc`]{.roman}  (lambda (arg fail2) (get-args (cdr aprocs) env  [;;
success continuation for]{.roman}   [;; recursive call to
`get/args`]{.roman}  (lambda (args fail3) (succeed (cons arg args)
fail3)) fail2)) fail)))
:::

The actual procedure application, which is performed by
`execute/appli/cation`, is accomplished in the same way as for the
ordinary evaluator, except for the need to manage the continuations.

::: scheme
(define (execute-application proc args succeed fail) (cond
((primitive-procedure? proc) (succeed (apply-primitive-procedure proc
args) fail)) ((compound-procedure? proc) ((procedure-body proc)
(extend-environment (procedure-parameters proc) args
(procedure-environment proc)) succeed fail)) (else (error \"Unknown
procedure type: EXECUTE-APPLICATION\" proc))))
:::

#### Evaluating `amb` expressions {#evaluating-amb-expressions .unnumbered}

The `amb` special form is the key element in the nondeterministic
language. Here we see the essence of the interpretation process and the
reason for keeping track of the continuations. The execution procedure
for `amb` defines a loop `try/next` that cycles through the execution
procedures for all the possible values of the `amb` expression. Each
execution procedure is called with a failure continuation that will try
the next one. When there are no more alternatives to try, the entire
`amb` expression fails.

::: scheme
(define (analyze-amb exp) (let ((cprocs (map analyze (amb-choices
exp)))) (lambda (env succeed fail) (define (try-next choices) (if (null?
choices) (fail) ((car choices) env succeed (lambda () (try-next (cdr
choices)))))) (try-next cprocs))))
:::

#### Driver loop {#driver-loop-1 .unnumbered}

The driver loop for the `amb` evaluator is complex, due to the mechanism
that permits the user to try again in evaluating an expression. The
driver uses a procedure called `internal/loop`, which takes as argument
a procedure `try/again`. The intent is that calling `try/again` should
go on to the next untried alternative in the nondeterministic
evaluation. `internal/loop` either calls `try/again` in response to the
user typing `try/again` at the driver loop, or else starts a new
evaluation by calling `ambeval`.

The failure continuation for this call to `ambeval` informs the user
that there are no more values and re-invokes the driver loop.

The success continuation for the call to `ambeval` is more subtle. We
print the obtained value and then invoke the internal loop again with a
`try/again` procedure that will be able to try the next alternative.
This `next/alternative` procedure is the second argument that was passed
to the success continuation. Ordinarily, we think of this second
argument as a failure continuation to be used if the current evaluation
branch later fails. In this case, however, we have completed a
successful evaluation, so we can invoke the "failure" alternative branch
in order to search for additional successful evaluations.

::: scheme
(define input-prompt \";;; Amb-Eval input:\") (define output-prompt
\";;; Amb-Eval value:\")

(define (driver-loop) (define (internal-loop try-again)
(prompt-for-input input-prompt) (let ((input (read))) (if (eq? input
'try-again) (try-again) (begin (newline) (display \";;; Starting a new
problem \") (ambeval input the-global-environment  [;; `ambeval`
success]{.roman}  (lambda (val next-alternative) (announce-output
output-prompt) (user-print val) (internal-loop next-alternative))  [;;
`ambeval` failure]{.roman}  (lambda () (announce-output \";;; There are
no more values of\") (user-print input) (driver-loop)))))))
(internal-loop (lambda () (newline) (display \";;; There is no current
problem\") (driver-loop))))
:::

The initial call to `internal/loop` uses a `try/again` procedure that
complains that there is no current problem and restarts the driver loop.
This is the behavior that will happen if the user types `try/again` when
there is no evaluation in progress.

> **[]{#Exercise 4.50 label="Exercise 4.50"}Exercise 4.50:** Implement a
> new special form `ramb` that is like `amb` except that it searches
> alternatives in a random order, rather than from left to right. Show
> how this can help with Alyssa's problem in [Exercise
> 4.49](#Exercise 4.49).

> **[]{#Exercise 4.51 label="Exercise 4.51"}Exercise 4.51:** Implement a
> new kind of assignment called `permanent/set!` that is not undone upon
> failure. For example, we can choose two distinct elements from a list
> and count the number of trials required to make a successful choice as
> follows:
>
> ::: scheme
> (define count 0) (let ((x (an-element-of '(a b c))) (y (an-element-of
> '(a b c)))) (permanent-set! count (+ count 1)) (require (not (eq? x
> y))) (list x y count))  *;;; Starting a new problem*   *;;; Amb-Eval
> value:*   *(a b 2)*   *;;; Amb-Eval input:*  try-again  *;;; Amb-Eval
> value:*   *(a c 3)*
> :::
>
> What values would have been displayed if we had used `set!` here
> rather than `permanent/set!` ?

> **[]{#Exercise 4.52 label="Exercise 4.52"}Exercise 4.52:** Implement a
> new construct called `if/fail` that permits the user to catch the
> failure of an expression. `if/fail` takes two expressions. It
> evaluates the first expression as usual and returns as usual if the
> evaluation succeeds. If the evaluation fails, however, the value of
> the second expression is returned, as in the following example:
>
> ::: scheme
>  *;;; Amb-Eval input:*  (if-fail (let ((x (an-element-of '(1 3 5))))
> (require (even? x)) x) 'all-odd)  *;;; Starting a new problem*   *;;;
> Amb-Eval value:*   *all-odd*
>
>  *;;; Amb-Eval input:*  (if-fail (let ((x (an-element-of '(1 3 5 8))))
> (require (even? x)) x) 'all-odd)  *;;; Starting a new problem*   *;;;
> Amb-Eval value:*   *8*
> :::

> **[]{#Exercise 4.53 label="Exercise 4.53"}Exercise 4.53:** With
> `permanent/set!` as described in [Exercise 4.51](#Exercise 4.51) and
> `if/fail` as in [Exercise 4.52](#Exercise 4.52), what will be the
> result of evaluating
>
> ::: scheme
> (let ((pairs '())) (if-fail (let ((p (prime-sum-pair '(1 3 5 8) '(20
> 35 110)))) (permanent-set! pairs (cons p pairs)) (amb)) pairs))
> :::

> **[]{#Exercise 4.54 label="Exercise 4.54"}Exercise 4.54:** If we had
> not realized that `require` could be implemented as an ordinary
> procedure that uses `amb`, to be defined by the user as part of a
> nondeterministic program, we would have had to implement it as a
> special form. This would require syntax procedures
>
> ::: scheme
> (define (require? exp) (tagged-list? exp 'require)) (define
> (require-predicate exp) (cadr exp))
> :::
>
> and a new clause in the dispatch in `analyze`
>
> ::: scheme
> ((require? exp) (analyze-require exp))
> :::
>
> as well the procedure `analyze/require` that handles `require`
> expressions. Complete the following definition of `analyze/require`.
>
> ::: scheme
> (define (analyze-require exp) (let ((pproc (analyze (require-predicate
> exp)))) (lambda (env succeed fail) (pproc env (lambda (pred-value
> fail2) (if
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$  (succeed
> 'ok fail2))) fail))))
> :::

## Logic Programming {#Section 4.4}

In [Chapter 1](#Chapter 1) we stressed that computer science deals with
imperative (how to) knowledge, whereas mathematics deals with
declarative (what is) knowledge. Indeed, programming languages require
that the programmer express knowledge in a form that indicates the
step-by-step methods for solving particular problems. On the other hand,
high-level languages provide, as part of the language implementation, a
substantial amount of methodological knowledge that frees the user from
concern with numerous details of how a specified computation will
progress.

Most programming languages, including Lisp, are organized around
computing the values of mathematical functions. Expression-oriented
languages (such as Lisp, Fortran, and Algol) capitalize on the "pun"
that an expression that describes the value of a function may also be
interpreted as a means of computing that value. Because of this, most
programming languages are strongly biased toward unidirectional
computations (computations with well-defined inputs and outputs). There
are, however, radically different programming languages that relax this
bias. We saw one such example in [Section 3.3.5](#Section 3.3.5), where
the objects of computation were arithmetic constraints. In a constraint
system the direction and the order of computation are not so well
specified; in carrying out a computation the system must therefore
provide more detailed "how to" knowledge than would be the case with an
ordinary arithmetic computation. This does not mean, however, that the
user is released altogether from the responsibility of providing
imperative knowledge. There are many constraint networks that implement
the same set of constraints, and the user must choose from the set of
mathematically equivalent networks a suitable network to specify a
particular computation.

The nondeterministic program evaluator of [Section 4.3](#Section 4.3)
also moves away from the view that programming is about constructing
algorithms for computing unidirectional functions. In a nondeterministic
language, expressions can have more than one value, and, as a result,
the computation is dealing with relations rather than with single-valued
functions. Logic programming extends this idea by combining a relational
vision of programming with a powerful kind of symbolic pattern matching
called *unification*.[^262]

This approach, when it works, can be a very powerful way to write
programs. Part of the power comes from the fact that a single "what is"
fact can be used to solve a number of different problems that would have
different "how to" components. As an example, consider the `append`
operation, which takes two lists as arguments and combines their
elements to form a single list. In a procedural language such as Lisp,
we could define `append` in terms of the basic list constructor `cons`,
as we did in [Section 2.2.1](#Section 2.2.1):

::: scheme
(define (append x y) (if (null? x) y (cons (car x) (append (cdr x) y))))
:::

This procedure can be regarded as a translation into Lisp of the
following two rules, the first of which covers the case where the first
list is empty and the second of which handles the case of a nonempty
list, which is a `cons` of two parts:

-   For any list `y`, the empty list and `y` `append` to form `y`.

-   For any `u`, `v`, `y`, and `z`, `(cons u v)` and `y` `append` to
    form `(cons u z)` if `v` and `y` `append` to form `z`.[^263]

Using the `append` procedure, we can answer questions such as

> Find the `append` of `(a b)` and `(c d)`.

But the same two rules are also sufficient for answering the following
sorts of questions, which the procedure can't answer:

> Find a list `y` that `append`s with `(a b)` to produce `(a b c d)`.
>
> Find all `x` and `y` that `append` to form `(a b c d)`.

In a logic programming language, the programmer writes an `append`
"procedure" by stating the two rules about `append` given above. "How
to" knowledge is provided automatically by the interpreter to allow this
single pair of rules to be used to answer all three types of questions
about `append`.[^264]

Contemporary logic programming languages (including the one we implement
here) have substantial deficiencies, in that their general "how to"
methods can lead them into spurious infinite loops or other undesirable
behavior. Logic programming is an active field of research in computer
science.[^265]

Earlier in this chapter we explored the technology of implementing
interpreters and described the elements that are essential to an
interpreter for a Lisp-like language (indeed, to an interpreter for any
conventional language). Now we will apply these ideas to discuss an
interpreter for a logic programming language. We call this language the
*query language*, because it is very useful for retrieving information
from data bases by formulating *queries*, or questions, expressed in the
language. Even though the query language is very different from Lisp, we
will find it convenient to describe the language in terms of the same
general framework we have been using all along: as a collection of
primitive elements, together with means of combination that enable us to
combine simple elements to create more complex elements and means of
abstraction that enable us to regard complex elements as single
conceptual units. An interpreter for a logic programming language is
considerably more complex than an interpreter for a language like Lisp.
Nevertheless, we will see that our query-language interpreter contains
many of the same elements found in the interpreter of [Section
4.1](#Section 4.1). In particular, there will be an "eval" part that
classifies expressions according to type and an "apply" part that
implements the language's abstraction mechanism (procedures in the case
of Lisp, and *rules* in the case of logic programming). Also, a central
role is played in the implementation by a frame data structure, which
determines the correspondence between symbols and their associated
values. One additional interesting aspect of our query-language
implementation is that we make substantial use of streams, which were
introduced in [Chapter 3](#Chapter 3).

### Deductive Information Retrieval {#Section 4.4.1}

Logic programming excels in providing interfaces to data bases for
information retrieval. The query language we shall implement in this
chapter is designed to be used in this way.

In order to illustrate what the query system does, we will show how it
can be used to manage the data base of personnel records for Microshaft,
a thriving high-technology company in the Boston area. The language
provides pattern-directed access to personnel information and can also
take advantage of general rules in order to make logical deductions.

#### A sample data base {#a-sample-data-base .unnumbered}

The personnel data base for Microshaft contains *assertions* about
company personnel. Here is the information about Ben Bitdiddle, the
resident computer wizard:

::: scheme
(address (Bitdiddle Ben) (Slumerville (Ridge Road) 10)) (job (Bitdiddle
Ben) (computer wizard)) (salary (Bitdiddle Ben) 60000)
:::

Each assertion is a list (in this case a triple) whose elements can
themselves be lists.

As resident wizard, Ben is in charge of the company's computer division,
and he supervises two programmers and one technician. Here is the
information about them:

::: scheme
(address (Hacker Alyssa P) (Cambridge (Mass Ave) 78)) (job (Hacker
Alyssa P) (computer programmer)) (salary (Hacker Alyssa P) 40000)
(supervisor (Hacker Alyssa P) (Bitdiddle Ben))

(address (Fect Cy D) (Cambridge (Ames Street) 3)) (job (Fect Cy D)
(computer programmer)) (salary (Fect Cy D) 35000) (supervisor (Fect Cy
D) (Bitdiddle Ben))

(address (Tweakit Lem E) (Boston (Bay State Road) 22)) (job (Tweakit Lem
E) (computer technician)) (salary (Tweakit Lem E) 25000) (supervisor
(Tweakit Lem E) (Bitdiddle Ben))
:::

There is also a programmer trainee, who is supervised by Alyssa:

::: scheme
(address (Reasoner Louis) (Slumerville (Pine Tree Road) 80)) (job
(Reasoner Louis) (computer programmer trainee)) (salary (Reasoner Louis)
30000) (supervisor (Reasoner Louis) (Hacker Alyssa P))
:::

All of these people are in the computer division, as indicated by the
word `computer` as the first item in their job descriptions.

Ben is a high-level employee. His supervisor is the company's big wheel
himself:

::: scheme
(supervisor (Bitdiddle Ben) (Warbucks Oliver)) (address (Warbucks
Oliver) (Swellesley (Top Heap Road))) (job (Warbucks Oliver)
(administration big wheel)) (salary (Warbucks Oliver) 150000)
:::

Besides the computer division supervised by Ben, the company has an
accounting division, consisting of a chief accountant and his assistant:

::: scheme
(address (Scrooge Eben) (Weston (Shady Lane) 10)) (job (Scrooge Eben)
(accounting chief accountant)) (salary (Scrooge Eben) 75000) (supervisor
(Scrooge Eben) (Warbucks Oliver))

(address (Cratchet Robert) (Allston (N Harvard Street) 16)) (job
(Cratchet Robert) (accounting scrivener)) (salary (Cratchet Robert)
18000) (supervisor (Cratchet Robert) (Scrooge Eben))
:::

There is also a secretary for the big wheel:

::: scheme
(address (Aull DeWitt) (Slumerville (Onion Square) 5)) (job (Aull
DeWitt) (administration secretary)) (salary (Aull DeWitt) 25000)
(supervisor (Aull DeWitt) (Warbucks Oliver))
:::

The data base also contains assertions about which kinds of jobs can be
done by people holding other kinds of jobs. For instance, a computer
wizard can do the jobs of both a computer programmer and a computer
technician:

::: scheme
(can-do-job (computer wizard) (computer programmer)) (can-do-job
(computer wizard) (computer technician))
:::

A computer programmer could fill in for a trainee:

::: scheme
(can-do-job (computer programmer) (computer programmer trainee))
:::

Also, as is well known,

::: scheme
(can-do-job (administration secretary) (administration big wheel))
:::

#### Simple queries {#simple-queries .unnumbered}

The query language allows users to retrieve information from the data
base by posing queries in response to the system's prompt. For example,
to find all computer programmers one can say

::: scheme
 *;;; Query input:*  (job ?x (computer programmer))
:::

The system will respond with the following items:

::: scheme
 *;;; Query results:*  (job (Hacker Alyssa P) (computer programmer))
(job (Fect Cy D) (computer programmer))
:::

The input query specifies that we are looking for entries in the data
base that match a certain *pattern*. In this example, the pattern
specifies entries consisting of three items, of which the first is the
literal symbol `job`, the second can be anything, and the third is the
literal list `(computer programmer)`. The "anything" that can be the
second item in the matching list is specified by a *pattern variable*,
`?x`. The general form of a pattern variable is a symbol, taken to be
the name of the variable, preceded by a question mark. We will see below
why it is useful to specify names for pattern variables rather than just
putting `?` into patterns to represent "anything." The system responds
to a simple query by showing all entries in the data base that match the
specified pattern.

A pattern can have more than one variable. For example, the query

::: scheme
(address ?x ?y)
:::

will list all the employees' addresses.

A pattern can have no variables, in which case the query simply
determines whether that pattern is an entry in the data base. If so,
there will be one match; if not, there will be no matches.

The same pattern variable can appear more than once in a query,
specifying that the same "anything" must appear in each position. This
is why variables have names. For example,

::: scheme
(supervisor ?x ?x)
:::

finds all people who supervise themselves (though there are no such
assertions in our sample data base).

The query

::: scheme
(job ?x (computer ?type))
:::

matches all job entries whose third item is a two-element list whose
first item is `computer`:

::: scheme
(job (Bitdiddle Ben) (computer wizard)) (job (Hacker Alyssa P) (computer
programmer)) (job (Fect Cy D) (computer programmer)) (job (Tweakit Lem
E) (computer technician))
:::

This same pattern does *not* match

::: scheme
(job (Reasoner Louis) (computer programmer trainee))
:::

because the third item in the entry is a list of three elements, and the
pattern's third item specifies that there should be two elements. If we
wanted to change the pattern so that the third item could be any list
beginning with `computer`, we could specify[^266]

::: scheme
(job ?x (computer . ?type))
:::

For example,

::: scheme
(computer . ?type)
:::

matches the data

::: scheme
(computer programmer trainee)
:::

with `?type` as the list `(programmer trainee)`. It also matches the
data

::: scheme
(computer programmer)
:::

with `?type` as the list `(programmer)`, and matches the data

::: scheme
(computer)
:::

with `?type` as the empty list `()`.

We can describe the query language's processing of simple queries as
follows:

-   The system finds all assignments to variables in the query pattern
    that *satisfy* the pattern---that is, all sets of values for the
    variables such that if the pattern variables are *instantiated with*
    (replaced by) the values, the result is in the data base.

-   The system responds to the query by listing all instantiations of
    the query pattern with the variable assignments that satisfy it.

Note that if the pattern has no variables, the query reduces to a
determination of whether that pattern is in the data base. If so, the
empty assignment, which assigns no values to variables, satisfies that
pattern for that data base.

> **[]{#Exercise 4.55 label="Exercise 4.55"}Exercise 4.55:** Give simple
> queries that retrieve the following information from the data base:
>
> 1.  all people supervised by Ben Bitdiddle;
>
> 2.  the names and jobs of all people in the accounting division;
>
> 3.  the names and addresses of all people who live in Slumerville.

#### Compound queries {#compound-queries .unnumbered}

Simple queries form the primitive operations of the query language. In
order to form compound operations, the query language provides means of
combination. One thing that makes the query language a logic programming
language is that the means of combination mirror the means of
combination used in forming logical expressions: `and`, `or`, and `not`.
(Here `and`, `or`, and `not` are not the Lisp primitives, but rather
operations built into the query language.)

We can use `and` as follows to find the addresses of all the computer
programmers:

::: scheme
(and (job ?person (computer programmer)) (address ?person ?where))
:::

The resulting output is

::: scheme
(and (job (Hacker Alyssa P) (computer programmer)) (address (Hacker
Alyssa P) (Cambridge (Mass Ave) 78))) (and (job (Fect Cy D) (computer
programmer)) (address (Fect Cy D) (Cambridge (Ames Street) 3)))
:::

In general,

::: scheme
(and
 $\color{SchemeDark}\langle$  *query*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *query*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *query*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
:::

is satisfied by all sets of values for the pattern variables that
simultaneously satisfy $\langle$*query*$_1\rangle$ $\dots$
$\langle$*query*$_n\rangle$.

As for simple queries, the system processes a compound query by finding
all assignments to the pattern variables that satisfy the query, then
displaying instantiations of the query with those values.

Another means of constructing compound queries is through `or`. For
example,

::: scheme
(or (supervisor ?x (Bitdiddle Ben)) (supervisor ?x (Hacker Alyssa P)))
:::

will find all employees supervised by Ben Bitdiddle or Alyssa P. Hacker:

::: scheme
(or (supervisor (Hacker Alyssa P) (Bitdiddle Ben)) (supervisor (Hacker
Alyssa P) (Hacker Alyssa P))) (or (supervisor (Fect Cy D) (Bitdiddle
Ben)) (supervisor (Fect Cy D) (Hacker Alyssa P))) (or (supervisor
(Tweakit Lem E) (Bitdiddle Ben)) (supervisor (Tweakit Lem E) (Hacker
Alyssa P))) (or (supervisor (Reasoner Louis) (Bitdiddle Ben))
(supervisor (Reasoner Louis) (Hacker Alyssa P)))
:::

In general,

::: scheme
(or
 $\color{SchemeDark}\langle$  *query*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *query*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *query*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
:::

is satisfied by all sets of values for the pattern variables that
satisfy at least one of $\langle$*query*$_1\rangle$ $\dots$
$\langle$*query*$_n\rangle$.

Compound queries can also be formed with `not`. For example,

::: scheme
(and (supervisor ?x (Bitdiddle Ben)) (not (job ?x (computer
programmer))))
:::

finds all people supervised by Ben Bitdiddle who are not computer
programmers. In general,

::: scheme
(not
 $\color{SchemeDark}\langle$  *query*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$ )
:::

is satisfied by all assignments to the pattern variables that do not
satisfy $\langle$*query*$_1\rangle$.[^267]

The final combining form is called `lisp/value`. When `lisp/value` is
the first element of a pattern, it specifies that the next element is a
Lisp predicate to be applied to the rest of the (instantiated) elements
as arguments. In general,

::: scheme
(lisp-value
 $\color{SchemeDark}\langle$  *predicate*  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *arg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *arg*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
:::

will be satisfied by assignments to the pattern variables for which the
$\langle$*predicate*$\rangle$ applied to the instantiated
$\langle$*arg*$_1\rangle$ $\dots$ $\langle$*arg*$_n\rangle$ is true. For
example, to find all people whose salary is greater than \$30,000 we
could write[^268]

::: scheme
(and (salary ?person ?amount) (lisp-value \> ?amount 30000))
:::

> **[]{#Exercise 4.56 label="Exercise 4.56"}Exercise 4.56:** Formulate
> compound queries that retrieve the following information:
>
> a.  the names of all people who are supervised by Ben Bitdiddle,
>     together with their addresses;
>
> b.  all people whose salary is less than Ben Bitdiddle's, together
>     with their salary and Ben Bitdiddle's salary;
>
> c.  all people who are supervised by someone who is not in the
>     computer division, together with the supervisor's name and job.

#### Rules {#rules .unnumbered}

In addition to primitive queries and compound queries, the query
language provides means for abstracting queries. These are given by
*rules*. The rule

::: scheme
(rule (lives-near ?person-1 ?person-2) (and (address ?person-1 (?town .
?rest-1)) (address ?person-2 (?town . ?rest-2)) (not (same ?person-1
?person-2))))
:::

specifies that two people live near each other if they live in the same
town. The final `not` clause prevents the rule from saying that all
people live near themselves. The `same` relation is defined by a very
simple rule:[^269]

::: scheme
(rule (same ?x ?x))
:::

The following rule declares that a person is a "wheel" in an
organization if he supervises someone who is in turn a supervisor:

::: scheme
(rule (wheel ?person) (and (supervisor ?middle-manager ?person)
(supervisor ?x ?middle-manager)))
:::

The general form of a rule is

::: scheme
(rule
 $\color{SchemeDark}\langle$  *conclusion*  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ )
:::

where $\langle$*conclusion*$\rangle$ is a pattern and
$\langle$*body*$\rangle$ is any query.[^270] We can think of a rule as
representing a large (even infinite) set of assertions, namely all
instantiations of the rule conclusion with variable assignments that
satisfy the rule body. When we described simple queries (patterns), we
said that an assignment to variables satisfies a pattern if the
instantiated pattern is in the data base. But the pattern needn't be
explicitly in the data base as an assertion. It can be an implicit
assertion implied by a rule. For example, the query

::: scheme
(lives-near ?x (Bitdiddle Ben))
:::

results in

::: scheme
(lives-near (Reasoner Louis) (Bitdiddle Ben)) (lives-near (Aull DeWitt)
(Bitdiddle Ben))
:::

To find all computer programmers who live near Ben Bitdiddle, we can ask

::: scheme
(and (job ?x (computer programmer)) (lives-near ?x (Bitdiddle Ben)))
:::

As in the case of compound procedures, rules can be used as parts of
other rules (as we saw with the `lives/near` rule above) or even be
defined recursively. For instance, the rule

::: scheme
(rule (outranked-by ?staff-person ?boss) (or (supervisor ?staff-person
?boss) (and (supervisor ?staff-person ?middle-manager) (outranked-by
?middle-manager ?boss))))
:::

says that a staff person is outranked by a boss in the organization if
the boss is the person's supervisor or (recursively) if the person's
supervisor is outranked by the boss.

> **[]{#Exercise 4.57 label="Exercise 4.57"}Exercise 4.57:** Define a
> rule that says that person 1 can replace person 2 if either person 1
> does the same job as person 2 or someone who does person 1's job can
> also do person 2's job, and if person 1 and person 2 are not the same
> person. Using your rule, give queries that find the following:
>
> a.  all people who can replace Cy D. Fect;
>
> b.  all people who can replace someone who is being paid more than
>     they are, together with the two salaries.

> **[]{#Exercise 4.58 label="Exercise 4.58"}Exercise 4.58:** Define a
> rule that says that a person is a "big shot" in a division if the
> person works in the division but does not have a supervisor who works
> in the division.

> **[]{#Exercise 4.59 label="Exercise 4.59"}Exercise 4.59:** Ben
> Bitdiddle has missed one meeting too many. Fearing that his habit of
> forgetting meetings could cost him his job, Ben decides to do
> something about it. He adds all the weekly meetings of the firm to the
> Microshaft data base by asserting the following:
>
> ::: scheme
> (meeting accounting (Monday 9am)) (meeting administration (Monday
> 10am)) (meeting computer (Wednesday 3pm)) (meeting administration
> (Friday 1pm))
> :::
>
> Each of the above assertions is for a meeting of an entire division.
> Ben also adds an entry for the company-wide meeting that spans all the
> divisions. All of the company's employees attend this meeting.
>
> ::: scheme
> (meeting whole-company (Wednesday 4pm))
> :::
>
> a.  On Friday morning, Ben wants to query the data base for all the
>     meetings that occur that day. What query should he use?
>
> b.  Alyssa P. Hacker is unimpressed. She thinks it would be much more
>     useful to be able to ask for her meetings by specifying her name.
>     So she designs a rule that says that a person's meetings include
>     all `whole/company` meetings plus all meetings of that person's
>     division. Fill in the body of Alyssa's rule.
>
>     ::: scheme
>     (rule (meeting-time ?person ?day-and-time)
>      $\color{SchemeDark}\langle$  *rule-body*  $\color{SchemeDark}\rangle$ )
>     :::
>
> c.  Alyssa arrives at work on Wednesday morning and wonders what
>     meetings she has to attend that day. Having defined the above
>     rule, what query should she make to find this out?

> **[]{#Exercise 4.60 label="Exercise 4.60"}Exercise 4.60:** By giving
> the query
>
> ::: scheme
> (lives-near ?person (Hacker Alyssa P))
> :::
>
> Alyssa P. Hacker is able to find people who live near her, with whom
> she can ride to work. On the other hand, when she tries to find all
> pairs of people who live near each other by querying
>
> ::: scheme
> (lives-near ?person-1 ?person-2)
> :::
>
> she notices that each pair of people who live near each other is
> listed twice; for example,
>
> ::: scheme
> (lives-near (Hacker Alyssa P) (Fect Cy D)) (lives-near (Fect Cy D)
> (Hacker Alyssa P))
> :::
>
> Why does this happen? Is there a way to find a list of people who live
> near each other, in which each pair appears only once? Explain.

#### Logic as programs {#logic-as-programs .unnumbered}

We can regard a rule as a kind of logical implication: *If* an
assignment of values to pattern variables satisfies the body, *then* it
satisfies the conclusion. Consequently, we can regard the query language
as having the ability to perform *logical deductions* based upon the
rules. As an example, consider the `append` operation described at the
beginning of [Section 4.4](#Section 4.4). As we said, `append` can be
characterized by the following two rules:

-   For any list `y`, the empty list and `y` `append` to form `y`.

-   For any `u`, `v`, `y`, and `z`, `(cons u v)` and `y` `append` to
    form `(cons u z)` if `v` and `y` `append` to form `z`.

To express this in our query language, we define two rules for a
relation

::: scheme
(append-to-form x y z)
:::

which we can interpret to mean "`x` and `y` `append` to form `z`":

::: scheme
(rule (append-to-form () ?y ?y)) (rule (append-to-form (?u . ?v) ?y (?u
. ?z)) (append-to-form ?v ?y ?z))
:::

The first rule has no body, which means that the conclusion holds for
any value of `?y`. Note how the second rule makes use of dotted-tail
notation to name the `car` and `cdr` of a list.

Given these two rules, we can formulate queries that compute the
`append` of two lists:

::: scheme
 *;;; Query input:*  (append-to-form (a b) (c d) ?z)  *;;; Query
results:*  (append-to-form (a b) (c d) (a b c d))
:::

What is more striking, we can use the same rules to ask the question
"Which list, when `append`ed to `(a b)`, yields `(a b c d)`?" This is
done as follows:

::: scheme
 *;;; Query input:*  (append-to-form (a b) ?y (a b c d))  *;;; Query
results:*  (append-to-form (a b) (c d) (a b c d))
:::

We can also ask for all pairs of lists that `append` to form
`(a b c d)`:

::: scheme
 *;;; Query input:*  (append-to-form ?x ?y (a b c d))  *;;; Query
results:*  (append-to-form () (a b c d) (a b c d)) (append-to-form (a)
(b c d) (a b c d)) (append-to-form (a b) (c d) (a b c d))
(append-to-form (a b c) (d) (a b c d)) (append-to-form (a b c d) () (a b
c d))
:::

The query system may seem to exhibit quite a bit of intelligence in
using the rules to deduce the answers to the queries above. Actually, as
we will see in the next section, the system is following a
well-determined algorithm in unraveling the rules. Unfortunately,
although the system works impressively in the `append` case, the general
methods may break down in more complex cases, as we will see in [Section
4.4.3](#Section 4.4.3).

> **[]{#Exercise 4.61 label="Exercise 4.61"}Exercise 4.61:** The
> following rules implement a `next/to` relation that finds adjacent
> elements of a list:
>
> ::: scheme
> (rule (?x next-to ?y in (?x ?y . ?u))) (rule (?x next-to ?y in (?v .
> ?z)) (?x next-to ?y in ?z))
> :::
>
> What will the response be to the following queries?
>
> ::: scheme
> (?x next-to ?y in (1 (2 3) 4)) (?x next-to 1 in (2 1 3 1))
> :::

> **[]{#Exercise 4.62 label="Exercise 4.62"}Exercise 4.62:** Define
> rules to implement the `last/pair` operation of [Exercise
> 2.17](#Exercise 2.17), which returns a list containing the last
> element of a nonempty list. Check your rules on queries such as
> `(last/pair (3) ?x)`, `(last/pair (1 2 3) ?x)` and
> `(last/pair (2 ?x) (3))`. Do your rules work correctly on queries such
> as `(last/pair ?x (3))` ?

> **[]{#Exercise 4.63 label="Exercise 4.63"}Exercise 4.63:** The
> following data base (see Genesis 4) traces the genealogy of the
> descendants of Ada back to Adam, by way of Cain:
>
> ::: scheme
> (son Adam Cain) (son Cain Enoch) (son Enoch Irad) (son Irad Mehujael)
> (son Mehujael Methushael) (son Methushael Lamech) (wife Lamech Ada)
> (son Ada Jabal) (son Ada Jubal)
> :::
>
> Formulate rules such as "If $S$ is the son of $f$, and $f$ is the son
> of $G$, then $S$ is the grandson of $G$" and "If $W$ is the wife of
> $M$, and $S$ is the son of $W$, then $S$ is the son of $M$" (which was
> supposedly more true in biblical times than today) that will enable
> the query system to find the grandson of Cain; the sons of Lamech; the
> grandsons of Methushael. (See [Exercise 4.69](#Exercise 4.69) for some
> rules to deduce more complicated relationships.)

### How the Query System Works {#Section 4.4.2}

In [Section 4.4.4](#Section 4.4.4) we will present an implementation of
the query interpreter as a collection of procedures. In this section we
give an overview that explains the general structure of the system
independent of low-level implementation details. After describing the
implementation of the interpreter, we will be in a position to
understand some of its limitations and some of the subtle ways in which
the query language's logical operations differ from the operations of
mathematical logic.

It should be apparent that the query evaluator must perform some kind of
search in order to match queries against facts and rules in the data
base. One way to do this would be to implement the query system as a
nondeterministic program, using the `amb` evaluator of [Section
4.3](#Section 4.3) (see [Exercise 4.78](#Exercise 4.78)). Another
possibility is to manage the search with the aid of streams. Our
implementation follows this second approach.

The query system is organized around two central operations called
*pattern matching* and *unification*. We first describe pattern matching
and explain how this operation, together with the organization of
information in terms of streams of frames, enables us to implement both
simple and compound queries. We next discuss unification, a
generalization of pattern matching needed to implement rules. Finally,
we show how the entire query interpreter fits together through a
procedure that classifies expressions in a manner analogous to the way
`eval` classifies expressions for the interpreter described in [Section
4.1](#Section 4.1).

#### Pattern matching {#pattern-matching .unnumbered}

A *pattern matcher* is a program that tests whether some datum fits a
specified pattern. For example, the data list `((a b) c (a b))` matches
the pattern `(?x c ?x)` with the pattern variable `?x` bound to `(a b)`.
The same data list matches the pattern `(?x ?y ?z)` with `?x` and `?z`
both bound to `(a b)` and `?y` bound to `c`. It also matches the pattern
`((?x ?y) c (?x ?y))` with `?x` bound to `a` and `?y` bound to `b`.
However, it does not match the pattern `(?x a ?y)`, since that pattern
specifies a list whose second element is the symbol `a`.

The pattern matcher used by the query system takes as inputs a pattern,
a datum, and a *frame* that specifies bindings for various pattern
variables. It checks whether the datum matches the pattern in a way that
is consistent with the bindings already in the frame. If so, it returns
the given frame augmented by any bindings that may have been determined
by the match. Otherwise, it indicates that the match has failed.

For example, using the pattern `(?x ?y ?x)` to match `(a b a)` given an
empty frame will return a frame specifying that `?x` is bound to `a` and
`?y` is bound to `b`. Trying the match with the same pattern, the same
datum, and a frame specifying that `?y` is bound to `a` will fail.
Trying the match with the same pattern, the same datum, and a frame in
which `?y` is bound to `b` and `?x` is unbound will return the given
frame augmented by a binding of `?x` to `a`.

The pattern matcher is all the mechanism that is needed to process
simple queries that don't involve rules. For instance, to process the
query

::: scheme
(job ?x (computer programmer))
:::

we scan through all assertions in the data base and select those that
match the pattern with respect to an initially empty frame. For each
match we find, we use the frame returned by the match to instantiate the
pattern with a value for `?x`.

#### Streams of frames {#streams-of-frames .unnumbered}

The testing of patterns against frames is organized through the use of
streams. Given a single frame, the matching process runs through the
data-base entries one by one. For each data-base entry, the matcher
generates either a special symbol indicating that the match has failed
or an extension to the frame. The results for all the data-base entries
are collected into a stream, which is passed through a filter to weed
out the failures. The result is a stream of all the frames that extend
the given frame via a match to some assertion in the data base.[^271]

In our system, a query takes an input stream of frames and performs the
above matching operation for every frame in the stream, as indicated in
[Figure 4.4](#Figure 4.4). That is, for each frame in the input stream,
the query generates a new stream consisting of all extensions to that
frame by matches to assertions in the data base. All these streams are
then combined to form one huge stream, which contains all possible
extensions of every frame in the input stream. This stream is the output
of the query.

To answer a simple query, we use the query with an input stream
consisting of a single empty frame. The resulting output stream contains
all extensions to the empty frame (that is, all answers to our query).
This stream of frames is then used to generate a stream of copies of the
original query pattern with the variables instantiated by the values in
each frame, and this is the stream that is finally printed.

[]{#Figure 4.4 label="Figure 4.4"}

![image](fig/chap4/Fig4.4.pdf){width="102mm"}

**Figure 4.4:** A query processes a stream of frames.

#### Compound queries {#compound-queries-1 .unnumbered}

The real elegance of the stream-of-frames implementation is evident when
we deal with compound queries. The processing of compound queries makes
use of the ability of our matcher to demand that a match be consistent
with a specified frame. For example, to handle the `and` of two queries,
such as

::: scheme
(and (can-do-job ?x (computer programmer trainee)) (job ?person ?x))
:::

(informally, "Find all people who can do the job of a computer
programmer trainee"), we first find all entries that match the pattern

::: scheme
(can-do-job ?x (computer programmer trainee))
:::

[]{#Figure 4.5 label="Figure 4.5"}

![image](fig/chap4/Fig4.5.pdf){width="93mm"}

> **Figure 4.5:** The `and` combination of two queries is produced by
> operating on the stream of frames in series.

This produces a stream of frames, each of which contains a binding for
`?x`. Then for each frame in the stream we find all entries that match

::: scheme
(job ?person ?x)
:::

in a way that is consistent with the given binding for `?x`. Each such
match will produce a frame containing bindings for `?x` and `?person`.
The `and` of two queries can be viewed as a series combination of the
two component queries, as shown in [Figure 4.5](#Figure 4.5). The frames
that pass through the first query filter are filtered and further
extended by the second query.

[]{#Figure 4.6 label="Figure 4.6"}

![image](fig/chap4/Fig4.6.pdf){width="107mm"}

> **Figure 4.6:** The `or` combination of two queries is produced by
> operating on the stream of frames in parallel and merging the results.

[Figure 4.6](#Figure 4.6) shows the analogous method for computing the
`or` of two queries as a parallel combination of the two component
queries. The input stream of frames is extended separately by each
query. The two resulting streams are then merged to produce the final
output stream.

Even from this high-level description, it is apparent that the
processing of compound queries can be slow. For example, since a query
may produce more than one output frame for each input frame, and each
query in an `and` gets its input frames from the previous query, an
`and` query could, in the worst case, have to perform a number of
matches that is exponential in the number of queries (see [Exercise
4.76](#Exercise 4.76)).[^272] Though systems for handling only simple
queries are quite practical, dealing with complex queries is extremely
difficult.[^273]

From the stream-of-frames viewpoint, the `not` of some query acts as a
filter that removes all frames for which the query can be satisfied. For
instance, given the pattern

::: scheme
(not (job ?x (computer programmer)))
:::

we attempt, for each frame in the input stream, to produce extension
frames that satisfy `(job ?x (computer programmer))`. We remove from the
input stream all frames for which such extensions exist. The result is a
stream consisting of only those frames in which the binding for `?x`
does not satisfy `(job ?x (computer programmer))`. For example, in
processing the query

::: scheme
(and (supervisor ?x ?y) (not (job ?x (computer programmer))))
:::

the first clause will generate frames with bindings for `?x` and `?y`.
The `not` clause will then filter these by removing all frames in which
the binding for `?x` satisfies the restriction that `?x` is a computer
programmer.[^274]

The `lisp/value` special form is implemented as a similar filter on
frame streams. We use each frame in the stream to instantiate any
variables in the pattern, then apply the Lisp predicate. We remove from
the input stream all frames for which the predicate fails.

#### Unification {#unification .unnumbered}

In order to handle rules in the query language, we must be able to find
the rules whose conclusions match a given query pattern. Rule
conclusions are like assertions except that they can contain variables,
so we will need a generalization of pattern matching---called
*unification*---in which both the "pattern" and the "datum" may contain
variables.

A unifier takes two patterns, each containing constants and variables,
and determines whether it is possible to assign values to the variables
that will make the two patterns equal. If so, it returns a frame
containing these bindings. For example, unifying `(?x a ?y)` and
`(?y ?z a)` will specify a frame in which `?x`, `?y`, and `?z` must all
be bound to `a`. On the other hand, unifying `(?x ?y a)` and `(?x b ?y)`
will fail, because there is no value for `?y` that can make the two
patterns equal. (For the second elements of the patterns to be equal,
`?y` would have to be `b`; however, for the third elements to be equal,
`?y` would have to be `a`.) The unifier used in the query system, like
the pattern matcher, takes a frame as input and performs unifications
that are consistent with this frame.

The unification algorithm is the most technically difficult part of the
query system. With complex patterns, performing unification may seem to
require deduction. To unify `(?x ?x)` and `((a ?y c) (a b ?z))`, for
example, the algorithm must infer that `?x` should be `(a b c)`, `?y`
should be `b`, and `?z` should be `c`. We may think of this process as
solving a set of equations among the pattern components. In general,
these are simultaneous equations, which may require substantial
manipulation to solve.[^275] For example, unifying `(?x ?x)` and
`((a ?y c) (a b ?z))` may be thought of as specifying the simultaneous
equations

::: scheme
?x = (a ?y c) ?x = (a b ?z)
:::

These equations imply that

::: scheme
(a ?y c) = (a b ?z)
:::

which in turn implies that

::: scheme
a = a, ?y = b, c = ?z,
:::

and hence that

::: scheme
?x = (a b c)
:::

In a successful pattern match, all pattern variables become bound, and
the values to which they are bound contain only constants. This is also
true of all the examples of unification we have seen so far. In general,
however, a successful unification may not completely determine the
variable values; some variables may remain unbound and others may be
bound to values that contain variables.

Consider the unification of `(?x a)` and `((b ?y) ?z)`. We can deduce
that `?x = (b ?y)` and `a = ?z`, but we cannot further solve for `?x` or
`?y`. The unification doesn't fail, since it is certainly possible to
make the two patterns equal by assigning values to `?x` and `?y`. Since
this match in no way restricts the values `?y` can take on, no binding
for `?y` is put into the result frame. The match does, however, restrict
the value of `?x`. Whatever value `?y` has, `?x` must be `(b ?y)`. A
binding of `?x` to the pattern `(b ?y)` is thus put into the frame. If a
value for `?y` is later determined and added to the frame (by a pattern
match or unification that is required to be consistent with this frame),
the previously bound `?x` will refer to this value.[^276]

#### Applying rules {#applying-rules .unnumbered}

Unification is the key to the component of the query system that makes
inferences from rules. To see how this is accomplished, consider
processing a query that involves applying a rule, such as

::: scheme
(lives-near ?x (Hacker Alyssa P))
:::

To process this query, we first use the ordinary pattern-match procedure
described above to see if there are any assertions in the data base that
match this pattern. (There will not be any in this case, since our data
base includes no direct assertions about who lives near whom.) The next
step is to attempt to unify the query pattern with the conclusion of
each rule. We find that the pattern unifies with the conclusion of the
rule

::: scheme
(rule (lives-near ?person-1 ?person-2) (and (address ?person-1 (?town .
?rest-1)) (address ?person-2 (?town . ?rest-2)) (not (same ?person-1
?person-2))))
:::

resulting in a frame specifying that `?person/2` is bound to
`(Hacker Alyssa P)` and that `?x` should be bound to (have the same
value as) `?person/1`. Now, relative to this frame, we evaluate the
compound query given by the body of the rule. Successful matches will
extend this frame by providing a binding for `?person/1`, and
consequently a value for `?x`, which we can use to instantiate the
original query pattern.

In general, the query evaluator uses the following method to apply a
rule when trying to establish a query pattern in a frame that specifies
bindings for some of the pattern variables:

-   Unify the query with the conclusion of the rule to form, if
    successful, an extension of the original frame.

-   Relative to the extended frame, evaluate the query formed by the
    body of the rule.

Notice how similar this is to the method for applying a procedure in the
`eval`/`apply` evaluator for Lisp:

-   Bind the procedure's parameters to its arguments to form a frame
    that extends the original procedure environment.

-   Relative to the extended environment, evaluate the expression formed
    by the body of the procedure.

The similarity between the two evaluators should come as no surprise.
Just as procedure definitions are the means of abstraction in Lisp, rule
definitions are the means of abstraction in the query language. In each
case, we unwind the abstraction by creating appropriate bindings and
evaluating the rule or procedure body relative to these.

#### Simple queries {#simple-queries-1 .unnumbered}

We saw earlier in this section how to evaluate simple queries in the
absence of rules. Now that we have seen how to apply rules, we can
describe how to evaluate simple queries by using both rules and
assertions.

Given the query pattern and a stream of frames, we produce, for each
frame in the input stream, two streams:

-   a stream of extended frames obtained by matching the pattern against
    all assertions in the data base (using the pattern matcher), and

-   a stream of extended frames obtained by applying all possible rules
    (using the unifier).[^277]

Appending these two streams produces a stream that consists of all the
ways that the given pattern can be satisfied consistent with the
original frame. These streams (one for each frame in the input stream)
are now all combined to form one large stream, which therefore consists
of all the ways that any of the frames in the original input stream can
be extended to produce a match with the given pattern.

#### The query evaluator and the driver loop {#the-query-evaluator-and-the-driver-loop .unnumbered}

Despite the complexity of the underlying matching operations, the system
is organized much like an evaluator for any language. The procedure that
coordinates the matching operations is called `qeval`, and it plays a
role analogous to that of the `eval` procedure for Lisp. `qeval` takes
as inputs a query and a stream of frames. Its output is a stream of
frames, corresponding to successful matches to the query pattern, that
extend some frame in the input stream, as indicated in [Figure
4.4](#Figure 4.4). Like `eval`, `qeval` classifies the different types
of expressions (queries) and dispatches to an appropriate procedure for
each. There is a procedure for each special form (`and`, `or`, `not`,
and `lisp/value`) and one for simple queries.

The driver loop, which is analogous to the `driver/loop` procedure for
the other evaluators in this chapter, reads queries from the terminal.
For each query, it calls `qeval` with the query and a stream that
consists of a single empty frame. This will produce the stream of all
possible matches (all possible extensions to the empty frame). For each
frame in the resulting stream, it instantiates the original query using
the values of the variables found in the frame. This stream of
instantiated queries is then printed.[^278]

The driver also checks for the special command `assert!`, which signals
that the input is not a query but rather an assertion or rule to be
added to the data base. For instance,

::: scheme
(assert! (job (Bitdiddle Ben) (computer wizard))) (assert! (rule (wheel
?person) (and (supervisor ?middle-manager ?person) (supervisor ?x
?middle-manager))))
:::

### Is Logic Programming Mathematical Logic? {#Section 4.4.3}

The means of combination used in the query language may at first seem
identical to the operations `and`, `or`, and `not` of mathematical
logic, and the application of query-language rules is in fact
accomplished through a legitimate method of inference.[^279] This
identification of the query language with mathematical logic is not
really valid, though, because the query language provides a *control
structure* that interprets the logical statements procedurally. We can
often take advantage of this control structure. For example, to find all
of the supervisors of programmers we could formulate a query in either
of two logically equivalent forms:

::: scheme
(and (job ?x (computer programmer)) (supervisor ?x ?y))
:::

or

::: scheme
(and (supervisor ?x ?y) (job ?x (computer programmer)))
:::

If a company has many more supervisors than programmers (the usual
case), it is better to use the first form rather than the second because
the data base must be scanned for each intermediate result (frame)
produced by the first clause of the `and`.

The aim of logic programming is to provide the programmer with
techniques for decomposing a computational problem into two separate
problems: "what" is to be computed, and "how" this should be computed.
This is accomplished by selecting a subset of the statements of
mathematical logic that is powerful enough to be able to describe
anything one might want to compute, yet weak enough to have a
controllable procedural interpretation. The intention here is that, on
the one hand, a program specified in a logic programming language should
be an effective program that can be carried out by a computer. Control
("how" to compute) is effected by using the order of evaluation of the
language. We should be able to arrange the order of clauses and the
order of subgoals within each clause so that the computation is done in
an order deemed to be effective and efficient. At the same time, we
should be able to view the result of the computation ("what" to compute)
as a simple consequence of the laws of logic.

Our query language can be regarded as just such a procedurally
interpretable subset of mathematical logic. An assertion represents a
simple fact (an atomic proposition). A rule represents the implication
that the rule conclusion holds for those cases where the rule body
holds. A rule has a natural procedural interpretation: To establish the
conclusion of the rule, establish the body of the rule. Rules,
therefore, specify computations. However, because rules can also be
regarded as statements of mathematical logic, we can justify any
"inference" accomplished by a logic program by asserting that the same
result could be obtained by working entirely within mathematical
logic.[^280]

#### Infinite loops {#infinite-loops .unnumbered}

A consequence of the procedural interpretation of logic programs is that
it is possible to construct hopelessly inefficient programs for solving
certain problems. An extreme case of inefficiency occurs when the system
falls into infinite loops in making deductions. As a simple example,
suppose we are setting up a data base of famous marriages, including

::: scheme
(assert! (married Minnie Mickey))
:::

If we now ask

::: scheme
(married Mickey ?who)
:::

we will get no response, because the system doesn't know that if $A$ is
married to $B$, then $B$ is married to $A$. So we assert the rule

::: scheme
(assert! (rule (married ?x ?y) (married ?y ?x)))
:::

and again query

::: scheme
(married Mickey ?who)
:::

Unfortunately, this will drive the system into an infinite loop, as
follows:

-   The system finds that the `married` rule is applicable; that is, the
    rule conclusion `(married ?x ?y)` successfully unifies with the
    query pattern `(married Mickey ?who)` to produce a frame in which
    `?x` is bound to `Mickey` and `?y` is bound to `?who`. So the
    interpreter proceeds to evaluate the rule body `(married ?y ?x)` in
    this frame---in effect, to process the query
    `(married ?who Mickey)`.

-   One answer appears directly as an assertion in the data base:
    `(married Minnie Mickey)`.

-   The `married` rule is also applicable, so the interpreter again
    evaluates the rule body, which this time is equivalent to
    `(married Mickey ?who)`.

The system is now in an infinite loop. Indeed, whether the system will
find the simple answer `(married Minnie Mickey)` before it goes into the
loop depends on implementation details concerning the order in which the
system checks the items in the data base. This is a very simple example
of the kinds of loops that can occur. Collections of interrelated rules
can lead to loops that are much harder to anticipate, and the appearance
of a loop can depend on the order of clauses in an `and` (see [Exercise
4.64](#Exercise 4.64)) or on low-level details concerning the order in
which the system processes queries.[^281]

#### Problems with `not` {#problems-with-not .unnumbered}

Another quirk in the query system concerns `not`. Given the data base of
[Section 4.4.1](#Section 4.4.1), consider the following two queries:

::: scheme
(and (supervisor ?x ?y) (not (job ?x (computer programmer)))) (and (not
(job ?x (computer programmer))) (supervisor ?x ?y))
:::

These two queries do not produce the same result. The first query begins
by finding all entries in the data base that match `(supervisor ?x ?y)`,
and then filters the resulting frames by removing the ones in which the
value of `?x` satisfies `(job ?x (computer programmer))`. The second
query begins by filtering the incoming frames to remove those that can
satisfy `(job ?x (computer programmer))`. Since the only incoming frame
is empty, it checks the data base to see if there are any patterns that
satisfy `(job ?x (computer programmer))`. Since there generally are
entries of this form, the `not` clause filters out the empty frame and
returns an empty stream of frames. Consequently, the entire compound
query returns an empty stream.

The trouble is that our implementation of `not` really is meant to serve
as a filter on values for the variables. If a `not` clause is processed
with a frame in which some of the variables remain unbound (as does `?x`
in the example above), the system will produce unexpected results.
Similar problems occur with the use of `lisp/value`---the Lisp predicate
can't work if some of its arguments are unbound. See [Exercise
4.77](#Exercise 4.77).

There is also a much more serious way in which the `not` of the query
language differs from the `not` of mathematical logic. In logic, we
interpret the statement "not $P$" to mean that $P$ is not true. In the
query system, however, "not $P$" means that $P$ is not deducible from
the knowledge in the data base. For example, given the personnel data
base of [Section 4.4.1](#Section 4.4.1), the system would happily deduce
all sorts of `not` statements, such as that Ben Bitdiddle is not a
baseball fan, that it is not raining outside, and that 2 + 2 is not
4.[^282] In other words, the `not` of logic programming languages
reflects the so-called *closed world assumption* that all relevant
information has been included in the data base.[^283]

> **[]{#Exercise 4.64 label="Exercise 4.64"}Exercise 4.64:** Louis
> Reasoner mistakenly deletes the `outranked/by` rule ([Section
> 4.4.1](#Section 4.4.1)) from the data base. When he realizes this, he
> quickly reinstalls it. Unfortunately, he makes a slight change in the
> rule, and types it in as
>
> ::: scheme
> (rule (outranked-by ?staff-person ?boss) (or (supervisor ?staff-person
> ?boss) (and (outranked-by ?middle-manager ?boss) (supervisor
> ?staff-person ?middle-manager))))
> :::
>
> Just after Louis types this information into the system, DeWitt Aull
> comes by to find out who outranks Ben Bitdiddle. He issues the query
>
> ::: scheme
> (outranked-by (Bitdiddle Ben) ?who)
> :::
>
> After answering, the system goes into an infinite loop. Explain why.

> **[]{#Exercise 4.65 label="Exercise 4.65"}Exercise 4.65:** Cy D. Fect,
> looking forward to the day when he will rise in the organization,
> gives a query to find all the wheels (using the `wheel` rule of
> [Section 4.4.1](#Section 4.4.1)):
>
> ::: scheme
> (wheel ?who)
> :::
>
> To his surprise, the system responds
>
> ::: scheme
>  *;;; Query results:*  (wheel (Warbucks Oliver)) (wheel (Bitdiddle
> Ben)) (wheel (Warbucks Oliver)) (wheel (Warbucks Oliver)) (wheel
> (Warbucks Oliver))
> :::
>
> Why is Oliver Warbucks listed four times?

> **[]{#Exercise 4.66 label="Exercise 4.66"}Exercise 4.66:** Ben has
> been generalizing the query system to provide statistics about the
> company. For example, to find the total salaries of all the computer
> programmers one will be able to say
>
> ::: scheme
> (sum ?amount (and (job ?x (computer programmer)) (salary ?x ?amount)))
> :::
>
> In general, Ben's new system allows expressions of the form
>
> ::: scheme
> (accumulation-function
>  $\color{SchemeDark}\langle$  *variable*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *query
> pattern*  $\color{SchemeDark}\rangle$ )
> :::
>
> where `accumulation/function` can be things like `sum`, `average`, or
> `maximum`. Ben reasons that it should be a cinch to implement this. He
> will simply feed the query pattern to `qeval`. This will produce a
> stream of frames. He will then pass this stream through a mapping
> function that extracts the value of the designated variable from each
> frame in the stream and feed the resulting stream of values to the
> accumulation function. Just as Ben completes the implementation and is
> about to try it out, Cy walks by, still puzzling over the `wheel`
> query result in [Exercise 4.65](#Exercise 4.65). When Cy shows Ben the
> system's response, Ben groans, "Oh, no, my simple accumulation scheme
> won't work!"
>
> What has Ben just realized? Outline a method he can use to salvage the
> situation.

> **[]{#Exercise 4.67 label="Exercise 4.67"}Exercise 4.67:** Devise a
> way to install a loop detector in the query system so as to avoid the
> kinds of simple loops illustrated in the text and in [Exercise
> 4.64](#Exercise 4.64). The general idea is that the system should
> maintain some sort of history of its current chain of deductions and
> should not begin processing a query that it is already working on.
> Describe what kind of information (patterns and frames) is included in
> this history, and how the check should be made. (After you study the
> details of the query-system implementation in [Section
> 4.4.4](#Section 4.4.4), you may want to modify the system to include
> your loop detector.)

> **[]{#Exercise 4.68 label="Exercise 4.68"}Exercise 4.68:** Define
> rules to implement the `reverse` operation of [Exercise
> 2.18](#Exercise 2.18), which returns a list containing the same
> elements as a given list in reverse order. (Hint: Use
> `append/to/form`.) Can your rules answer both `(reverse (1 2 3) ?x)`
> and `(reverse ?x (1 2 3))` ?

> **[]{#Exercise 4.69 label="Exercise 4.69"}Exercise 4.69:** Beginning
> with the data base and the rules you formulated in [Exercise
> 4.63](#Exercise 4.63), devise a rule for adding "greats" to a grandson
> relationship. This should enable the system to deduce that Irad is the
> great-grandson of Adam, or that Jabal and Jubal are the
> great-great-great-great-great-grandsons of Adam. (Hint: Represent the
> fact about Irad, for example, as `((great grandson) Adam Irad)`. Write
> rules that determine if a list ends in the word `grandson`. Use this
> to express a rule that allows one to derive the relationship
> `((great . ?rel) ?x ?y)`, where `?rel` is a list ending in
> `grandson`.) Check your rules on queries such as
> `((great grandson) ?g ?ggs)` and `(?relationship Adam Irad)`.

### Implementing the Query System {#Section 4.4.4}

[Section 4.4.2](#Section 4.4.2) described how the query system works.
Now we fill in the details by presenting a complete implementation of
the system.

#### The Driver Loop and Instantiation {#Section 4.4.4.1}

The driver loop for the query system repeatedly reads input expressions.
If the expression is a rule or assertion to be added to the data base,
then the information is added. Otherwise the expression is assumed to be
a query. The driver passes this query to the evaluator `qeval` together
with an initial frame stream consisting of a single empty frame. The
result of the evaluation is a stream of frames generated by satisfying
the query with variable values found in the data base. These frames are
used to form a new stream consisting of copies of the original query in
which the variables are instantiated with values supplied by the stream
of frames, and this final stream is printed at the terminal:

::: scheme
(define input-prompt \";;; Query input:\") (define output-prompt \";;;
Query results:\")

(define (query-driver-loop) (prompt-for-input input-prompt) (let ((q
(query-syntax-process (read)))) (cond ((assertion-to-be-added? q)
(add-rule-or-assertion! (add-assertion-body q)) (newline) (display
\"Assertion added to data base.\") (query-driver-loop)) (else (newline)
(display output-prompt) (display-stream (stream-map (lambda (frame)
(instantiate q frame (lambda (v f) (contract-question-mark v)))) (qeval
q (singleton-stream '())))) (query-driver-loop)))))
:::

Here, as in the other evaluators in this chapter, we use an abstract
syntax for the expressions of the query language. The implementation of
the expression syntax, including the predicate `assertion/to/be/added?`
and the selector `add/assertion/body`, is given in [Section
4.4.4.7](#Section 4.4.4.7). `add/rule/or/assertion!` is defined in
[Section 4.4.4.5](#Section 4.4.4.5).

Before doing any processing on an input expression, the driver loop
transforms it syntactically into a form that makes the processing more
efficient. This involves changing the representation of pattern
variables. When the query is instantiated, any variables that remain
unbound are transformed back to the input representation before being
printed. These transformations are performed by the two procedures
`query/syntax/process` and `contract/question/mark` ([Section
4.4.4.7](#Section 4.4.4.7)).

To instantiate an expression, we copy it, replacing any variables in the
expression by their values in a given frame. The values are themselves
instantiated, since they could contain variables (for example, if `?x`
in `exp` is bound to `?y` as the result of unification and `?y` is in
turn bound to 5). The action to take if a variable cannot be
instantiated is given by a procedural argument to `instantiate`.

::: scheme
(define (instantiate exp frame unbound-var-handler) (define (copy exp)
(cond ((var? exp) (let ((binding (binding-in-frame exp frame))) (if
binding (copy (binding-value binding)) (unbound-var-handler exp
frame)))) ((pair? exp) (cons (copy (car exp)) (copy (cdr exp)))) (else
exp))) (copy exp))
:::

The procedures that manipulate bindings are defined in [Section
4.4.4.8](#Section 4.4.4.8).

#### The Evaluator {#Section 4.4.4.2}

The `qeval` procedure, called by the `query/driver/loop`, is the basic
evaluator of the query system. It takes as inputs a query and a stream
of frames, and it returns a stream of extended frames. It identifies
special forms by a data-directed dispatch using `get` and `put`, just as
we did in implementing generic operations in [Chapter 2](#Chapter 2).
Any query that is not identified as a special form is assumed to be a
simple query, to be processed by `simple/query`.

::: scheme
(define (qeval query frame-stream) (let ((qproc (get (type query)
'qeval))) (if qproc (qproc (contents query) frame-stream) (simple-query
query frame-stream))))
:::

`type` and `contents`, defined in [Section 4.4.4.7](#Section 4.4.4.7),
implement the abstract syntax of the special forms.

#### Simple queries {#simple-queries-2 .unnumbered}

The `simple/query` procedure handles simple queries. It takes as
arguments a simple query (a pattern) together with a stream of frames,
and it returns the stream formed by extending each frame by all
data-base matches of the query.

::: scheme
(define (simple-query query-pattern frame-stream) (stream-flatmap
(lambda (frame) (stream-append-delayed (find-assertions query-pattern
frame) (delay (apply-rules query-pattern frame)))) frame-stream))
:::

For each frame in the input stream, we use `find/assertions` ([Section
4.4.4.3](#Section 4.4.4.3)) to match the pattern against all assertions
in the data base, producing a stream of extended frames, and we use
`apply/rules` ([Section 4.4.4.4](#Section 4.4.4.4)) to apply all
possible rules, producing another stream of extended frames. These two
streams are combined (using `stream/append/delayed`, [Section
4.4.4.6](#Section 4.4.4.6)) to make a stream of all the ways that the
given pattern can be satisfied consistent with the original frame (see
[Exercise 4.71](#Exercise 4.71)). The streams for the individual input
frames are combined using `stream/flatmap` ([Section
4.4.4.6](#Section 4.4.4.6)) to form one large stream of all the ways
that any of the frames in the original input stream can be extended to
produce a match with the given pattern.

#### Compound queries {#compound-queries-2 .unnumbered}

`and` queries are handled as illustrated in [Figure 4.5](#Figure 4.5) by
the `conjoin` procedure. `conjoin` takes as inputs the conjuncts and the
frame stream and returns the stream of extended frames. First, `conjoin`
processes the stream of frames to find the stream of all possible frame
extensions that satisfy the first query in the conjunction. Then, using
this as the new frame stream, it recursively applies `conjoin` to the
rest of the queries.

::: scheme
(define (conjoin conjuncts frame-stream) (if (empty-conjunction?
conjuncts) frame-stream (conjoin (rest-conjuncts conjuncts) (qeval
(first-conjunct conjuncts) frame-stream))))
:::

The expression

::: scheme
(put 'and 'qeval conjoin)
:::

sets up `qeval` to dispatch to `conjoin` when an `and` form is
encountered.

`or` queries are handled similarly, as shown in [Figure
4.6](#Figure 4.6). The output streams for the various disjuncts of the
`or` are computed separately and merged using the `interleave/delayed`
procedure from [Section 4.4.4.6](#Section 4.4.4.6). (See [Exercise
4.71](#Exercise 4.71) and [Exercise 4.72](#Exercise 4.72).)

::: scheme
(define (disjoin disjuncts frame-stream) (if (empty-disjunction?
disjuncts) the-empty-stream (interleave-delayed (qeval (first-disjunct
disjuncts) frame-stream) (delay (disjoin (rest-disjuncts disjuncts)
frame-stream))))) (put 'or 'qeval disjoin)
:::

The predicates and selectors for the syntax of conjuncts and disjuncts
are given in [Section 4.4.4.7](#Section 4.4.4.7).

#### Filters {#filters .unnumbered}

`not` is handled by the method outlined in [Section
4.4.2](#Section 4.4.2). We attempt to extend each frame in the input
stream to satisfy the query being negated, and we include a given frame
in the output stream only if it cannot be extended.

::: scheme
(define (negate operands frame-stream) (stream-flatmap (lambda (frame)
(if (stream-null? (qeval (negated-query operands) (singleton-stream
frame))) (singleton-stream frame) the-empty-stream)) frame-stream)) (put
'not 'qeval negate)
:::

`lisp/value` is a filter similar to `not`. Each frame in the stream is
used to instantiate the variables in the pattern, the indicated
predicate is applied, and the frames for which the predicate returns
false are filtered out of the input stream. An error results if there
are unbound pattern variables.

::: scheme
(define (lisp-value call frame-stream) (stream-flatmap (lambda (frame)
(if (execute (instantiate call frame (lambda (v f) (error \"Unknown pat
var: LISP-VALUE\" v)))) (singleton-stream frame) the-empty-stream))
frame-stream)) (put 'lisp-value 'qeval lisp-value)
:::

`execute`, which applies the predicate to the arguments, must `eval` the
predicate expression to get the procedure to apply. However, it must not
evaluate the arguments, since they are already the actual arguments, not
expressions whose evaluation (in Lisp) will produce the arguments. Note
that `execute` is implemented using `eval` and `apply` from the
underlying Lisp system.

::: scheme
(define (execute exp) (apply (eval (predicate exp)
user-initial-environment) (args exp)))
:::

The `always/true` special form provides for a query that is always
satisfied. It ignores its contents (normally empty) and simply passes
through all the frames in the input stream. `always/true` is used by the
`rule/body` selector ([Section 4.4.4.7](#Section 4.4.4.7)) to provide
bodies for rules that were defined without bodies (that is, rules whose
conclusions are always satisfied).

::: scheme
(define (always-true ignore frame-stream) frame-stream) (put
'always-true 'qeval always-true)
:::

The selectors that define the syntax of `not` and `lisp/value` are given
in [Section 4.4.4.7](#Section 4.4.4.7).

#### Finding Assertions by Pattern Matching {#Section 4.4.4.3}

`find/assertions`, called by `simple/query` ([Section
4.4.4.2](#Section 4.4.4.2)), takes as input a pattern and a frame. It
returns a stream of frames, each extending the given one by a data-base
match of the given pattern. It uses `fetch/assertions` ([Section
4.4.4.5](#Section 4.4.4.5)) to get a stream of all the assertions in the
data base that should be checked for a match against the pattern and the
frame. The reason for `fetch/assertions` here is that we can often apply
simple tests that will eliminate many of the entries in the data base
from the pool of candidates for a successful match. The system would
still work if we eliminated `fetch/assertions` and simply checked a
stream of all assertions in the data base, but the computation would be
less efficient because we would need to make many more calls to the
matcher.

::: scheme
(define (find-assertions pattern frame) (stream-flatmap (lambda (datum)
(check-an-assertion datum pattern frame)) (fetch-assertions pattern
frame)))
:::

`check/an/assertion` takes as arguments a pattern, a data object
(assertion), and a frame and returns either a one-element stream
containing the extended frame or `the/empty/stream` if the match fails.

::: scheme
(define (check-an-assertion assertion query-pat query-frame) (let
((match-result (pattern-match query-pat assertion query-frame))) (if
(eq? match-result 'failed) the-empty-stream (singleton-stream
match-result))))
:::

The basic pattern matcher returns either the symbol `failed` or an
extension of the given frame. The basic idea of the matcher is to check
the pattern against the data, element by element, accumulating bindings
for the pattern variables. If the pattern and the data object are the
same, the match succeeds and we return the frame of bindings accumulated
so far. Otherwise, if the pattern is a variable we extend the current
frame by binding the variable to the data, so long as this is consistent
with the bindings already in the frame. If the pattern and the data are
both pairs, we (recursively) match the `car` of the pattern against the
`car` of the data to produce a frame; in this frame we then match the
`cdr` of the pattern against the `cdr` of the data. If none of these
cases are applicable, the match fails and we return the symbol `failed`.

::: scheme
(define (pattern-match pat dat frame) (cond ((eq? frame 'failed)
'failed) ((equal? pat dat) frame) ((var? pat) (extend-if-consistent pat
dat frame)) ((and (pair? pat) (pair? dat)) (pattern-match (cdr pat) (cdr
dat) (pattern-match (car pat) (car dat) frame))) (else 'failed)))
:::

Here is the procedure that extends a frame by adding a new binding, if
this is consistent with the bindings already in the frame:

::: scheme
(define (extend-if-consistent var dat frame) (let ((binding
(binding-in-frame var frame))) (if binding (pattern-match (binding-value
binding) dat frame) (extend var dat frame))))
:::

If there is no binding for the variable in the frame, we simply add the
binding of the variable to the data. Otherwise we match, in the frame,
the data against the value of the variable in the frame. If the stored
value contains only constants, as it must if it was stored during
pattern matching by `extend/if/consistent`, then the match simply tests
whether the stored and new values are the same. If so, it returns the
unmodified frame; if not, it returns a failure indication. The stored
value may, however, contain pattern variables if it was stored during
unification (see [Section 4.4.4.4](#Section 4.4.4.4)). The recursive
match of the stored pattern against the new data will add or check
bindings for the variables in this pattern. For example, suppose we have
a frame in which `?x` is bound to `(f ?y)` and `?y` is unbound, and we
wish to augment this frame by a binding of `?x` to `(f b)`. We look up
`?x` and find that it is bound to `(f ?y)`. This leads us to match
`(f ?y)` against the proposed new value `(f b)` in the same frame.
Eventually this match extends the frame by adding a binding of `?y` to
`b`. `?X` remains bound to `(f ?y)`. We never modify a stored binding
and we never store more than one binding for a given variable.

The procedures used by `extend/if/consistent` to manipulate bindings are
defined in [Section 4.4.4.8](#Section 4.4.4.8).

#### Patterns with dotted tails {#patterns-with-dotted-tails .unnumbered}

If a pattern contains a dot followed by a pattern variable, the pattern
variable matches the rest of the data list (rather than the next element
of the data list), just as one would expect with the dotted-tail
notation described in [Exercise 2.20](#Exercise 2.20). Although the
pattern matcher we have just implemented doesn't look for dots, it does
behave as we want. This is because the Lisp `read` primitive, which is
used by `query/driver/loop` to read the query and represent it as a list
structure, treats dots in a special way.

When `read` sees a dot, instead of making the next item be the next
element of a list (the `car` of a `cons` whose `cdr` will be the rest of
the list) it makes the next item be the `cdr` of the list structure. For
example, the list structure produced by `read` for the pattern
`(computer ?type)` could be constructed by evaluating the expression
`(cons ’computer (cons ’?type ’()))`, and that for `(computer . ?type)`
could be constructed by evaluating the expression
`(cons ’computer ’?type)`.

Thus, as `pattern/match` recursively compares `car`s and `cdr`s of a
data list and a pattern that had a dot, it eventually matches the
variable after the dot (which is a `cdr` of the pattern) against a
sublist of the data list, binding the variable to that list. For
example, matching the pattern `(computer . ?type)` against
`(computer programmer trainee)` will match `?type` against the list
`(programmer trainee)`.

#### Rules and Unification {#Section 4.4.4.4}

`apply/rules` is the rule analog of `find/assertions` ([Section
4.4.4.3](#Section 4.4.4.3)). It takes as input a pattern and a frame,
and it forms a stream of extension frames by applying rules from the
data base. `stream/flatmap` maps `apply/a/rule` down the stream of
possibly applicable rules (selected by `fetch/rules`, [Section
4.4.4.5](#Section 4.4.4.5)) and combines the resulting streams of
frames.

::: scheme
(define (apply-rules pattern frame) (stream-flatmap (lambda (rule)
(apply-a-rule rule pattern frame)) (fetch-rules pattern frame)))
:::

`apply/a/rule` applies rules using the method outlined in [Section
4.4.2](#Section 4.4.2). It first augments its argument frame by unifying
the rule conclusion with the pattern in the given frame. If this
succeeds, it evaluates the rule body in this new frame.

Before any of this happens, however, the program renames all the
variables in the rule with unique new names. The reason for this is to
prevent the variables for different rule applications from becoming
confused with each other. For instance, if two rules both use a variable
named `?x`, then each one may add a binding for `?x` to the frame when
it is applied. These two `?x`'s have nothing to do with each other, and
we should not be fooled into thinking that the two bindings must be
consistent. Rather than rename variables, we could devise a more clever
environment structure; however, the renaming approach we have chosen
here is the most straightforward, even if not the most efficient. (See
[Exercise 4.79](#Exercise 4.79).) Here is the `apply/a/rule` procedure:

::: scheme
(define (apply-a-rule rule query-pattern query-frame) (let ((clean-rule
(rename-variables-in rule))) (let ((unify-result (unify-match
query-pattern (conclusion clean-rule) query-frame))) (if (eq?
unify-result 'failed) the-empty-stream (qeval (rule-body clean-rule)
(singleton-stream unify-result))))))
:::

The selectors `rule/body` and `conclusion` that extract parts of a rule
are defined in [Section 4.4.4.7](#Section 4.4.4.7).

We generate unique variable names by associating a unique identifier
(such as a number) with each rule application and combining this
identifier with the original variable names. For example, if the
rule-application identifier is 7, we might change each `?x` in the rule
to `?x/7` and each `?y` in the rule to `?y/7`. (`make/new/variable` and
`new/rule/application/id` are included with the syntax procedures in
[Section 4.4.4.7](#Section 4.4.4.7).)

::: scheme
(define (rename-variables-in rule) (let ((rule-application-id
(new-rule-application-id))) (define (tree-walk exp) (cond ((var? exp)
(make-new-variable exp rule-application-id)) ((pair? exp) (cons
(tree-walk (car exp)) (tree-walk (cdr exp)))) (else exp))) (tree-walk
rule)))
:::

The unification algorithm is implemented as a procedure that takes as
inputs two patterns and a frame and returns either the extended frame or
the symbol `failed`. The unifier is like the pattern matcher except that
it is symmetrical---variables are allowed on both sides of the match.
`unify/match` is basically the same as `pattern/match`, except that
there is extra code (marked "`***`" below) to handle the case where the
object on the right side of the match is a variable.

::: scheme
(define (unify-match p1 p2 frame) (cond ((eq? frame 'failed) 'failed)
((equal? p1 p2) frame) ((var? p1) (extend-if-possible p1 p2 frame))
((var? p2) (extend-if-possible p2 p1 frame))  [; \*\*\*]{.roman}  ((and
(pair? p1) (pair? p2)) (unify-match (cdr p1) (cdr p2) (unify-match (car
p1) (car p2) frame))) (else 'failed)))
:::

In unification, as in one-sided pattern matching, we want to accept a
proposed extension of the frame only if it is consistent with existing
bindings. The procedure `extend/if/possible` used in unification is the
same as the `extend/if/consistent` used in pattern matching except for
two special checks, marked "`***`" in the program below. In the first
case, if the variable we are trying to match is not bound, but the value
we are trying to match it with is itself a (different) variable, it is
necessary to check to see if the value is bound, and if so, to match its
value. If both parties to the match are unbound, we may bind either to
the other.

The second check deals with attempts to bind a variable to a pattern
that includes that variable. Such a situation can occur whenever a
variable is repeated in both patterns. Consider, for example, unifying
the two patterns `(?x ?x)` and
`(?y `$\langle$*`expression involving ``?y`*$\rangle$`)` in a frame
where both `?x` and `?y` are unbound. First `?x` is matched against
`?y`, making a binding of `?x` to `?y`. Next, the same `?x` is matched
against the given expression involving `?y`. Since `?x` is already bound
to `?y`, this results in matching `?y` against the expression. If we
think of the unifier as finding a set of values for the pattern
variables that make the patterns the same, then these patterns imply
instructions to find a `?y` such that `?y` is equal to the expression
involving `?y`. There is no general method for solving such equations,
so we reject such bindings; these cases are recognized by the predicate
`depends/on?`.[^284] On the other hand, we do not want to reject
attempts to bind a variable to itself. For example, consider unifying
`(?x ?x)` and `(?y ?y)`. The second attempt to bind `?x` to `?y` matches
`?y` (the stored value of `?x`) against `?y` (the new value of `?x`).
This is taken care of by the `equal?` clause of `unify/match`.

::: scheme
(define (extend-if-possible var val frame) (let ((binding
(binding-in-frame var frame))) (cond (binding (unify-match
(binding-value binding) val frame)) ((var? val)  [; \*\*\*]{.roman}
(let ((binding (binding-in-frame val frame))) (if binding (unify-match
var (binding-value binding) frame) (extend var val frame))))
((depends-on? val var frame)  [; \*\*\*]{.roman}  'failed) (else (extend
var val frame)))))
:::

`depends/on?` is a predicate that tests whether an expression proposed
to be the value of a pattern variable depends on the variable. This must
be done relative to the current frame because the expression may contain
occurrences of a variable that already has a value that depends on our
test variable. The structure of `depends/on?` is a simple recursive tree
walk in which we substitute for the values of variables whenever
necessary.

::: scheme
(define (depends-on? exp var frame) (define (tree-walk e) (cond ((var?
e) (if (equal? var e) true (let ((b (binding-in-frame e frame))) (if b
(tree-walk (binding-value b)) false)))) ((pair? e) (or (tree-walk (car
e)) (tree-walk (cdr e)))) (else false))) (tree-walk exp))
:::

#### Maintaining the Data Base {#Section 4.4.4.5}

One important problem in designing logic programming languages is that
of arranging things so that as few irrelevant data-base entries as
possible will be examined in checking a given pattern. In our system, in
addition to storing all assertions in one big stream, we store all
assertions whose `car`s are constant symbols in separate streams, in a
table indexed by the symbol. To fetch an assertion that may match a
pattern, we first check to see if the `car` of the pattern is a constant
symbol. If so, we return (to be tested using the matcher) all the stored
assertions that have the same `car`. If the pattern's `car` is not a
constant symbol, we return all the stored assertions. Cleverer methods
could also take advantage of information in the frame, or try also to
optimize the case where the `car` of the pattern is not a constant
symbol. We avoid building our criteria for indexing (using the `car`,
handling only the case of constant symbols) into the program; instead we
call on predicates and selectors that embody our criteria.

::: scheme
(define THE-ASSERTIONS the-empty-stream) (define (fetch-assertions
pattern frame) (if (use-index? pattern) (get-indexed-assertions pattern)
(get-all-assertions))) (define (get-all-assertions) THE-ASSERTIONS)
(define (get-indexed-assertions pattern) (get-stream (index-key-of
pattern) 'assertion-stream))
:::

`get/stream` looks up a stream in the table and returns an empty stream
if nothing is stored there.

::: scheme
(define (get-stream key1 key2) (let ((s (get key1 key2))) (if s s
the-empty-stream)))
:::

Rules are stored similarly, using the `car` of the rule conclusion. Rule
conclusions are arbitrary patterns, however, so they differ from
assertions in that they can contain variables. A pattern whose `car` is
a constant symbol can match rules whose conclusions start with a
variable as well as rules whose conclusions have the same `car`. Thus,
when fetching rules that might match a pattern whose `car` is a constant
symbol we fetch all rules whose conclusions start with a variable as
well as those whose conclusions have the same `car` as the pattern. For
this purpose we store all rules whose conclusions start with a variable
in a separate stream in our table, indexed by the symbol `?`.

::: scheme
(define THE-RULES the-empty-stream) (define (fetch-rules pattern frame)
(if (use-index? pattern) (get-indexed-rules pattern) (get-all-rules)))
(define (get-all-rules) THE-RULES) (define (get-indexed-rules pattern)
(stream-append (get-stream (index-key-of pattern) 'rule-stream)
(get-stream '? 'rule-stream)))
:::

`add/rule/or/assertion!` is used by `query/driver/loop` to add
assertions and rules to the data base. Each item is stored in the index,
if appropriate, and in a stream of all assertions or rules in the data
base.

::: scheme
(define (add-rule-or-assertion! assertion) (if (rule? assertion)
(add-rule! assertion) (add-assertion! assertion))) (define
(add-assertion! assertion) (store-assertion-in-index assertion) (let
((old-assertions THE-ASSERTIONS)) (set! THE-ASSERTIONS (cons-stream
assertion old-assertions)) 'ok)) (define (add-rule! rule)
(store-rule-in-index rule) (let ((old-rules THE-RULES)) (set! THE-RULES
(cons-stream rule old-rules)) 'ok))
:::

To actually store an assertion or a rule, we check to see if it can be
indexed. If so, we store it in the appropriate stream.

::: scheme
(define (store-assertion-in-index assertion) (if (indexable? assertion)
(let ((key (index-key-of assertion))) (let ((current-assertion-stream
(get-stream key 'assertion-stream))) (put key 'assertion-stream
(cons-stream assertion current-assertion-stream)))))) (define
(store-rule-in-index rule) (let ((pattern (conclusion rule))) (if
(indexable? pattern) (let ((key (index-key-of pattern))) (let
((current-rule-stream (get-stream key 'rule-stream))) (put key
'rule-stream (cons-stream rule current-rule-stream)))))))
:::

The following procedures define how the data-base index is used. A
pattern (an assertion or a rule conclusion) will be stored in the table
if it starts with a variable or a constant symbol.

::: scheme
(define (indexable? pat) (or (constant-symbol? (car pat)) (var? (car
pat))))
:::

The key under which a pattern is stored in the table is either `?` (if
it starts with a variable) or the constant symbol with which it starts.

::: scheme
(define (index-key-of pat) (let ((key (car pat))) (if (var? key) '?
key)))
:::

The index will be used to retrieve items that might match a pattern if
the pattern starts with a constant symbol.

::: scheme
(define (use-index? pat) (constant-symbol? (car pat)))
:::

> **[]{#Exercise 4.70 label="Exercise 4.70"}Exercise 4.70:** What is the
> purpose of the `let` bindings in the procedures `add/assertion!` and
> `add/rule!` ? What would be wrong with the following implementation of
> `add/assertion!` ? Hint: Recall the definition of the infinite stream
> of ones in [Section 3.5.2](#Section 3.5.2):
> `(define ones (cons/stream 1 ones))`.
>
> ::: scheme
> (define (add-assertion! assertion) (store-assertion-in-index
> assertion) (set! THE-ASSERTIONS (cons-stream assertion
> THE-ASSERTIONS)) 'ok)
> :::

#### Stream Operations {#Section 4.4.4.6}

The query system uses a few stream operations that were not presented in
[Chapter 3](#Chapter 3).

`stream/append/delayed` and `interleave/delayed` are just like
`stream/append` and `interleave` ([Section 3.5.3](#Section 3.5.3)),
except that they take a delayed argument (like the `integral` procedure
in [Section 3.5.4](#Section 3.5.4)). This postpones looping in some
cases (see [Exercise 4.71](#Exercise 4.71)).

::: scheme
(define (stream-append-delayed s1 delayed-s2) (if (stream-null? s1)
(force delayed-s2) (cons-stream (stream-car s1) (stream-append-delayed
(stream-cdr s1) delayed-s2)))) (define (interleave-delayed s1
delayed-s2) (if (stream-null? s1) (force delayed-s2) (cons-stream
(stream-car s1) (interleave-delayed (force delayed-s2) (delay
(stream-cdr s1))))))
:::

`stream/flatmap`, which is used throughout the query evaluator to map a
procedure over a stream of frames and combine the resulting streams of
frames, is the stream analog of the `flatmap` procedure introduced for
ordinary lists in [Section 2.2.3](#Section 2.2.3). Unlike ordinary
`flatmap`, however, we accumulate the streams with an interleaving
process, rather than simply appending them (see [Exercise
4.72](#Exercise 4.72) and [Exercise 4.73](#Exercise 4.73)).

::: scheme
(define (stream-flatmap proc s) (flatten-stream (stream-map proc s)))

(define (flatten-stream stream) (if (stream-null? stream)
the-empty-stream (interleave-delayed (stream-car stream) (delay
(flatten-stream (stream-cdr stream))))))
:::

The evaluator also uses the following simple procedure to generate a
stream consisting of a single element:

::: scheme
(define (singleton-stream x) (cons-stream x the-empty-stream))
:::

#### Query Syntax Procedures {#Section 4.4.4.7}

`type` and `contents`, used by `qeval` ([Section
4.4.4.2](#Section 4.4.4.2)), specify that a special form is identified
by the symbol in its `car`. They are the same as the `type/tag` and
`contents` procedures in [Section 2.4.2](#Section 2.4.2), except for the
error message.

::: scheme
(define (type exp) (if (pair? exp) (car exp) (error \"Unknown expression
TYPE\" exp))) (define (contents exp) (if (pair? exp) (cdr exp) (error
\"Unknown expression CONTENTS\" exp)))
:::

The following procedures, used by `query/driver/loop` (in [Section
4.4.4.1](#Section 4.4.4.1)), specify that rules and assertions are added
to the data base by expressions of the form
`(assert! `$\langle$*`rule/or/assertion`*$\rangle$`)`:

::: scheme
(define (assertion-to-be-added? exp) (eq? (type exp) 'assert!)) (define
(add-assertion-body exp) (car (contents exp)))
:::

Here are the syntax definitions for the `and`, `or`, `not`, and
`lisp/value` special forms ([Section 4.4.4.2](#Section 4.4.4.2)):

::: scheme
(define (empty-conjunction? exps) (null? exps)) (define (first-conjunct
exps) (car exps)) (define (rest-conjuncts exps) (cdr exps)) (define
(empty-disjunction? exps) (null? exps)) (define (first-disjunct exps)
(car exps)) (define (rest-disjuncts exps) (cdr exps)) (define
(negated-query exps) (car exps)) (define (predicate exps) (car exps))
(define (args exps) (cdr exps))
:::

The following three procedures define the syntax of rules:

::: scheme
(define (rule? statement) (tagged-list? statement 'rule)) (define
(conclusion rule) (cadr rule)) (define (rule-body rule) (if (null? (cddr
rule)) '(always-true) (caddr rule)))
:::

`query/driver/loop` ([Section 4.4.4.1](#Section 4.4.4.1)) calls
`query/syntax/process` to transform pattern variables in the expression,
which have the form `?symbol`, into the internal format `(? symbol)`.
That is to say, a pattern such as `(job ?x ?y)` is actually represented
internally by the system as `(job (? x) (? y))`. This increases the
efficiency of query processing, since it means that the system can check
to see if an expression is a pattern variable by checking whether the
`car` of the expression is the symbol `?`, rather than having to extract
characters from the symbol. The syntax transformation is accomplished by
the following procedure:[^285]

::: scheme
(define (query-syntax-process exp) (map-over-symbols
expand-question-mark exp)) (define (map-over-symbols proc exp) (cond
((pair? exp) (cons (map-over-symbols proc (car exp)) (map-over-symbols
proc (cdr exp)))) ((symbol? exp) (proc exp)) (else exp))) (define
(expand-question-mark symbol) (let ((chars (symbol-\>string symbol)))
(if (string=? (substring chars 0 1) \"?\") (list '? (string-\>symbol
(substring chars 1 (string-length chars)))) symbol)))
:::

Once the variables are transformed in this way, the variables in a
pattern are lists starting with `?`, and the constant symbols (which
need to be recognized for data-base indexing, [Section
4.4.4.5](#Section 4.4.4.5)) are just the symbols.

::: scheme
(define (var? exp) (tagged-list? exp '?)) (define (constant-symbol? exp)
(symbol? exp))
:::

Unique variables are constructed during rule application (in [Section
4.4.4.4](#Section 4.4.4.4)) by means of the following procedures. The
unique identifier for a rule application is a number, which is
incremented each time a rule is applied.

::: scheme
(define rule-counter 0) (define (new-rule-application-id) (set!
rule-counter (+ 1 rule-counter)) rule-counter) (define
(make-new-variable var rule-application-id) (cons '? (cons
rule-application-id (cdr var))))
:::

When `query/driver/loop` instantiates the query to print the answer, it
converts any unbound pattern variables back to the right form for
printing, using

::: scheme
(define (contract-question-mark variable) (string-\>symbol
(string-append \"?\" (if (number? (cadr variable)) (string-append
(symbol-\>string (caddr variable)) \"-\" (number-\>string (cadr
variable))) (symbol-\>string (cadr variable))))))
:::

#### Frames and Bindings {#Section 4.4.4.8}

Frames are represented as lists of bindings, which are variable-value
pairs:

::: scheme
(define (make-binding variable value) (cons variable value)) (define
(binding-variable binding) (car binding)) (define (binding-value
binding) (cdr binding)) (define (binding-in-frame variable frame) (assoc
variable frame)) (define (extend variable value frame) (cons
(make-binding variable value) frame))
:::

> **[]{#Exercise 4.71 label="Exercise 4.71"}Exercise 4.71:** Louis
> Reasoner wonders why the `simple/query` and `disjoin` procedures
> ([Section 4.4.4.2](#Section 4.4.4.2)) are implemented using explicit
> `delay` operations, rather than being defined as follows:
>
> ::: scheme
> (define (simple-query query-pattern frame-stream) (stream-flatmap
> (lambda (frame) (stream-append (find-assertions query-pattern frame)
> (apply-rules query-pattern frame))) frame-stream)) (define (disjoin
> disjuncts frame-stream) (if (empty-disjunction? disjuncts)
> the-empty-stream (interleave (qeval (first-disjunct disjuncts)
> frame-stream) (disjoin (rest-disjuncts disjuncts) frame-stream))))
> :::
>
> Can you give examples of queries where these simpler definitions would
> lead to undesirable behavior?

> **[]{#Exercise 4.72 label="Exercise 4.72"}Exercise 4.72:** Why do
> `disjoin` and `stream/flatmap` interleave the streams rather than
> simply append them? Give examples that illustrate why interleaving
> works better. (Hint: Why did we use `interleave` in [Section
> 3.5.3](#Section 3.5.3)?)

> **[]{#Exercise 4.73 label="Exercise 4.73"}Exercise 4.73:** Why does
> `flatten/stream` use `delay` explicitly? What would be wrong with
> defining it as follows:
>
> ::: scheme
> (define (flatten-stream stream) (if (stream-null? stream)
> the-empty-stream (interleave (stream-car stream) (flatten-stream
> (stream-cdr stream)))))
> :::

> **[]{#Exercise 4.74 label="Exercise 4.74"}Exercise 4.74:** Alyssa P.
> Hacker proposes to use a simpler version of `stream/flatmap` in
> `negate`, `lisp/value`, and `find/assertions`. She observes that the
> procedure that is mapped over the frame stream in these cases always
> produces either the empty stream or a singleton stream, so no
> interleaving is needed when combining these streams.
>
> a.  Fill in the missing expressions in Alyssa's program.
>
>     ::: scheme
>     (define (simple-stream-flatmap proc s) (simple-flatten (stream-map
>     proc s))) (define (simple-flatten stream) (stream-map
>      $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>     (stream-filter
>      $\color{SchemeDark}\langle$ ?? $\color{SchemeDark}\rangle$
>     stream)))
>     :::
>
> b.  Does the query system's behavior change if we change it in this
>     way?

> **[]{#Exercise 4.75 label="Exercise 4.75"}Exercise 4.75:** Implement
> for the query language a new special form called `unique`. `unique`
> should succeed if there is precisely one item in the data base
> satisfying a specified query. For example,
>
> ::: scheme
> (unique (job ?x (computer wizard)))
> :::
>
> should print the one-item stream
>
> ::: scheme
> (unique (job (Bitdiddle Ben) (computer wizard)))
> :::
>
> since Ben is the only computer wizard, and
>
> ::: scheme
> (unique (job ?x (computer programmer)))
> :::
>
> should print the empty stream, since there is more than one computer
> programmer. Moreover,
>
> ::: scheme
> (and (job ?x ?j) (unique (job ?anyone ?j)))
> :::
>
> should list all the jobs that are filled by only one person, and the
> people who fill them.
>
> There are two parts to implementing `unique`. The first is to write a
> procedure that handles this special form, and the second is to make
> `qeval` dispatch to that procedure. The second part is trivial, since
> `qeval` does its dispatching in a data-directed way. If your procedure
> is called `uniquely/asserted`, all you need to do is
>
> ::: scheme
> (put 'unique 'qeval uniquely-asserted)
> :::
>
> and `qeval` will dispatch to this procedure for every query whose
> `type` (`car`) is the symbol `unique`.
>
> The real problem is to write the procedure `uniquely/asserted`. This
> should take as input the `contents` (`cdr`) of the `unique` query,
> together with a stream of frames. For each frame in the stream, it
> should use `qeval` to find the stream of all extensions to the frame
> that satisfy the given query. Any stream that does not have exactly
> one item in it should be eliminated. The remaining streams should be
> passed back to be accumulated into one big stream that is the result
> of the `unique` query. This is similar to the implementation of the
> `not` special form.
>
> Test your implementation by forming a query that lists all people who
> supervise precisely one person.

> **[]{#Exercise 4.76 label="Exercise 4.76"}Exercise 4.76:** Our
> implementation of `and` as a series combination of queries ([Figure
> 4.5](#Figure 4.5)) is elegant, but it is inefficient because in
> processing the second query of the `and` we must scan the data base
> for each frame produced by the first query. If the data base has $n$
> elements, and a typical query produces a number of output frames
> proportional to $n$ (say $n / k$), then scanning the data base for
> each frame produced by the first query will require $n^2 / k$ calls to
> the pattern matcher. Another approach would be to process the two
> clauses of the `and` separately, then look for all pairs of output
> frames that are compatible. If each query produces $n / k$ output
> frames, then this means that we must perform $n^2 / k^2$ compatibility
> checks---a factor of $k$ fewer than the number of matches required in
> our current method.
>
> Devise an implementation of `and` that uses this strategy. You must
> implement a procedure that takes two frames as inputs, checks whether
> the bindings in the frames are compatible, and, if so, produces a
> frame that merges the two sets of bindings. This operation is similar
> to unification.

> **[]{#Exercise 4.77 label="Exercise 4.77"}Exercise 4.77:** In [Section
> 4.4.3](#Section 4.4.3) we saw that `not` and `lisp/value` can cause
> the query language to give "wrong" answers if these filtering
> operations are applied to frames in which variables are unbound.
> Devise a way to fix this shortcoming. One idea is to perform the
> filtering in a "delayed" manner by appending to the frame a "promise"
> to filter that is fulfilled only when enough variables have been bound
> to make the operation possible. We could wait to perform filtering
> until all other operations have been performed. However, for
> efficiency's sake, we would like to perform filtering as soon as
> possible so as to cut down on the number of intermediate frames
> generated.

> **[]{#Exercise 4.78 label="Exercise 4.78"}Exercise 4.78:** Redesign
> the query language as a nondeterministic program to be implemented
> using the evaluator of [Section 4.3](#Section 4.3), rather than as a
> stream process. In this approach, each query will produce a single
> answer (rather than the stream of all answers) and the user can type
> `try/again` to see more answers. You should find that much of the
> mechanism we built in this section is subsumed by nondeterministic
> search and backtracking. You will probably also find, however, that
> your new query language has subtle differences in behavior from the
> one implemented here. Can you find examples that illustrate this
> difference?

> **[]{#Exercise 4.79 label="Exercise 4.79"}Exercise 4.79:** When we
> implemented the Lisp evaluator in [Section 4.1](#Section 4.1), we saw
> how to use local environments to avoid name conflicts between the
> parameters of procedures. For example, in evaluating
>
> ::: scheme
> (define (square x) (\* x x)) (define (sum-of-squares x y) (+ (square
> x) (square y))) (sum-of-squares 3 4)
> :::
>
> there is no confusion between the `x` in `square` and the `x` in
> `sum/of/squares`, because we evaluate the body of each procedure in an
> environment that is specially constructed to contain bindings for the
> local variables. In the query system, we used a different strategy to
> avoid name conflicts in applying rules. Each time we apply a rule we
> rename the variables with new names that are guaranteed to be unique.
> The analogous strategy for the Lisp evaluator would be to do away with
> local environments and simply rename the variables in the body of a
> procedure each time we apply the procedure.
>
> Implement for the query language a rule-application method that uses
> environments rather than renaming. See if you can build on your
> environment structure to create constructs in the query language for
> dealing with large systems, such as the rule analog of
> block-structured procedures. Can you relate any of this to the problem
> of making deductions in a context (e.g., "If I supposed that $P$ were
> true, then I would be able to deduce $A$ and $B$.") as a method of
> problem solving? (This problem is open-ended. A good answer is
> probably worth a Ph.D.)

# Computing with Register Machines {#Chapter 5}

> My aim is to show that the heavenly machine is not a kind of divine,
> live being, but a kind of clockwork (and he who believes that a clock
> has soul attributes the maker's glory to the work), insofar as nearly
> all the manifold motions are caused by a most simple and material
> force, just as all motions of the clock are caused by a single weight.
>
> ---Johannes Kepler (letter to Herwart von Hohenburg, 1605)

We began this book by studying processes and
by describing processes in terms of procedures written in Lisp. To
explain the meanings of these procedures, we used a succession of models
of evaluation: the substitution model of [Chapter 1](#Chapter 1), the
environment model of [Chapter 3](#Chapter 3), and the metacircular
evaluator of [Chapter 4](#Chapter 4). Our examination of the
metacircular evaluator, in particular, dispelled much of the mystery of
how Lisp-like languages are interpreted. But even the metacircular
evaluator leaves important questions unanswered, because it fails to
elucidate the mechanisms of control in a Lisp system. For instance, the
evaluator does not explain how the evaluation of a subexpression manages
to return a value to the expression that uses this value, nor does the
evaluator explain how some recursive procedures generate iterative
processes (that is, are evaluated using constant space) whereas other
recursive procedures generate recursive processes. These questions
remain unanswered because the metacircular evaluator is itself a Lisp
program and hence inherits the control structure of the underlying Lisp
system. In order to provide a more complete description of the control
structure of the Lisp evaluator, we must work at a more primitive level
than Lisp itself.

In this chapter we will describe processes in terms of the step-by-step
operation of a traditional computer. Such a computer, or *register
machine*, sequentially executes *instructions* that manipulate the
contents of a fixed set of storage elements called *registers*. A
typical register-machine instruction applies a primitive operation to
the contents of some registers and assigns the result to another
register. Our descriptions of processes executed by register machines
will look very much like "machine-language" programs for traditional
computers. However, instead of focusing on the machine language of any
particular computer, we will examine several Lisp procedures and design
a specific register machine to execute each procedure. Thus, we will
approach our task from the perspective of a hardware architect rather
than that of a machine-language computer programmer. In designing
register machines, we will develop mechanisms for implementing important
programming constructs such as recursion. We will also present a
language for describing designs for register machines. In [Section
5.2](#Section 5.2) we will implement a Lisp program that uses these
descriptions to simulate the machines we design.

Most of the primitive operations of our register machines are very
simple. For example, an operation might add the numbers fetched from two
registers, producing a result to be stored into a third register. Such
an operation can be performed by easily described hardware. In order to
deal with list structure, however, we will also use the memory
operations `car`, `cdr`, and `cons`, which require an elaborate
storage-allocation mechanism. In [Section 5.3](#Section 5.3) we study
their implementation in terms of more elementary operations.

In [Section 5.4](#Section 5.4), after we have accumulated experience
formulating simple procedures as register machines, we will design a
machine that carries out the algorithm described by the metacircular
evaluator of [Section 4.1](#Section 4.1). This will fill in the gap in
our understanding of how Scheme expressions are interpreted, by
providing an explicit model for the mechanisms of control in the
evaluator. In [Section 5.5](#Section 5.5) we will study a simple
compiler that translates Scheme programs into sequences of instructions
that can be executed directly with the registers and operations of the
evaluator register machine.

## Designing Register Machines {#Section 5.1}

To design a register machine, we must design its *data paths* (registers
and operations) and the *controller* that sequences these operations. To
illustrate the design of a simple register machine, let us examine
Euclid's Algorithm, which is used to compute the greatest common divisor
(gcd) of two integers. As we saw in [Section
1.2.5](#Section 1.2.5), Euclid's Algorithm can be carried out by an
iterative process, as specified by the following procedure:

::: scheme
(define (gcd a b) (if (= b 0) a (gcd b (remainder a b))))
:::

A machine to carry out this algorithm must keep track of two numbers,
$a$ and $b$, so let us assume that these numbers are stored in two
registers with those names. The basic operations required are testing
whether the contents of register `b` is zero and computing the remainder
of the contents of register `a` divided by the contents of register `b`.
The remainder operation is a complex process, but assume for the moment
that we have a primitive device that computes remainders. On each cycle
of the gcd algorithm, the contents of register `a` must be
replaced by the contents of register `b`, and the contents of `b` must
be replaced by the remainder of the old contents of `a` divided by the
old contents of `b`. It would be convenient if these replacements could
be done simultaneously, but in our model of register machines we will
assume that only one register can be assigned a new value at each step.
To accomplish the replacements, our machine will use a third "temporary"
register, which we call `t`. (First the remainder will be placed in `t`,
then the contents of `b` will be placed in `a`, and finally the
remainder stored in `t` will be placed in `b`.)

We can illustrate the registers and operations required for this machine
by using the data-path diagram shown in [Figure 5.1](#Figure 5.1). In
this diagram, the registers (`a`, `b`, and `t`) are represented by
rectangles. Each way to assign a value to a register is indicated by an
arrow with an `X` behind the head, pointing from the source of data to
the register. We can think of the `X` as a button that, when pushed,
allows the value at the source to "flow" into the designated register.
The label next to each button is the name we will use to refer to the
button. The names are arbitrary, and can be chosen to have mnemonic
value (for example, `a←b` denotes pushing the button that assigns the
contents of register `b` to register `a`). The source of data for a
register can be another register (as in the `a←b` assignment), an
operation result (as in the `t←r` assignment), or a constant (a built-in
value that cannot be changed, represented in a data-path diagram by a
triangle containing the constant).

[]{#Figure 5.1 label="Figure 5.1"}

![image](fig/chap5/Fig5.1a.pdf){width="58mm"}

**Figure 5.1:** Data paths for a gcd machine.

An operation that computes a value from constants and the contents of
registers is represented in a data-path diagram by a trapezoid
containing a name for the operation. For example, the box marked `rem`
in [Figure 5.1](#Figure 5.1) represents an operation that computes the
remainder of the contents of the registers `a` and `b` to which it is
attached. Arrows (without buttons) point from the input registers and
constants to the box, and arrows connect the operation's output value to
registers. A test is represented by a circle containing a name for the
test. For example, our gcd machine has an operation that
tests whether the contents of register `b` is zero. A test also has
arrows from its input registers and constants, but it has no output
arrows; its value is used by the controller rather than by the data
paths. Overall, the data-path diagram shows the registers and operations
that are required for the machine and how they must be connected. If we
view the arrows as wires and the `X` buttons as switches, the data-path
diagram is very like the wiring diagram for a machine that could be
constructed from electrical components.

[]{#Figure 5.2 label="Figure 5.2"}

![image](fig/chap5/Fig5.2.pdf){width="41mm"}

**Figure 5.2:** Controller for a gcd machine.

In order for the data paths to actually compute gcds, the
buttons must be pushed in the correct sequence. We will describe this
sequence in terms of a controller diagram, as illustrated in [Figure
5.2](#Figure 5.2). The elements of the controller diagram indicate how
the data-path components should be operated. The rectangular boxes in
the controller diagram identify data-path buttons to be pushed, and the
arrows describe the sequencing from one step to the next. The diamond in
the diagram represents a decision. One of the two sequencing arrows will
be followed, depending on the value of the data-path test identified in
the diamond. We can interpret the controller in terms of a physical
analogy: Think of the diagram as a maze in which a marble is rolling.
When the marble rolls into a box, it pushes the data-path button that is
named by the box. When the marble rolls into a decision node (such as
the test for `b` = 0), it leaves the node on the path determined by the
result of the indicated test. Taken together, the data paths and the
controller completely describe a machine for computing
gcds. We start the controller (the rolling marble) at the
place marked `start`, after placing numbers in registers `a` and `b`.
When the controller reaches `done`, we will find the value of the
gcd in register `a`.

> **[]{#Exercise 5.1 label="Exercise 5.1"}Exercise 5.1:** Design a
> register machine to compute factorials using the iterative algorithm
> specified by the following procedure. Draw data-path and controller
> diagrams for this machine.
>
> ::: scheme
> (define (factorial n) (define (iter product counter) (if (\> counter
> n) product (iter (\* counter product) (+ counter 1)))) (iter 1 1))
> :::

### A Language for Describing Register Machines {#Section 5.1.1}

Data-path and controller diagrams are adequate for representing simple
machines such as gcd, but they are unwieldy for describing
large machines such as a Lisp interpreter. To make it possible to deal
with complex machines, we will create a language that presents, in
textual form, all the information given by the data-path and controller
diagrams. We will start with a notation that directly mirrors the
diagrams.

We define the data paths of a machine by describing the registers and
the operations. To describe a register, we give it a name and specify
the buttons that control assignment to it. We give each of these buttons
a name and specify the source of the data that enters the register under
the button's control. (The source is a register, a constant, or an
operation.) To describe an operation, we give it a name and specify its
inputs (registers or constants).

We define the controller of a machine as a sequence of *instructions*
together with *labels* that identify *entry points* in the sequence. An
instruction is one of the following:

-   The name of a data-path button to push to assign a value to a
    register. (This corresponds to a box in the controller diagram.)

-   A `test` instruction, that performs a specified test.

-   A conditional branch (`branch` instruction) to a location indicated
    by a controller label, based on the result of the previous test.
    (The test and branch together correspond to a diamond in the
    controller diagram.) If the test is false, the controller should
    continue with the next instruction in the sequence. Otherwise, the
    controller should continue with the instruction after the label.

-   An unconditional branch (`goto` instruction) naming a controller
    label at which to continue execution.

The machine starts at the beginning of the controller instruction
sequence and stops when execution reaches the end of the sequence.
Except when a branch changes the flow of control, instructions are
executed in the order in which they are listed.

> **[]{#Figure 5.3 label="Figure 5.3"}Figure 5.3:** $\downarrow$ A
> specification of the gcd machine.
>
> ::: scheme
> (data-paths (registers ((name a) (buttons ((name a\<-b) (source
> (register b))))) ((name b) (buttons ((name b\<-t) (source (register
> t))))) ((name t) (buttons ((name t\<-r) (source (operation rem))))))
> (operations ((name rem) (inputs (register a) (register b))) ((name =)
> (inputs (register b) (constant 0))))) (controller test-b  [;
> label]{.roman}  (test =)  [; test]{.roman}  (branch (label gcd-done))
>  [; conditional branch]{.roman}  (t\<-r)  [; button push]{.roman}
> (a\<-b)  [; button push]{.roman}  (b\<-t)  [; button push]{.roman}
> (goto (label test-b))  [; unconditional branch]{.roman}  gcd-done)  [;
> label]{.roman}
> :::

[Figure 5.3](#Figure 5.3) shows the gcd machine described
in this way. This example only hints at the generality of these
descriptions, since the gcd machine is a very simple case:
Each register has only one button, and each button and test is used only
once in the controller.

Unfortunately, it is difficult to read such a description. In order to
understand the controller instructions we must constantly refer back to
the definitions of the button names and the operation names, and to
understand what the buttons do we may have to refer to the definitions
of the operation names. We will thus transform our notation to combine
the information from the data-path and controller descriptions so that
we see it all together.

To obtain this form of description, we will replace the arbitrary button
and operation names by the definitions of their behavior. That is,
instead of saying (in the controller) "Push button `t←r`" and separately
saying (in the data paths) "Button `t←r` assigns the value of the `rem`
operation to register `t`" and "The `rem` operation's inputs are the
contents of registers `a` and `b`," we will say (in the controller)
"Push the button that assigns to register `t` the value of the `rem`
operation on the contents of registers `a` and `b`." Similarly, instead
of saying (in the controller) "Perform the `=` test" and separately
saying (in the data paths) "The `=` test operates on the contents of
register `b` and the constant 0," we will say "Perform the `=` test on
the contents of register `b` and the constant 0." We will omit the
data-path description, leaving only the controller sequence. Thus, the
gcd machine is described as follows:

::: scheme
(controller test-b (test (op =) (reg b) (const 0)) (branch (label
gcd-done)) (assign t (op rem) (reg a) (reg b)) (assign a (reg b))
(assign b (reg t)) (goto (label test-b)) gcd-done)
:::

This form of description is easier to read than the kind illustrated in
[Figure 5.3](#Figure 5.3), but it also has disadvantages:

-   It is more verbose for large machines, because complete descriptions
    of the data-path elements are repeated whenever the elements are
    mentioned in the controller instruction sequence. (This is not a
    problem in the gcd example, because each operation and
    button is used only once.) Moreover, repeating the data-path
    descriptions obscures the actual data-path structure of the machine;
    it is not obvious for a large machine how many registers,
    operations, and buttons there are and how they are interconnected.

-   Because the controller instructions in a machine definition look
    like Lisp expressions, it is easy to forget that they are not
    arbitrary Lisp expressions. They can notate only legal machine
    operations. For example, operations can operate directly only on
    constants and the contents of registers, not on the results of other
    operations.

In spite of these disadvantages, we will use this register-machine
language throughout this chapter, because we will be more concerned with
understanding controllers than with understanding the elements and
connections in data paths. We should keep in mind, however, that
data-path design is crucial in designing real machines.

> **[]{#Exercise 5.2 label="Exercise 5.2"}Exercise 5.2:** Use the
> register-machine language to describe the iterative factorial machine
> of [Exercise 5.1](#Exercise 5.1).

#### Actions {#actions .unnumbered}

Let us modify the gcd machine so that we can type in the
numbers whose gcd we want and get the answer printed at
our terminal. We will not discuss how to make a machine that can read
and print, but will assume (as we do when we use `read` and `display` in
Scheme) that they are available as primitive operations.[^286]

[]{#Figure 5.4 label="Figure 5.4"}

![image](fig/chap5/Fig5.4b.pdf){width="107mm"}

**Figure 5.4:** A gcd machine that reads inputs and prints
results.

`read` is like the operations we have been using in that it produces a
value that can be stored in a register. But `read` does not take inputs
from any registers; its value depends on something that happens outside
the parts of the machine we are designing. We will allow our machine's
operations to have such behavior, and thus will draw and notate the use
of `read` just as we do any other operation that computes a value.

`print`, on the other hand, differs from the operations we have been
using in a fundamental way: It does not produce an output value to be
stored in a register. Though it has an effect, this effect is not on a
part of the machine we are designing. We will refer to this kind of
operation as an *action*. We will represent an action in a data-path
diagram just as we represent an operation that computes a value---as a
trapezoid that contains the name of the action. Arrows point to the
action box from any inputs (registers or constants). We also associate a
button with the action. Pushing the button makes the action happen. To
make a controller push an action button we use a new kind of instruction
called `perform`. Thus, the action of printing the contents of register
`a` is represented in a controller sequence by the instruction

::: scheme
(perform (op print) (reg a))
:::

[Figure 5.4](#Figure 5.4) shows the data paths and controller for the
new gcd machine. Instead of having the machine stop after
printing the answer, we have made it start over, so that it repeatedly
reads a pair of numbers, computes their gcd, and prints
the result. This structure is like the driver loops we used in the
interpreters of [Chapter 4](#Chapter 4).

### Abstraction in Machine Design {#Section 5.1.2}

We will often define a machine to include "primitive" operations that
are actually very complex. For example, in [Section 5.4](#Section 5.4)
and [Section 5.5](#Section 5.5) we will treat Scheme's environment
manipulations as primitive. Such abstraction is valuable because it
allows us to ignore the details of parts of a machine so that we can
concentrate on other aspects of the design. The fact that we have swept
a lot of complexity under the rug, however, does not mean that a machine
design is unrealistic. We can always replace the complex "primitives" by
simpler primitive operations.

Consider the gcd machine. The machine has an instruction
that computes the remainder of the contents of registers `a` and `b` and
assigns the result to register `t`. If we want to construct the
gcd machine without using a primitive remainder operation,
we must specify how to compute remainders in terms of simpler
operations, such as subtraction. Indeed, we can write a Scheme procedure
that finds remainders in this way:

::: scheme
(define (remainder n d) (if (\< n d) n (remainder (- n d) d)))
:::

[]{#Figure 5.5 label="Figure 5.5"}

![image](fig/chap5/Fig5.5a.pdf){width="67mm"}

> **Figure 5.5:** Data paths and controller for the elaborated
> gcd machine.

We can thus replace the remainder operation in the gcd
machine's data paths with a subtraction operation and a comparison test.
[Figure 5.5](#Figure 5.5) shows the data paths and controller for the
elaborated machine. The instruction

::: scheme
(assign t (op rem) (reg a) (reg b))
:::

in the gcd controller definition is replaced by a sequence
of instructions that contains a loop, as shown in [Figure
5.6](#Figure 5.6).

> **[]{#Figure 5.6 label="Figure 5.6"}Figure 5.6:** $\downarrow$
> Controller instruction sequence for the gcd machine in
> [Figure 5.5](#Figure 5.5).
>
> ::: scheme
> (controller test-b (test (op =) (reg b) (const 0)) (branch (label
> gcd-done)) (assign t (reg a)) rem-loop (test (op \<) (reg t) (reg b))
> (branch (label rem-done)) (assign t (op -) (reg t) (reg b)) (goto
> (label rem-loop)) rem-done (assign a (reg b)) (assign b (reg t)) (goto
> (label test-b)) gcd-done)
> :::

> **[]{#Exercise 5.3 label="Exercise 5.3"}Exercise 5.3:** Design a
> machine to compute square roots using Newton's method, as described in
> [Section 1.1.7](#Section 1.1.7):
>
> ::: scheme
> (define (sqrt x) (define (good-enough? guess) (\< (abs (- (square
> guess) x)) 0.001)) (define (improve guess) (average guess (/ x
> guess))) (define (sqrt-iter guess) (if (good-enough? guess) guess
> (sqrt-iter (improve guess)))) (sqrt-iter 1.0))
> :::
>
> Begin by assuming that `good/enough?` and `improve` operations are
> available as primitives. Then show how to expand these in terms of
> arithmetic operations. Describe each version of the `sqrt` machine
> design by drawing a data-path diagram and writing a controller
> definition in the register-machine language.

### Subroutines {#Section 5.1.3}

When designing a machine to perform a computation, we would often prefer
to arrange for components to be shared by different parts of the
computation rather than duplicate the components. Consider a machine
that includes two gcd computations---one that finds the
gcd of the contents of registers `a` and `b` and one that
finds the gcd of the contents of registers `c` and `d`. We
might start by assuming we have a primitive `gcd` operation, then expand
the two instances of `gcd` in terms of more primitive operations.
[Figure 5.7](#Figure 5.7) shows just the gcd portions of
the resulting machine's data paths, without showing how they connect to
the rest of the machine. The figure also shows the corresponding
portions of the machine's controller sequence.

[]{#Figure 5.7 label="Figure 5.7"}

![image](fig/chap5/Fig5.7b.pdf){width="105mm"}

> **Figure 5.7:** Portions of the data paths and controller sequence for
> a machine with two gcd computations.

This machine has two remainder operation boxes and two boxes for testing
equality. If the duplicated components are complicated, as is the
remainder box, this will not be an economical way to build the machine.
We can avoid duplicating the data-path components by using the same
components for both gcd computations, provided that doing
so will not affect the rest of the larger machine's computation. If the
values in registers `a` and `b` are not needed by the time the
controller gets to `gcd/2` (or if these values can be moved to other
registers for safekeeping), we can change the machine so that it uses
registers `a` and `b`, rather than registers `c` and `d`, in computing
the second gcd as well as the first. If we do this, we
obtain the controller sequence shown in [Figure 5.8](#Figure 5.8).

> **[]{#Figure 5.8 label="Figure 5.8"}Figure 5.8:** $\downarrow$
> Portions of the controller sequence for a machine that uses the same
> data-path components for two different gcd computations.
>
> ::: scheme
> gcd-1 (test (op =) (reg b) (const 0)) (branch (label after-gcd-1))
> (assign t (op rem) (reg a) (reg b)) (assign a (reg b)) (assign b (reg
> t)) (goto (label gcd-1)) after-gcd-1  $\dots$  gcd-2 (test (op =) (reg
> b) (const 0)) (branch (label after-gcd-2)) (assign t (op rem) (reg a)
> (reg b)) (assign a (reg b)) (assign b (reg t)) (goto (label gcd-2))
> after-gcd-2
> :::

We have removed the duplicate data-path components (so that the data
paths are again as in [Figure 5.1](#Figure 5.1)), but the controller now
has two gcd sequences that differ only in their
entry-point labels. It would be better to replace these two sequences by
branches to a single sequence---a `gcd` *subroutine*---at the end of
which we branch back to the correct place in the main instruction
sequence. We can accomplish this as follows: Before branching to `gcd`,
we place a distinguishing value (such as 0 or 1) into a special
register, `continue`. At the end of the `gcd` subroutine we return
either to `after/gcd/1` or to `after/gcd/2`, depending on the value of
the `continue` register. [Figure 5.9](#Figure 5.9) shows the relevant
portion of the resulting controller sequence, which includes only a
single copy of the `gcd` instructions.

> **[]{#Figure 5.9 label="Figure 5.9"}Figure 5.9:** $\downarrow$ Using a
> `continue` register to avoid the duplicate controller sequence in
> [Figure 5.8](#Figure 5.8).
>
> ::: scheme
> gcd (test (op =) (reg b) (const 0)) (branch (label gcd-done)) (assign
> t (op rem) (reg a) (reg b)) (assign a (reg b)) (assign b (reg t))
> (goto (label gcd)) gcd-done (test (op =) (reg continue) (const 0))
> (branch (label after-gcd-1)) (goto (label after-gcd-2))  $\dots$   [;;
> Before branching to `gcd` from the first place where]{.roman}   [;; it
> is needed, we place 0 in the `continue` register]{.roman}  (assign
> continue (const 0)) (goto (label gcd)) after-gcd-1  $\dots$
>
>  [;; Before the second use of `gcd`, we place 1]{.roman}   [;; in the
> `continue` register]{.roman}  (assign continue (const 1)) (goto (label
> gcd)) after-gcd-2
> :::

This is a reasonable approach for handling small problems, but it would
be awkward if there were many instances of gcd
computations in the controller sequence. To decide where to continue
executing after the `gcd` subroutine, we would need tests in the data
paths and branch instructions in the controller for all the places that
use `gcd`. A more powerful method for implementing subroutines is to
have the `continue` register hold the label of the entry point in the
controller sequence at which execution should continue when the
subroutine is finished. Implementing this strategy requires a new kind
of connection between the data paths and the controller of a register
machine: There must be a way to assign to a register a label in the
controller sequence in such a way that this value can be fetched from
the register and used to continue execution at the designated entry
point.

To reflect this ability, we will extend the `assign` instruction of the
register-machine language to allow a register to be assigned as value a
label from the controller sequence (as a special kind of constant). We
will also extend the `goto` instruction to allow execution to continue
at the entry point described by the contents of a register rather than
only at an entry point described by a constant label. Using these new
constructs we can terminate the `gcd` subroutine with a branch to the
location stored in the `continue` register. This leads to the controller
sequence shown in [Figure 5.10](#Figure 5.10).

> **[]{#Figure 5.10 label="Figure 5.10"}Figure 5.10:** $\downarrow$
> Assigning labels to the `continue` register simplifies and generalizes
> the strategy shown in [Figure 5.9](#Figure 5.9).
>
> ::: scheme
> gcd (test (op =) (reg b) (const 0)) (branch (label gcd-done)) (assign
> t (op rem) (reg a) (reg b)) (assign a (reg b)) (assign b (reg t))
> (goto (label gcd)) gcd-done (goto (reg continue))  $\dots$   [;;
> Before calling `gcd`, we assign to `continue`]{.roman}   [;; the label
> to which `gcd` should return.]{.roman}  (assign continue (label
> after-gcd-1)) (goto (label gcd)) after-gcd-1  $\dots$   [;; Here is
> the second call to `gcd`,]{.roman}   [;; with a different
> continuation.]{.roman}  (assign continue (label after-gcd-2)) (goto
> (label gcd)) after-gcd-2
> :::

A machine with more than one subroutine could use multiple continuation
registers (e.g., `gcd/continue`, `factorial/continue`) or we could have
all subroutines share a single `continue` register. Sharing is more
economical, but we must be careful if we have a subroutine (`sub1`) that
calls another subroutine (`sub2`). Unless `sub1` saves the contents of
`continue` in some other register before setting up `continue` for the
call to `sub2`, `sub1` will not know where to go when it is finished.
The mechanism developed in the next section to handle recursion also
provides a better solution to this problem of nested subroutine calls.

### Using a Stack to Implement Recursion {#Section 5.1.4}

With the ideas illustrated so far, we can implement any iterative
process by specifying a register machine that has a register
corresponding to each state variable of the process. The machine
repeatedly executes a controller loop, changing the contents of the
registers, until some termination condition is satisfied. At each point
in the controller sequence, the state of the machine (representing the
state of the iterative process) is completely determined by the contents
of the registers (the values of the state variables).

Implementing recursive processes, however, requires an additional
mechanism. Consider the following recursive method for computing
factorials, which we first examined in [Section 1.2.1](#Section 1.2.1):

::: scheme
(define (factorial n) (if (= n 1) 1 (\* (factorial (- n 1)) n)))
:::

As we see from the procedure, computing $n!$ requires computing
$(n - 1)!$. Our gcd machine, modeled on the procedure

::: scheme
(define (gcd a b) (if (= b 0) a (gcd b (remainder a b))))
:::

similarly had to compute another gcd. But there is an
important difference between the `gcd` procedure, which reduces the
original computation to a new gcd computation, and
`factorial`, which requires computing another factorial as a subproblem.
In gcd, the answer to the new gcd
computation is the answer to the original problem. To compute the next
gcd, we simply place the new arguments in the input
registers of the gcd machine and reuse the machine's data
paths by executing the same controller sequence. When the machine is
finished solving the final gcd problem, it has completed
the entire computation.

In the case of factorial (or any recursive process) the answer to the
new factorial subproblem is not the answer to the original problem. The
value obtained for $(n - 1)!$ must be multiplied by $n$ to get the final
answer. If we try to imitate the gcd design, and solve the
factorial subproblem by decrementing the `n` register and rerunning the
factorial machine, we will no longer have available the old value of `n`
by which to multiply the result. We thus need a second factorial machine
to work on the subproblem. This second factorial computation itself has
a factorial subproblem, which requires a third factorial machine, and so
on. Since each factorial machine contains another factorial machine
within it, the total machine contains an infinite nest of similar
machines and hence cannot be constructed from a fixed, finite number of
parts.

Nevertheless, we can implement the factorial process as a register
machine if we can arrange to use the same components for each nested
instance of the machine. Specifically, the machine that computes $n!$
should use the same components to work on the subproblem of computing
$(n - 1)!$, on the subproblem for $(n - 2)!$, and so on. This is
plausible because, although the factorial process dictates that an
unbounded number of copies of the same machine are needed to perform a
computation, only one of these copies needs to be active at any given
time. When the machine encounters a recursive subproblem, it can suspend
work on the main problem, reuse the same physical parts to work on the
subproblem, then continue the suspended computation.

In the subproblem, the contents of the registers will be different than
they were in the main problem. (In this case the `n` register is
decremented.) In order to be able to continue the suspended computation,
the machine must save the contents of any registers that will be needed
after the subproblem is solved so that these can be restored to continue
the suspended computation. In the case of factorial, we will save the
old value of `n`, to be restored when we are finished computing the
factorial of the decremented `n` register.[^287]

Since there is no *a priori* limit on the depth of nested recursive
calls, we may need to save an arbitrary number of register values. These
values must be restored in the reverse of the order in which they were
saved, since in a nest of recursions the last subproblem to be entered
is the first to be finished. This dictates the use of a *stack*, or
"last in, first out" data structure, to save register values. We can
extend the register-machine language to include a stack by adding two
kinds of instructions: Values are placed on the stack using a `save`
instruction and restored from the stack using a `restore` instruction.
After a sequence of values has been `save`d on the stack, a sequence of
`restore`s will retrieve these values in reverse order.[^288]

With the aid of the stack, we can reuse a single copy of the factorial
machine's data paths for each factorial subproblem. There is a similar
design issue in reusing the controller sequence that operates the data
paths. To reexecute the factorial computation, the controller cannot
simply loop back to the beginning, as with an iterative process, because
after solving the $(n - 1)!$ subproblem the machine must still multiply
the result by $n$. The controller must suspend its computation of $n!$,
solve the $(n - 1)!$ subproblem, then continue its computation of $n!$.
This view of the factorial computation suggests the use of the
subroutine mechanism described in [Section 5.1.3](#Section 5.1.3), which
has the controller use a `continue` register to transfer to the part of
the sequence that solves a subproblem and then continue where it left
off on the main problem. We can thus make a factorial subroutine that
returns to the entry point stored in the `continue` register. Around
each subroutine call, we save and restore `continue` just as we do the
`n` register, since each "level" of the factorial computation will use
the same `continue` register. That is, the factorial subroutine must put
a new value in `continue` when it calls itself for a subproblem, but it
will need the old value in order to return to the place that called it
to solve a subproblem.

[Figure 5.11](#Figure 5.11) shows the data paths and controller for a
machine that implements the recursive `factorial` procedure. The machine
has a stack and three registers, called `n`, `val`, and `continue`. To
simplify the data-path diagram, we have not named the
register-assignment buttons, only the stack-operation buttons (`sc` and
`sn` to save registers, `rc` and `rn` to restore registers). To operate
the machine, we put in register `n` the number whose factorial we wish
to compute and start the machine. When the machine reaches `fact/done`,
the computation is finished and the answer will be found in the `val`
register. In the controller sequence, `n` and `continue` are saved
before each recursive call and restored upon return from the call.
Returning from a call is accomplished by branching to the location
stored in `continue`. `continue` is initialized when the machine starts
so that the last return will go to `fact/done`. The `val` register,
which holds the result of the factorial computation, is not saved before
the recursive call, because the old contents of `val` is not useful
after the subroutine returns. Only the new value, which is the value
produced by the subcomputation, is needed.

[]{#Figure 5.11 label="Figure 5.11"}

![image](fig/chap5/Fig5.11a.pdf){width="106mm"}

**Figure 5.11:** A recursive factorial machine.

Although in principle the factorial computation requires an infinite
machine, the machine in [Figure 5.11](#Figure 5.11) is actually finite
except for the stack, which is potentially unbounded. Any particular
physical implementation of a stack, however, will be of finite size, and
this will limit the depth of recursive calls that can be handled by the
machine. This implementation of factorial illustrates the general
strategy for realizing recursive algorithms as ordinary register
machines augmented by stacks. When a recursive subproblem is
encountered, we save on the stack the registers whose current values
will be required after the subproblem is solved, solve the recursive
subproblem, then restore the saved registers and continue execution on
the main problem. The `continue` register must always be saved. Whether
there are other registers that need to be saved depends on the
particular machine, since not all recursive computations need the
original values of registers that are modified during solution of the
subproblem (see [Exercise 5.4](#Exercise 5.4)).

#### A double recursion {#a-double-recursion .unnumbered}

Let us examine a more complex recursive process, the tree-recursive
computation of the Fibonacci numbers, which we introduced in [Section
1.2.2](#Section 1.2.2):

::: scheme
(define (fib n) (if (\< n 2) n (+ (fib (- n 1)) (fib (- n 2)))))
:::

Just as with factorial, we can implement the recursive Fibonacci
computation as a register machine with registers `n`, `val`, and
`continue`. The machine is more complex than the one for factorial,
because there are two places in the controller sequence where we need to
perform recursive calls---once to compute ${\rm Fib}(n - 1)$ and once to
compute ${\rm Fib}(n - 2)$. To set up for each of these calls, we save
the registers whose values will be needed later, set the `n` register to
the number whose Fib we need to compute recursively ($n - 1$ or
$n - 2$), and assign to `continue` the entry point in the main sequence
to which to return (`afterfib/n/1` or `afterfib/n/2`, respectively). We
then go to `fib/loop`. When we return from the recursive call, the
answer is in `val`. [Figure 5.12](#Figure 5.12) shows the controller
sequence for this machine.

> **[]{#Figure 5.12 label="Figure 5.12"}Figure 5.12:** $\downarrow$
> Controller for a machine to compute Fibonacci numbers.
>
> ::: scheme
> (controller (assign continue (label fib-done)) fib-loop (test (op \<)
> (reg n) (const 2)) (branch (label immediate-answer))  [;; set up to
> compute Fib$(n-1)$]{.roman}  (save continue) (assign continue (label
> afterfib-n-1)) (save n)  [; save old value of `n`]{.roman}  (assign n
> (op -) (reg n) (const 1))  [; clobber `n` to `n/1`]{.roman}  (goto
> (label fib-loop))  [; perform recursive call]{.roman}  afterfib-n-1
>  [; upon return, `val` contains Fib$(n-1)$]{.roman}  (restore n)
> (restore continue)  [;; set up to compute Fib$(n - 2)$]{.roman}
> (assign n (op -) (reg n) (const 2)) (save continue) (assign continue
> (label afterfib-n-2)) (save val)  [; save Fib$(n-1)$]{.roman}  (goto
> (label fib-loop)) afterfib-n-2  [; upon return, `val` contains
> Fib$(n-2)$]{.roman}  (assign n (reg val))  [; `n` now contains
> Fib$(n-2)$]{.roman}  (restore val)  [; `val` now contains
> Fib$(n-1)$]{.roman}  (restore continue) (assign val  [; Fib$(n-1)$ +
> Fib$(n-2)$]{.roman}  (op +) (reg val) (reg n)) (goto (reg continue))
>  [; return to caller, answer is in `val`]{.roman}  immediate-answer
> (assign val (reg n))  [; base case: Fib$(n) = n$]{.roman}  (goto (reg
> continue)) fib-done)
> :::

> **[]{#Exercise 5.4 label="Exercise 5.4"}Exercise 5.4:** Specify
> register machines that implement each of the following procedures. For
> each machine, write a controller instruction sequence and draw a
> diagram showing the data paths.
>
> a.  Recursive exponentiation:
>
>     ::: scheme
>     (define (expt b n) (if (= n 0) 1 (\* b (expt b (- n 1)))))
>     :::
>
> b.  Iterative exponentiation:
>
>     ::: scheme
>     (define (expt b n) (define (expt-iter counter product) (if (=
>     counter 0) product (expt-iter (- counter 1) (\* b product))))
>     (expt-iter n 1))
>     :::

> **[]{#Exercise 5.5 label="Exercise 5.5"}Exercise 5.5:** Hand-simulate
> the factorial and Fibonacci machines, using some nontrivial input
> (requiring execution of at least one recursive call). Show the
> contents of the stack at each significant point in the execution.

> **[]{#Exercise 5.6 label="Exercise 5.6"}Exercise 5.6:** Ben Bitdiddle
> observes that the Fibonacci machine's controller sequence has an extra
> `save` and an extra `restore`, which can be removed to make a faster
> machine. Where are these instructions?

### Instruction Summary {#Section 5.1.5}

A controller instruction in our register-machine language has one of the
following forms, where each $\langle$*input*$_i\rangle$ is either
`(reg `$\langle$*`register/name`*$\rangle$`)` or
`(const `$\langle$*`constant/value`*$\rangle$`)`. These instructions
were introduced in [Section 5.1.1](#Section 5.1.1):

::: scheme
(assign
 $\color{SchemeDark}\langle$  *register-name*  $\color{SchemeDark}\rangle$
(reg
 $\color{SchemeDark}\langle$  *register-name*  $\color{SchemeDark}\rangle$ ))
(assign
 $\color{SchemeDark}\langle$  *register-name*  $\color{SchemeDark}\rangle$
(const
 $\color{SchemeDark}\langle$  *constant-value*  $\color{SchemeDark}\rangle$ ))
(assign
 $\color{SchemeDark}\langle$  *register-name*  $\color{SchemeDark}\rangle$
(op
 $\color{SchemeDark}\langle$  *operation-name*  $\color{SchemeDark}\rangle$ )
 $\color{SchemeDark}\langle$  *input*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *input*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
(perform (op
 $\color{SchemeDark}\langle$  *operation-name*  $\color{SchemeDark}\rangle$ )
 $\color{SchemeDark}\langle$  *input*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *input*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
(test (op
 $\color{SchemeDark}\langle$  *operation-name*  $\color{SchemeDark}\rangle$ )
 $\color{SchemeDark}\langle$  *input*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\dots$
 $\color{SchemeDark}\langle$  *input*  $\color{SchemeDark}_{\hbox{\ttfamily\itshape\scriptsize n}}\rangle$ )
(branch (label
 $\color{SchemeDark}\langle$  *label-name*  $\color{SchemeDark}\rangle$ ))
(goto (label
 $\color{SchemeDark}\langle$  *label-name*  $\color{SchemeDark}\rangle$ ))
:::

The use of registers to hold labels was introduced in [Section
5.1.3](#Section 5.1.3):

::: scheme
(assign
 $\color{SchemeDark}\langle$  *register-name*  $\color{SchemeDark}\rangle$
(label
 $\color{SchemeDark}\langle$  *label-name*  $\color{SchemeDark}\rangle$ ))
(goto (reg
 $\color{SchemeDark}\langle$  *register-name*  $\color{SchemeDark}\rangle$ ))
:::

Instructions to use the stack were introduced in [Section
5.1.4](#Section 5.1.4):

::: scheme
(save
 $\color{SchemeDark}\langle$  *register-name*  $\color{SchemeDark}\rangle$ )
(restore
 $\color{SchemeDark}\langle$  *register-name*  $\color{SchemeDark}\rangle$ )
:::

The only kind of $\langle$*constant-value*$\rangle$ we have seen so far
is a number, but later we will use strings, symbols, and lists. For
example,

::: scheme
(const \"abc\")  [is the string]{.roman}  \"abc\", (const abc)  [is the
symbol]{.roman}  abc, (const (a b c))  [is the list]{.roman}  (a b c),
 [and]{.roman}  (const ())  [is the empty list.]{.roman}
:::

## A Register-Machine Simulator {#Section 5.2}

In order to gain a good understanding of the design of register
machines, we must test the machines we design to see if they perform as
expected. One way to test a design is to hand-simulate the operation of
the controller, as in [Exercise 5.5](#Exercise 5.5). But this is
extremely tedious for all but the simplest machines. In this section we
construct a simulator for machines described in the register-machine
language. The simulator is a Scheme program with four interface
procedures. The first uses a description of a register machine to
construct a model of the machine (a data structure whose parts
correspond to the parts of the machine to be simulated), and the other
three allow us to simulate the machine by manipulating the model:

> ::: scheme
> (make-machine
>  $\color{SchemeDark}\langle$  *register-names*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *operations*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *controller*  $\color{SchemeDark}\rangle$ )
> :::
>
> constructs and returns a model of the machine with the given
> registers, operations, and controller.
>
> ::: scheme
> (set-register-contents!
>  $\color{SchemeDark}\langle\kern0.08em$  *machine-model*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *register-name*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *value*  $\color{SchemeDark}\rangle$ )
> :::
>
> stores a value in a simulated register in the given machine.
>
> ::: scheme
> (get-register-contents
>  $\color{SchemeDark}\langle\kern0.08em$  *machine-model*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *register-name*  $\color{SchemeDark}\rangle$ )
> :::
>
> returns the contents of a simulated register in the given machine.
>
> ::: scheme
> (start
>  $\color{SchemeDark}\langle\kern0.08em$  *machine-model*  $\color{SchemeDark}\rangle$ )
> :::
>
> simulates the execution of the given machine, starting from the
> beginning of the controller sequence and stopping when it reaches the
> end of the sequence.

As an example of how these procedures are used, we can define
`gcd/machine` to be a model of the gcd machine of [Section
5.1.1](#Section 5.1.1) as follows:

::: scheme
(define gcd-machine (make-machine '(a b t) (list (list 'rem remainder)
(list '= =)) '(test-b (test (op =) (reg b) (const 0)) (branch (label
gcd-done)) (assign t (op rem) (reg a) (reg b)) (assign a (reg b))
(assign b (reg t)) (goto (label test-b)) gcd-done)))
:::

The first argument to `make/machine` is a list of register names. The
next argument is a table (a list of two-element lists) that pairs each
operation name with a Scheme procedure that implements the operation
(that is, produces the same output value given the same input values).
The last argument specifies the controller as a list of labels and
machine instructions, as in [Section 5.1](#Section 5.1).

To compute gcds with this machine, we set the input
registers, start the machine, and examine the result when the simulation
terminates:

::: scheme
(set-register-contents! gcd-machine 'a 206)  *done*
(set-register-contents! gcd-machine 'b 40)  *done*  (start gcd-machine)
 *done*  (get-register-contents gcd-machine 'a)  *2*
:::

This computation will run much more slowly than a `gcd` procedure
written in Scheme, because we will simulate low-level machine
instructions, such as `assign`, by much more complex operations.

> **[]{#Exercise 5.7 label="Exercise 5.7"}Exercise 5.7:** Use the
> simulator to test the machines you designed in [Exercise
> 5.4](#Exercise 5.4).

### The Machine Model {#Section 5.2.1}

The machine model generated by `make/machine` is represented as a
procedure with local state using the message-passing techniques
developed in [Chapter 3](#Chapter 3). To build this model,
`make/machine` begins by calling the procedure `make/new/machine` to
construct the parts of the machine model that are common to all register
machines. This basic machine model constructed by `make/new/machine` is
essentially a container for some registers and a stack, together with an
execution mechanism that processes the controller instructions one by
one.

`make/machine` then extends this basic model (by sending it messages) to
include the registers, operations, and controller of the particular
machine being defined. First it allocates a register in the new machine
for each of the supplied register names and installs the designated
operations in the machine. Then it uses an *assembler* (described below
in [Section 5.2.2](#Section 5.2.2)) to transform the controller list
into instructions for the new machine and installs these as the
machine's instruction sequence. `make/machine` returns as its value the
modified machine model.

::: scheme
(define (make-machine register-names ops controller-text) (let ((machine
(make-new-machine))) (for-each (lambda (register-name) ((machine
'allocate-register) register-name)) register-names) ((machine
'install-operations) ops) ((machine 'install-instruction-sequence)
(assemble controller-text machine)) machine))
:::

#### Registers {#registers .unnumbered}

We will represent a register as a procedure with local state, as in
[Chapter 3](#Chapter 3). The procedure `make/register` creates a
register that holds a value that can be accessed or changed:

::: scheme
(define (make-register name) (let ((contents '\*unassigned\*)) (define
(dispatch message) (cond ((eq? message 'get) contents) ((eq? message
'set) (lambda (value) (set! contents value))) (else (error \"Unknown
request: REGISTER\" message)))) dispatch))
:::

The following procedures are used to access registers:

::: scheme
(define (get-contents register) (register 'get)) (define (set-contents!
register value) ((register 'set) value))
:::

#### The stack {#the-stack .unnumbered}

We can also represent a stack as a procedure with local state. The
procedure `make/stack` creates a stack whose local state consists of a
list of the items on the stack. A stack accepts requests to `push` an
item onto the stack, to `pop` the top item off the stack and return it,
and to `initialize` the stack to empty.

::: scheme
(define (make-stack) (let ((s '())) (define (push x) (set! s (cons x
s))) (define (pop) (if (null? s) (error \"Empty stack: POP\") (let ((top
(car s))) (set! s (cdr s)) top))) (define (initialize) (set! s '())
'done) (define (dispatch message) (cond ((eq? message 'push) push) ((eq?
message 'pop) (pop)) ((eq? message 'initialize) (initialize)) (else
(error \"Unknown request: STACK\" message)))) dispatch))
:::

The following procedures are used to access stacks:

::: scheme
(define (pop stack) (stack 'pop)) (define (push stack value) ((stack
'push) value))
:::

#### The basic machine {#the-basic-machine .unnumbered}

The `make/new/machine` procedure, shown in [Figure 5.13](#Figure 5.13),
constructs an object whose local state consists of a stack, an initially
empty instruction sequence, a list of operations that initially contains
an operation to initialize the stack, and a *register table* that
initially contains two registers, named `flag` and `pc` (for "program
counter"). The internal procedure `allocate/register` adds new entries
to the register table, and the internal procedure `lookup/register`
looks up registers in the table.

The `flag` register is used to control branching in the simulated
machine. `test` instructions set the contents of `flag` to the result of
the test (true or false). `branch` instructions decide whether or not to
branch by examining the contents of `flag`.

The `pc` register determines the sequencing of instructions as the
machine runs. This sequencing is implemented by the internal procedure
`execute`. In the simulation model, each machine instruction is a data
structure that includes a procedure of no arguments, called the
*instruction execution procedure*, such that calling this procedure
simulates executing the instruction. As the simulation runs, `pc` points
to the place in the instruction sequence beginning with the next
instruction to be executed. `execute` gets that instruction, executes it
by calling the instruction execution procedure, and repeats this cycle
until there are no more instructions to execute (i.e., until `pc` points
to the end of the instruction sequence).

> **[]{#Figure 5.13 label="Figure 5.13"}Figure 5.13:** $\downarrow$ The
> `make/new/machine` procedure, which implements the basic machine
> model.
>
> ::: scheme
> (define (make-new-machine) (let ((pc (make-register 'pc)) (flag
> (make-register 'flag)) (stack (make-stack)) (the-instruction-sequence
> '())) (let ((the-ops (list (list 'initialize-stack (lambda () (stack
> 'initialize))))) (register-table (list (list 'pc pc) (list 'flag
> flag)))) (define (allocate-register name) (if (assoc name
> register-table) (error \"Multiply defined register: \" name) (set!
> register-table (cons (list name (make-register name))
> register-table))) 'register-allocated) (define (lookup-register name)
> (let ((val (assoc name register-table))) (if val (cadr val) (error
> \"Unknown register:\" name)))) (define (execute) (let ((insts
> (get-contents pc))) (if (null? insts) 'done (begin
> ((instruction-execution-proc (car insts))) (execute))))) (define
> (dispatch message) (cond ((eq? message 'start) (set-contents! pc
> the-instruction-sequence) (execute)) ((eq? message
> 'install-instruction-sequence) (lambda (seq) (set!
> the-instruction-sequence seq))) ((eq? message 'allocate-register)
> allocate-register) ((eq? message 'get-register) lookup-register) ((eq?
> message 'install-operations) (lambda (ops) (set! the-ops (append
> the-ops ops)))) ((eq? message 'stack) stack) ((eq? message
> 'operations) the-ops) (else (error \"Unknown request: MACHINE\"
> message)))) dispatch)))
> :::

As part of its operation, each instruction execution procedure modifies
`pc` to indicate the next instruction to be executed. `branch` and
`goto` instructions change `pc` to point to the new destination. All
other instructions simply advance `pc`, making it point to the next
instruction in the sequence. Observe that each call to `execute` calls
`execute` again, but this does not produce an infinite loop because
running the instruction execution procedure changes the contents of
`pc`.

`make/new/machine` returns a `dispatch` procedure that implements
message-passing access to the internal state. Notice that starting the
machine is accomplished by setting `pc` to the beginning of the
instruction sequence and calling `execute`.

For convenience, we provide an alternate procedural interface to a
machine's `start` operation, as well as procedures to set and examine
register contents, as specified at the beginning of [Section
5.2](#Section 5.2):

::: scheme
(define (start machine) (machine 'start)) (define (get-register-contents
machine register-name) (get-contents (get-register machine
register-name))) (define (set-register-contents! machine register-name
value) (set-contents! (get-register machine register-name) value) 'done)
:::

These procedures (and many procedures in [Section 5.2.2](#Section 5.2.2)
and [Section 5.2.3](#Section 5.2.3)) use the following to look up the
register with a given name in a given machine:

::: scheme
(define (get-register machine reg-name) ((machine 'get-register)
reg-name))
:::

### The Assembler {#Section 5.2.2}

The assembler transforms the sequence of controller expressions for a
machine into a corresponding list of machine instructions, each with its
execution procedure. Overall, the assembler is much like the evaluators
we studied in [Chapter 4](#Chapter 4)---there is an input language (in
this case, the register-machine language) and we must perform an
appropriate action for each type of expression in the language.

The technique of producing an execution procedure for each instruction
is just what we used in [Section 4.1.7](#Section 4.1.7) to speed up the
evaluator by separating analysis from runtime execution. As we saw in
[Chapter 4](#Chapter 4), much useful analysis of Scheme expressions
could be performed without knowing the actual values of variables. Here,
analogously, much useful analysis of register-machine-language
expressions can be performed without knowing the actual contents of
machine registers. For example, we can replace references to registers
by pointers to the register objects, and we can replace references to
labels by pointers to the place in the instruction sequence that the
label designates.

Before it can generate the instruction execution procedures, the
assembler must know what all the labels refer to, so it begins by
scanning the controller text to separate the labels from the
instructions. As it scans the text, it constructs both a list of
instructions and a table that associates each label with a pointer into
that list. Then the assembler augments the instruction list by inserting
the execution procedure for each instruction.

The `assemble` procedure is the main entry to the assembler. It takes
the controller text and the machine model as arguments and returns the
instruction sequence to be stored in the model. `assemble` calls
`extract/labels` to build the initial instruction list and label table
from the supplied controller text. The second argument to
`extract/labels` is a procedure to be called to process these results:
This procedure uses `update/insts!` to generate the instruction
execution procedures and insert them into the instruction list, and
returns the modified list.

::: scheme
(define (assemble controller-text machine) (extract-labels
controller-text (lambda (insts labels) (update-insts! insts labels
machine) insts)))
:::

`extract/labels` takes as arguments a list `text` (the sequence of
controller instruction expressions) and a `receive` procedure. `receive`
will be called with two values: (1) a list `insts` of instruction data
structures, each containing an instruction from `text`; and (2) a table
called `labels`, which associates each label from `text` with the
position in the list `insts` that the label designates.

::: scheme
(define (extract-labels text receive) (if (null? text) (receive '() '())
(extract-labels (cdr text) (lambda (insts labels) (let ((next-inst (car
text))) (if (symbol? next-inst) (receive insts (cons (make-label-entry
next-inst insts) labels)) (receive (cons (make-instruction next-inst)
insts) labels)))))))
:::

`extract/labels` works by sequentially scanning the elements of the
`text` and accumulating the `insts` and the `labels`. If an element is a
symbol (and thus a label) an appropriate entry is added to the `labels`
table. Otherwise the element is accumulated onto the `insts` list.[^289]

`update/insts!` modifies the instruction list, which initially contains
only the text of the instructions, to include the corresponding
execution procedures:

::: scheme
(define (update-insts! insts labels machine) (let ((pc (get-register
machine 'pc)) (flag (get-register machine 'flag)) (stack (machine
'stack)) (ops (machine 'operations))) (for-each (lambda (inst)
(set-instruction-execution-proc! inst (make-execution-procedure
(instruction-text inst) labels machine pc flag stack ops))) insts)))
:::

The machine instruction data structure simply pairs the instruction text
with the corresponding execution procedure. The execution procedure is
not yet available when `extract/labels` constructs the instruction, and
is inserted later by `update/insts!`.

::: scheme
(define (make-instruction text) (cons text '())) (define
(instruction-text inst) (car inst)) (define (instruction-execution-proc
inst) (cdr inst)) (define (set-instruction-execution-proc! inst proc)
(set-cdr! inst proc))
:::

The instruction text is not used by our simulator, but it is handy to
keep around for debugging (see [Exercise 5.16](#Exercise 5.16)).

Elements of the label table are pairs:

::: scheme
(define (make-label-entry label-name insts) (cons label-name insts))
:::

Entries will be looked up in the table with

::: scheme
(define (lookup-label labels label-name) (let ((val (assoc label-name
labels))) (if val (cdr val) (error \"Undefined label: ASSEMBLE\"
label-name))))
:::

> **[]{#Exercise 5.8 label="Exercise 5.8"}Exercise 5.8:** The following
> register-machine code is ambiguous, because the label `here` is
> defined more than once:
>
> ::: scheme
> start (goto (label here)) here (assign a (const 3)) (goto (label
> there)) here (assign a (const 4)) (goto (label there)) there
> :::
>
> With the simulator as written, what will the contents of register `a`
> be when control reaches `there`? Modify the `extract/labels` procedure
> so that the assembler will signal an error if the same label name is
> used to indicate two different locations.

### Generating Execution Procedures for Instructions {#Section 5.2.3}

The assembler calls `make/execution/procedure` to generate the execution
procedure for an instruction. Like the `analyze` procedure in the
evaluator of [Section 4.1.7](#Section 4.1.7), this dispatches on the
type of instruction to generate the appropriate execution procedure.

::: scheme
(define (make-execution-procedure inst labels machine pc flag stack ops)
(cond ((eq? (car inst) 'assign) (make-assign inst machine labels ops
pc)) ((eq? (car inst) 'test) (make-test inst machine labels ops flag
pc)) ((eq? (car inst) 'branch) (make-branch inst machine labels flag
pc)) ((eq? (car inst) 'goto) (make-goto inst machine labels pc)) ((eq?
(car inst) 'save) (make-save inst machine stack pc)) ((eq? (car inst)
'restore) (make-restore inst machine stack pc)) ((eq? (car inst)
'perform) (make-perform inst machine labels ops pc)) (else (error
\"Unknown instruction type: ASSEMBLE\" inst))))
:::

For each type of instruction in the register-machine language, there is
a generator that builds an appropriate execution procedure. The details
of these procedures determine both the syntax and meaning of the
individual instructions in the register-machine language. We use data
abstraction to isolate the detailed syntax of register-machine
expressions from the general execution mechanism, as we did for
evaluators in [Section 4.1.2](#Section 4.1.2), by using syntax
procedures to extract and classify the parts of an instruction.

#### `assign` instructions {#assign-instructions .unnumbered}

The `make/assign` procedure handles `assign` instructions:

::: scheme
(define (make-assign inst machine labels operations pc) (let ((target
(get-register machine (assign-reg-name inst))) (value-exp
(assign-value-exp inst))) (let ((value-proc (if (operation-exp?
value-exp) (make-operation-exp value-exp machine labels operations)
(make-primitive-exp (car value-exp) machine labels)))) (lambda ()  [;
execution procedure for `assign`]{.roman}  (set-contents! target
(value-proc)) (advance-pc pc)))))
:::

`make/assign` extracts the target register name (the second element of
the instruction) and the value expression (the rest of the list that
forms the instruction) from the `assign` instruction using the selectors

::: scheme
(define (assign-reg-name assign-instruction) (cadr assign-instruction))
(define (assign-value-exp assign-instruction) (cddr assign-instruction))
:::

The register name is looked up with `get/register` to produce the target
register object. The value expression is passed to `make/operation/exp`
if the value is the result of an operation, and to `make/primitive/exp`
otherwise. These procedures (shown below) parse the value expression and
produce an execution procedure for the value. This is a procedure of no
arguments, called `value/proc`, which will be evaluated during the
simulation to produce the actual value to be assigned to the register.
Notice that the work of looking up the register name and parsing the
value expression is performed just once, at assembly time, not every
time the instruction is simulated. This saving of work is the reason we
use execution procedures, and corresponds directly to the saving in work
we obtained by separating program analysis from execution in the
evaluator of [Section 4.1.7](#Section 4.1.7).

The result returned by `make/assign` is the execution procedure for the
`assign` instruction. When this procedure is called (by the machine
model's `execute` procedure), it sets the contents of the target
register to the result obtained by executing `value/proc`. Then it
advances the `pc` to the next instruction by running the procedure

::: scheme
(define (advance-pc pc) (set-contents! pc (cdr (get-contents pc))))
:::

`advance/pc` is the normal termination for all instructions except
`branch` and `goto`.

#### `test`, `branch`, and `goto` instructions {#test-branch-and-goto-instructions .unnumbered}

`make/test` handles `test` instructions in a similar way. It extracts
the expression that specifies the condition to be tested and generates
an execution procedure for it. At simulation time, the procedure for the
condition is called, the result is assigned to the `flag` register, and
the `pc` is advanced:

::: scheme
(define (make-test inst machine labels operations flag pc) (let
((condition (test-condition inst))) (if (operation-exp? condition) (let
((condition-proc (make-operation-exp condition machine labels
operations))) (lambda () (set-contents! flag (condition-proc))
(advance-pc pc))) (error \"Bad TEST instruction: ASSEMBLE\" inst))))
(define (test-condition test-instruction) (cdr test-instruction))
:::

The execution procedure for a `branch` instruction checks the contents
of the `flag` register and either sets the contents of the `pc` to the
branch destination (if the branch is taken) or else just advances the
`pc` (if the branch is not taken). Notice that the indicated destination
in a `branch` instruction must be a label, and the `make/branch`
procedure enforces this. Notice also that the label is looked up at
assembly time, not each time the `branch` instruction is simulated.

::: scheme
(define (make-branch inst machine labels flag pc) (let ((dest
(branch-dest inst))) (if (label-exp? dest) (let ((insts (lookup-label
labels (label-exp-label dest)))) (lambda () (if (get-contents flag)
(set-contents! pc insts) (advance-pc pc)))) (error \"Bad BRANCH
instruction: ASSEMBLE\" inst)))) (define (branch-dest
branch-instruction) (cadr branch-instruction))
:::

A `goto` instruction is similar to a branch, except that the destination
may be specified either as a label or as a register, and there is no
condition to check---the `pc` is always set to the new destination.

::: scheme
(define (make-goto inst machine labels pc) (let ((dest (goto-dest
inst))) (cond ((label-exp? dest) (let ((insts (lookup-label labels
(label-exp-label dest)))) (lambda () (set-contents! pc insts))))
((register-exp? dest) (let ((reg (get-register machine (register-exp-reg
dest)))) (lambda () (set-contents! pc (get-contents reg))))) (else
(error \"Bad GOTO instruction: ASSEMBLE\" inst))))) (define (goto-dest
goto-instruction) (cadr goto-instruction))
:::

#### Other instructions {#other-instructions .unnumbered}

The stack instructions `save` and `restore` simply use the stack with
the designated register and advance the `pc`:

::: scheme
(define (make-save inst machine stack pc) (let ((reg (get-register
machine (stack-inst-reg-name inst)))) (lambda () (push stack
(get-contents reg)) (advance-pc pc)))) (define (make-restore inst
machine stack pc) (let ((reg (get-register machine (stack-inst-reg-name
inst)))) (lambda () (set-contents! reg (pop stack)) (advance-pc pc))))
(define (stack-inst-reg-name stack-instruction) (cadr
stack-instruction))
:::

The final instruction type, handled by `make/perform`, generates an
execution procedure for the action to be performed. At simulation time,
the action procedure is executed and the `pc` advanced.

::: scheme
(define (make-perform inst machine labels operations pc) (let ((action
(perform-action inst))) (if (operation-exp? action) (let ((action-proc
(make-operation-exp action machine labels operations))) (lambda ()
(action-proc) (advance-pc pc))) (error \"Bad PERFORM instruction:
ASSEMBLE\" inst)))) (define (perform-action inst) (cdr inst))
:::

#### Execution procedures for subexpressions {#execution-procedures-for-subexpressions .unnumbered}

The value of a `reg`, `label`, or `const` expression may be needed for
assignment to a register (`make/assign`) or for input to an operation
(`make/operation/exp`, below). The following procedure generates
execution procedures to produce values for these expressions during the
simulation:

::: scheme
(define (make-primitive-exp exp machine labels) (cond ((constant-exp?
exp) (let ((c (constant-exp-value exp))) (lambda () c))) ((label-exp?
exp) (let ((insts (lookup-label labels (label-exp-label exp)))) (lambda
() insts))) ((register-exp? exp) (let ((r (get-register machine
(register-exp-reg exp)))) (lambda () (get-contents r)))) (else (error
\"Unknown expression type: ASSEMBLE\" exp))))
:::

The syntax of `reg`, `label`, and `const` expressions is determined by

::: scheme
(define (register-exp? exp) (tagged-list? exp 'reg)) (define
(register-exp-reg exp) (cadr exp)) (define (constant-exp? exp)
(tagged-list? exp 'const)) (define (constant-exp-value exp) (cadr exp))
(define (label-exp? exp) (tagged-list? exp 'label)) (define
(label-exp-label exp) (cadr exp))
:::

`assign`, `perform`, and `test` instructions may include the application
of a machine operation (specified by an `op` expression) to some
operands (specified by `reg` and `const` expressions). The following
procedure produces an execution procedure for an "operation
expression"---a list containing the operation and operand expressions
from the instruction:

::: scheme
(define (make-operation-exp exp machine labels operations) (let ((op
(lookup-prim (operation-exp-op exp) operations)) (aprocs (map (lambda
(e) (make-primitive-exp e machine labels)) (operation-exp-operands
exp)))) (lambda () (apply op (map (lambda (p) (p)) aprocs)))))
:::

The syntax of operation expressions is determined by

::: scheme
(define (operation-exp? exp) (and (pair? exp) (tagged-list? (car exp)
'op))) (define (operation-exp-op operation-exp) (cadr (car
operation-exp))) (define (operation-exp-operands operation-exp) (cdr
operation-exp))
:::

Observe that the treatment of operation expressions is very much like
the treatment of procedure applications by the `analyze/application`
procedure in the evaluator of [Section 4.1.7](#Section 4.1.7) in that we
generate an execution procedure for each operand. At simulation time, we
call the operand procedures and apply the Scheme procedure that
simulates the operation to the resulting values. The simulation
procedure is found by looking up the operation name in the operation
table for the machine:

::: scheme
(define (lookup-prim symbol operations) (let ((val (assoc symbol
operations))) (if val (cadr val) (error \"Unknown operation: ASSEMBLE\"
symbol))))
:::

> **[]{#Exercise 5.9 label="Exercise 5.9"}Exercise 5.9:** The treatment
> of machine operations above permits them to operate on labels as well
> as on constants and the contents of registers. Modify the
> expression-processing procedures to enforce the condition that
> operations can be used only with registers and constants.

> **[]{#Exercise 5.10 label="Exercise 5.10"}Exercise 5.10:** Design a
> new syntax for register-machine instructions and modify the simulator
> to use your new syntax. Can you implement your new syntax without
> changing any part of the simulator except the syntax procedures in
> this section?

> **[]{#Exercise 5.11 label="Exercise 5.11"}Exercise 5.11:** When we
> introduced `save` and `restore` in [Section 5.1.4](#Section 5.1.4), we
> didn't specify what would happen if you tried to restore a register
> that was not the last one saved, as in the sequence
>
> ::: scheme
> (save y) (save x) (restore y)
> :::
>
> There are several reasonable possibilities for the meaning of
> `restore`:
>
> a.  `(restore y)` puts into `y` the last value saved on the stack,
>     regardless of what register that value came from. This is the way
>     our simulator behaves. Show how to take advantage of this behavior
>     to eliminate one instruction from the Fibonacci machine of
>     [Section 5.1.4](#Section 5.1.4) ([Figure 5.12](#Figure 5.12)).
>
> b.  `(restore y)` puts into `y` the last value saved on the stack, but
>     only if that value was saved from `y`; otherwise, it signals an
>     error. Modify the simulator to behave this way. You will have to
>     change `save` to put the register name on the stack along with the
>     value.
>
> c.  `(restore y)` puts into `y` the last value saved from `y`
>     regardless of what other registers were saved after `y` and not
>     restored. Modify the simulator to behave this way. You will have
>     to associate a separate stack with each register. You should make
>     the `initialize/stack` operation initialize all the register
>     stacks.

> **[]{#Exercise 5.12 label="Exercise 5.12"}Exercise 5.12:** The
> simulator can be used to help determine the data paths required for
> implementing a machine with a given controller. Extend the assembler
> to store the following information in the machine model:
>
> -   a list of all instructions, with duplicates removed, sorted by
>     instruction type (`assign`, `goto`, and so on);
>
> -   a list (without duplicates) of the registers used to hold entry
>     points (these are the registers referenced by `goto`
>     instructions);
>
> -   a list (without duplicates) of the registers that are `save`d or
>     `restore`d;
>
> -   for each register, a list (without duplicates) of the sources from
>     which it is assigned (for example, the sources for register `val`
>     in the factorial machine of [Figure 5.11](#Figure 5.11) are
>     `(const 1)` and `((op *) (reg n) (reg val))`).
>
> Extend the message-passing interface to the machine to provide access
> to this new information. To test your analyzer, define the Fibonacci
> machine from [Figure 5.12](#Figure 5.12) and examine the lists you
> constructed.

> **[]{#Exercise 5.13 label="Exercise 5.13"}Exercise 5.13:** Modify the
> simulator so that it uses the controller sequence to determine what
> registers the machine has rather than requiring a list of registers as
> an argument to `make/machine`. Instead of pre-allocating the registers
> in `make/machine`, you can allocate them one at a time when they are
> first seen during assembly of the instructions.

### Monitoring Machine Performance {#Section 5.2.4}

Simulation is useful not only for verifying the correctness of a
proposed machine design but also for measuring the machine's
performance. For example, we can install in our simulation program a
"meter" that measures the number of stack operations used in a
computation. To do this, we modify our simulated stack to keep track of
the number of times registers are saved on the stack and the maximum
depth reached by the stack, and add a message to the stack's interface
that prints the statistics, as shown below. We also add an operation to
the basic machine model to print the stack statistics, by initializing
`the/ops` in `make/new/machine` to

::: scheme
(list (list 'initialize-stack (lambda () (stack 'initialize))) (list
'print-stack-statistics (lambda () (stack 'print-statistics))))
:::

Here is the new version of `make/stack`:

::: scheme
(define (make-stack) (let ((s '()) (number-pushes 0) (max-depth 0)
(current-depth 0)) (define (push x) (set! s (cons x s)) (set!
number-pushes (+ 1 number-pushes)) (set! current-depth (+ 1
current-depth)) (set! max-depth (max current-depth max-depth))) (define
(pop) (if (null? s) (error \"Empty stack: POP\") (let ((top (car s)))
(set! s (cdr s)) (set! current-depth (- current-depth 1)) top))) (define
(initialize) (set! s '()) (set! number-pushes 0) (set! max-depth 0)
(set! current-depth 0) 'done) (define (print-statistics) (newline)
(display (list 'total-pushes '= number-pushes 'maximum-depth '=
max-depth))) (define (dispatch message) (cond ((eq? message 'push) push)
((eq? message 'pop) (pop)) ((eq? message 'initialize) (initialize))
((eq? message 'print-statistics) (print-statistics)) (else (error
\"Unknown request: STACK\" message)))) dispatch))
:::

[Exercise 5.15](#Exercise 5.15) through [Exercise 5.19](#Exercise 5.19)
describe other useful monitoring and debugging features that can be
added to the register-machine simulator.

> **[]{#Exercise 5.14 label="Exercise 5.14"}Exercise 5.14:** Measure the
> number of pushes and the maximum stack depth required to compute $n!$
> for various small values of $n$ using the factorial machine shown in
> [Figure 5.11](#Figure 5.11). From your data determine formulas in
> terms of $n$ for the total number of push operations and the maximum
> stack depth used in computing $n!$ for any $n > 1$. Note that each of
> these is a linear function of $n$ and is thus determined by two
> constants. In order to get the statistics printed, you will have to
> augment the factorial machine with instructions to initialize the
> stack and print the statistics. You may want to also modify the
> machine so that it repeatedly reads a value for $n$, computes the
> factorial, and prints the result (as we did for the gcd
> machine in [Figure 5.4](#Figure 5.4)), so that you will not have to
> repeatedly invoke `get/register/contents`, `set/register/contents!`,
> and `start`.

> **[]{#Exercise 5.15 label="Exercise 5.15"}Exercise 5.15:** Add
> *instruction counting* to the register machine simulation. That is,
> have the machine model keep track of the number of instructions
> executed. Extend the machine model's interface to accept a new message
> that prints the value of the instruction count and resets the count to
> zero.

> **[]{#Exercise 5.16 label="Exercise 5.16"}Exercise 5.16:** Augment the
> simulator to provide for *instruction tracing*. That is, before each
> instruction is executed, the simulator should print the text of the
> instruction. Make the machine model accept `trace/on` and `trace/off`
> messages to turn tracing on and off.

> **[]{#Exercise 5.17 label="Exercise 5.17"}Exercise 5.17:** Extend the
> instruction tracing of [Exercise 5.16](#Exercise 5.16) so that before
> printing an instruction, the simulator prints any labels that
> immediately precede that instruction in the controller sequence. Be
> careful to do this in a way that does not interfere with instruction
> counting ([Exercise 5.15](#Exercise 5.15)). You will have to make the
> simulator retain the necessary label information.

> **[]{#Exercise 5.18 label="Exercise 5.18"}Exercise 5.18:** Modify the
> `make/register` procedure of [Section 5.2.1](#Section 5.2.1) so that
> registers can be traced. Registers should accept messages that turn
> tracing on and off. When a register is traced, assigning a value to
> the register should print the name of the register, the old contents
> of the register, and the new contents being assigned. Extend the
> interface to the machine model to permit you to turn tracing on and
> off for designated machine registers.

> **[]{#Exercise 5.19 label="Exercise 5.19"}Exercise 5.19:** Alyssa P.
> Hacker wants a *breakpoint* feature in the simulator to help her debug
> her machine designs. You have been hired to install this feature for
> her. She wants to be able to specify a place in the controller
> sequence where the simulator will stop and allow her to examine the
> state of the machine. You are to implement a procedure
>
> ::: scheme
> (set-breakpoint
>  $\color{SchemeDark}\langle\kern0.08em$  *machine*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *label*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *n*  $\color{SchemeDark}\rangle$ )
> :::
>
> that sets a breakpoint just before the $n^{\mathrm{th}}$ instruction
> after the given label. For example,
>
> ::: scheme
> (set-breakpoint gcd-machine 'test-b 4)
> :::
>
> installs a breakpoint in `gcd/machine` just before the assignment to
> register `a`. When the simulator reaches the breakpoint it should
> print the label and the offset of the breakpoint and stop executing
> instructions. Alyssa can then use `get/register/contents` and
> `set/register/contents!` to manipulate the state of the simulated
> machine. She should then be able to continue execution by saying
>
> ::: scheme
> (proceed-machine
>  $\color{SchemeDark}\langle\kern0.08em$  *machine*  $\color{SchemeDark}\rangle$ )
> :::
>
> She should also be able to remove a specific breakpoint by means of
>
> ::: scheme
> (cancel-breakpoint
>  $\color{SchemeDark}\langle\kern0.08em$  *machine*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *label*  $\color{SchemeDark}\rangle$
>  $\color{SchemeDark}\langle$  *n*  $\color{SchemeDark}\rangle$ )
> :::
>
> or to remove all breakpoints by means of
>
> ::: scheme
> (cancel-all-breakpoints
>  $\color{SchemeDark}\langle\kern0.08em$  *machine*  $\color{SchemeDark}\rangle$ )
> :::

## Storage Allocation and Garbage Collection {#Section 5.3}

In [Section 5.4](#Section 5.4), we will show how to implement a Scheme
evaluator as a register machine. In order to simplify the discussion, we
will assume that our register machines can be equipped with a
*list-structured memory*, in which the basic operations for manipulating
list-structured data are primitive. Postulating the existence of such a
memory is a useful abstraction when one is focusing on the mechanisms of
control in a Scheme interpreter, but this does not reflect a realistic
view of the actual primitive data operations of contemporary computers.
To obtain a more complete picture of how a Lisp system operates, we must
investigate how list structure can be represented in a way that is
compatible with conventional computer memories.

There are two considerations in implementing list structure. The first
is purely an issue of representation: how to represent the
"box-and-pointer" structure of Lisp pairs, using only the storage and
addressing capabilities of typical computer memories. The second issue
concerns the management of memory as a computation proceeds. The
operation of a Lisp system depends crucially on the ability to
continually create new data objects. These include objects that are
explicitly created by the Lisp procedures being interpreted as well as
structures created by the interpreter itself, such as environments and
argument lists. Although the constant creation of new data objects would
pose no problem on a computer with an infinite amount of rapidly
addressable memory, computer memories are available only in finite sizes
(more's the pity). Lisp systems thus provide an *automatic storage
allocation* facility to support the illusion of an infinite memory. When
a data object is no longer needed, the memory allocated to it is
automatically recycled and used to construct new data objects. There are
various techniques for providing such automatic storage allocation. The
method we shall discuss in this section is called *garbage collection*.

### Memory as Vectors {#Section 5.3.1}

A conventional computer memory can be thought of as an array of
cubbyholes, each of which can contain a piece of information. Each
cubbyhole has a unique name, called its *address* or *location*. Typical
memory systems provide two primitive operations: one that fetches the
data stored in a specified location and one that assigns new data to a
specified location. Memory addresses can be incremented to support
sequential access to some set of the cubbyholes. More generally, many
important data operations require that memory addresses be treated as
data, which can be stored in memory locations and manipulated in machine
registers. The representation of list structure is one application of
such *address arithmetic*.

To model computer memory, we use a new kind of data structure called a
*vector*. Abstractly, a vector is a compound data object whose
individual elements can be accessed by means of an integer index in an
amount of time that is independent of the index.[^290] In order to
describe memory operations, we use two primitive Scheme procedures for
manipulating vectors:

-   `(vector/ref `$\langle$*`vector`*$\rangle$` `$\langle$*`n`*$\rangle$`)`
    returns the $n^{\mathrm{th}}$ element of the vector.

-   `(vector/set! `$\langle$*`vector`*$\rangle$` `$\langle$*`n`*$\rangle$` `$\langle$*`value`*$\rangle$`)`
    sets the $n^{\mathrm{th}}$ element of the vector to the designated
    value.

For example, if `v` is a vector, then `(vector/ref v 5)` gets the fifth
entry in the vector `v` and `(vector/set! v 5 7)` changes the value of
the fifth entry of the vector `v` to 7.[^291] For computer memory, this
access can be implemented through the use of address arithmetic to
combine a *base address* that specifies the beginning location of a
vector in memory with an *index* that specifies the offset of a
particular element of the vector.

#### Representing Lisp data {#representing-lisp-data .unnumbered}

We can use vectors to implement the basic pair structures required for a
list-structured memory. Let us imagine that computer memory is divided
into two vectors: `the/cars` and `the/cdrs`. We will represent list
structure as follows: A pointer to a pair is an index into the two
vectors. The `car` of the pair is the entry in `the/cars` with the
designated index, and the `cdr` of the pair is the entry in `the/cdrs`
with the designated index. We also need a representation for objects
other than pairs (such as numbers and symbols) and a way to distinguish
one kind of data from another. There are many methods of accomplishing
this, but they all reduce to using *typed pointers*, that is, to
extending the notion of "pointer" to include information on data
type.[^292] The data type enables the system to distinguish a pointer to
a pair (which consists of the "pair" data type and an index into the
memory vectors) from pointers to other kinds of data (which consist of
some other data type and whatever is being used to represent data of
that type). Two data objects are considered to be the same (`eq?`) if
their pointers are identical.[^293] [Figure 5.14](#Figure 5.14)
illustrates the use of this method to represent the list `((1 2) 3 4)`,
whose box-and-pointer diagram is also shown. We use letter prefixes to
denote the data-type information. Thus, a pointer to the pair with index
5 is denoted `p5`, the empty list is denoted by the pointer `e0`, and a
pointer to the number 4 is denoted `n4`. In the box-and-pointer diagram,
we have indicated at the lower left of each pair the vector index that
specifies where the `car` and `cdr` of the pair are stored. The blank
locations in `the/cars` and `the/cdrs` may contain parts of other list
structures (not of interest here).

A pointer to a number, such as `n4`, might consist of a type indicating
numeric data together with the actual representation of the number
4.[^294] To deal with numbers that are too large to be represented in
the fixed amount of space allocated for a single pointer, we could use a
distinct *bignum* data type, for which the pointer designates a list in
which the parts of the number are stored.[^295]

[]{#Figure 5.14 label="Figure 5.14"}

![image](fig/chap5/Fig5.14a.pdf){width="91mm"}

> **Figure 5.14:** Box-and-pointer and memory-vector representations of
> the list `((1 2) 3 4)`.

A symbol might be represented as a typed pointer that designates a
sequence of the characters that form the symbol's printed
representation. This sequence is constructed by the Lisp reader when the
character string is initially encountered in input. Since we want two
instances of a symbol to be recognized as the "same" symbol by `eq?` and
we want `eq?` to be a simple test for equality of pointers, we must
ensure that if the reader sees the same character string twice, it will
use the same pointer (to the same sequence of characters) to represent
both occurrences. To accomplish this, the reader maintains a table,
traditionally called the *obarray*, of all the symbols it has ever
encountered. When the reader encounters a character string and is about
to construct a symbol, it checks the obarray to see if it has ever
before seen the same character string. If it has not, it uses the
characters to construct a new symbol (a typed pointer to a new character
sequence) and enters this pointer in the obarray. If the reader has seen
the string before, it returns the symbol pointer stored in the obarray.
This process of replacing character strings by unique pointers is called
*interning* symbols.

#### Implementing the primitive list operations {#implementing-the-primitive-list-operations .unnumbered}

Given the above representation scheme, we can replace each "primitive"
list operation of a register machine with one or more primitive vector
operations. We will use two registers, `the/cars` and `the/cdrs`, to
identify the memory vectors, and will assume that `vector/ref` and
`vector/set!` are available as primitive operations. We also assume that
numeric operations on pointers (such as incrementing a pointer, using a
pair pointer to index a vector, or adding two numbers) use only the
index portion of the typed pointer.

For example, we can make a register machine support the instructions

::: scheme
(assign
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
(op car) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ ))
(assign
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
(op cdr) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ ))
:::

if we implement these, respectively, as

::: scheme
(assign
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
(op vector-ref) (reg the-cars) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ ))
(assign
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
(op vector-ref) (reg the-cdrs) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ ))
:::

The instructions

::: scheme
(perform (op set-car!) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$ )
(reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ ))
(perform (op set-cdr!) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$ )
(reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ ))
:::

are implemented as

::: scheme
(perform (op vector-set!) (reg the-cars) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$ )
(reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ ))
(perform (op vector-set!) (reg the-cdrs) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$ )
(reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ ))
:::

`cons` is performed by allocating an unused index and storing the
arguments to `cons` in `the/cars` and `the/cdrs` at that indexed vector
position. We presume that there is a special register, `free`, that
always holds a pair pointer containing the next available index, and
that we can increment the index part of that pointer to find the next
free location.[^296] For example, the instruction

::: scheme
(assign
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
(op cons) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ )
(reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 3}}\rangle$ ))
:::

is implemented as the following sequence of vector operations:[^297]

::: scheme
(perform (op vector-set!) (reg the-cars) (reg free) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ ))
(perform (op vector-set!) (reg the-cdrs) (reg free) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 3}}\rangle$ ))
(assign
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
(reg free)) (assign free (op +) (reg free) (const 1))
:::

The `eq?` operation

::: scheme
(op eq?) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$ )
(reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ )
:::

simply tests the equality of all fields in the registers, and predicates
such as `pair?`, `null?`, `symbol?`, and `number?` need only check the
type field.

#### Implementing stacks {#implementing-stacks .unnumbered}

Although our register machines use stacks, we need do nothing special
here, since stacks can be modeled in terms of lists. The stack can be a
list of the saved values, pointed to by a special register `the/stack`.
Thus, ` (save `$\langle$*`reg`*$\rangle$`)` can be implemented as

::: scheme
(assign the-stack (op cons) (reg
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}\rangle$ ) (reg
the-stack))
:::

Similarly, `(restore `$\langle$*`reg`*$\rangle$`)` can be implemented as

::: scheme
(assign
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}\rangle$  (op
car) (reg the-stack)) (assign the-stack (op cdr) (reg the-stack))
:::

and `(perform (op initialize/stack))` can be implemented as

::: scheme
(assign the-stack (const ()))
:::

These operations can be further expanded in terms of the vector
operations given above. In conventional computer architectures, however,
it is usually advantageous to allocate the stack as a separate vector.
Then pushing and popping the stack can be accomplished by incrementing
or decrementing an index into that vector.

> **[]{#Exercise 5.20 label="Exercise 5.20"}Exercise 5.20:** Draw the
> box-and-pointer representation and the memory-vector representation
> (as in [Figure 5.14](#Figure 5.14)) of the list structure produced by
>
> ::: scheme
> (define x (cons 1 2)) (define y (list x x))
> :::
>
> with the `free` pointer initially `p1`. What is the final value of
> `free` ? What pointers represent the values of `x` and `y` ?

> **[]{#Exercise 5.21 label="Exercise 5.21"}Exercise 5.21:** Implement
> register machines for the following procedures. Assume that the
> list-structure memory operations are available as machine primitives.
>
> a.  Recursive `count/leaves`:
>
>     ::: scheme
>     (define (count-leaves tree) (cond ((null? tree) 0) ((not (pair?
>     tree)) 1) (else (+ (count-leaves (car tree)) (count-leaves (cdr
>     tree))))))
>     :::
>
> b.  Recursive `count/leaves` with explicit counter:
>
>     ::: scheme
>     (define (count-leaves tree) (define (count-iter tree n) (cond
>     ((null? tree) n) ((not (pair? tree)) (+ n 1)) (else (count-iter
>     (cdr tree) (count-iter (car tree) n))))) (count-iter tree 0))
>     :::

> **[]{#Exercise 5.22 label="Exercise 5.22"}Exercise 5.22:** [Exercise
> 3.12](#Exercise 3.12) of [Section 3.3.1](#Section 3.3.1) presented an
> `append` procedure that appends two lists to form a new list and an
> `append!` procedure that splices two lists together. Design a register
> machine to implement each of these procedures. Assume that the
> list-structure memory operations are available as primitive
> operations.

### Maintaining the Illusion of Infinite Memory {#Section 5.3.2}

The representation method outlined in [Section 5.3.1](#Section 5.3.1)
solves the problem of implementing list structure, provided that we have
an infinite amount of memory. With a real computer we will eventually
run out of free space in which to construct new pairs.[^298] However,
most of the pairs generated in a typical computation are used only to
hold intermediate results. After these results are accessed, the pairs
are no longer needed---they are *garbage*. For instance, the computation

::: scheme
(accumulate + 0 (filter odd? (enumerate-interval 0 n)))
:::

constructs two lists: the enumeration and the result of filtering the
enumeration. When the accumulation is complete, these lists are no
longer needed, and the allocated memory can be reclaimed. If we can
arrange to collect all the garbage periodically, and if this turns out
to recycle memory at about the same rate at which we construct new
pairs, we will have preserved the illusion that there is an infinite
amount of memory.

In order to recycle pairs, we must have a way to determine which
allocated pairs are not needed (in the sense that their contents can no
longer influence the future of the computation). The method we shall
examine for accomplishing this is known as *garbage collection*. Garbage
collection is based on the observation that, at any moment in a Lisp
interpretation, the only objects that can affect the future of the
computation are those that can be reached by some succession of `car`
and `cdr` operations starting from the pointers that are currently in
the machine registers.[^299] Any memory cell that is not so accessible
may be recycled.

There are many ways to perform garbage collection. The method we shall
examine here is called *stop-and-copy*. The basic idea is to divide
memory into two halves: "working memory" and "free memory." When `cons`
constructs pairs, it allocates these in working memory. When working
memory is full, we perform garbage collection by locating all the useful
pairs in working memory and copying these into consecutive locations in
free memory. (The useful pairs are located by tracing all the `car` and
`cdr` pointers, starting with the machine registers.) Since we do not
copy the garbage, there will presumably be additional free memory that
we can use to allocate new pairs. In addition, nothing in the working
memory is needed, since all the useful pairs in it have been copied.
Thus, if we interchange the roles of working memory and free memory, we
can continue processing; new pairs will be allocated in the new working
memory (which was the old free memory). When this is full, we can copy
the useful pairs into the new free memory (which was the old working
memory).[^300]

#### Implementation of a stop-and-copy garbage collector {#implementation-of-a-stop-and-copy-garbage-collector .unnumbered}

We now use our register-machine language to describe the stop-and-copy
algorithm in more detail. We will assume that there is a register called
`root` that contains a pointer to a structure that eventually points at
all accessible data. This can be arranged by storing the contents of all
the machine registers in a pre-allocated list pointed at by `root` just
before starting garbage collection.[^301] We also assume that, in
addition to the current working memory, there is free memory available
into which we can copy the useful data. The current working memory
consists of vectors whose base addresses are in registers called
`the/cars` and `the/cdrs`, and the free memory is in registers called
`new/cars` and `new/cdrs`.

Garbage collection is triggered when we exhaust the free cells in the
current working memory, that is, when a `cons` operation attempts to
increment the `free` pointer beyond the end of the memory vector. When
the garbage-collection process is complete, the `root` pointer will
point into the new memory, all objects accessible from the `root` will
have been moved to the new memory, and the `free` pointer will indicate
the next place in the new memory where a new pair can be allocated. In
addition, the roles of working memory and new memory will have been
interchanged---new pairs will be constructed in the new memory,
beginning at the place indicated by `free`, and the (previous) working
memory will be available as the new memory for the next garbage
collection. [Figure 5.15](#Figure 5.15) shows the arrangement of memory
just before and just after garbage collection.

[]{#Figure 5.15 label="Figure 5.15"}

![image](fig/chap5/Fig5.15a.pdf){width="91mm"}

> **Figure 5.15:** Reconfiguration of memory by the garbage-collection
> process.

The state of the garbage-collection process is controlled by maintaining
two pointers: `free` and `scan`. These are initialized to point to the
beginning of the new memory. The algorithm begins by relocating the pair
pointed at by `root` to the beginning of the new memory. The pair is
copied, the `root` pointer is adjusted to point to the new location, and
the `free` pointer is incremented. In addition, the old location of the
pair is marked to show that its contents have been moved. This marking
is done as follows: In the `car` position, we place a special tag that
signals that this is an already-moved object. (Such an object is
traditionally called a *broken heart*.)[^302] In the `cdr` position we
place a *forwarding address* that points at the location to which the
object has been moved.

After relocating the root, the garbage collector enters its basic cycle.
At each step in the algorithm, the `scan` pointer (initially pointing at
the relocated root) points at a pair that has been moved to the new
memory but whose `car` and `cdr` pointers still refer to objects in the
old memory. These objects are each relocated, and the `scan` pointer is
incremented. To relocate an object (for example, the object indicated by
the `car` pointer of the pair we are scanning) we check to see if the
object has already been moved (as indicated by the presence of a
broken-heart tag in the `car` position of the object). If the object has
not already been moved, we copy it to the place indicated by `free`,
update `free`, set up a broken heart at the object's old location, and
update the pointer to the object (in this example, the `car` pointer of
the pair we are scanning) to point to the new location. If the object
has already been moved, its forwarding address (found in the `cdr`
position of the broken heart) is substituted for the pointer in the pair
being scanned. Eventually, all accessible objects will have been moved
and scanned, at which point the `scan` pointer will overtake the `free`
pointer and the process will terminate.

We can specify the stop-and-copy algorithm as a sequence of instructions
for a register machine. The basic step of relocating an object is
accomplished by a subroutine called `relocate/old/result/in/new`. This
subroutine gets its argument, a pointer to the object to be relocated,
from a register named `old`. It relocates the designated object
(incrementing `free` in the process), puts a pointer to the relocated
object into a register called `new`, and returns by branching to the
entry point stored in the register `relocate/continue`. To begin garbage
collection, we invoke this subroutine to relocate the `root` pointer,
after initializing `free` and `scan`. When the relocation of `root` has
been accomplished, we install the new pointer as the new `root` and
enter the main loop of the garbage collector.

::: scheme
begin-garbage-collection (assign free (const 0)) (assign scan (const 0))
(assign old (reg root)) (assign relocate-continue (label reassign-root))
(goto (label relocate-old-result-in-new)) reassign-root (assign root
(reg new)) (goto (label gc-loop))
:::

In the main loop of the garbage collector we must determine whether
there are any more objects to be scanned. We do this by testing whether
the `scan` pointer is coincident with the `free` pointer. If the
pointers are equal, then all accessible objects have been relocated, and
we branch to `gc/flip`, which cleans things up so that we can continue
the interrupted computation. If there are still pairs to be scanned, we
call the relocate subroutine to relocate the `car` of the next pair (by
placing the `car` pointer in `old`). The `relocate/continue` register is
set up so that the subroutine will return to update the `car` pointer.

::: scheme
gc-loop (test (op =) (reg scan) (reg free)) (branch (label gc-flip))
(assign old (op vector-ref) (reg new-cars) (reg scan)) (assign
relocate-continue (label update-car)) (goto (label
relocate-old-result-in-new))
:::

At `update/car`, we modify the `car` pointer of the pair being scanned,
then proceed to relocate the `cdr` of the pair. We return to
`update/cdr` when that relocation has been accomplished. After
relocating and updating the `cdr`, we are finished scanning that pair,
so we continue with the main loop.

::: scheme
update-car (perform (op vector-set!) (reg new-cars) (reg scan) (reg
new)) (assign old (op vector-ref) (reg new-cdrs) (reg scan)) (assign
relocate-continue (label update-cdr)) (goto (label
relocate-old-result-in-new)) update-cdr (perform (op vector-set!) (reg
new-cdrs) (reg scan) (reg new)) (assign scan (op +) (reg scan) (const
1)) (goto (label gc-loop))
:::

The subroutine `relocate/old/result/in/new` relocates objects as
follows: If the object to be relocated (pointed at by `old`) is not a
pair, then we return the same pointer to the object unchanged (in
`new`). (For example, we may be scanning a pair whose `car` is the
number 4. If we represent the `car` by `n4`, as described in [Section
5.3.1](#Section 5.3.1), then we want the "relocated" `car` pointer to
still be `n4`.) Otherwise, we must perform the relocation. If the `car`
position of the pair to be relocated contains a broken-heart tag, then
the pair has in fact already been moved, so we retrieve the forwarding
address (from the `cdr` position of the broken heart) and return this in
`new`. If the pointer in `old` points at a yet-unmoved pair, then we
move the pair to the first free cell in new memory (pointed at by
`free`) and set up the broken heart by storing a broken-heart tag and
forwarding address at the old location. `relocate/old/result/in/new`
uses a register `oldcr` to hold the `car` or the `cdr` of the object
pointed at by `old`.[^303]

::: scheme
relocate-old-result-in-new (test (op pointer-to-pair?) (reg old))
(branch (label pair)) (assign new (reg old)) (goto (reg
relocate-continue)) pair (assign oldcr (op vector-ref) (reg the-cars)
(reg old)) (test (op broken-heart?) (reg oldcr)) (branch (label
already-moved)) (assign new (reg free))  [; new location for
pair]{.roman}   [;; Update `free` pointer.]{.roman}  (assign free (op +)
(reg free) (const 1))  [;; Copy the `car` and `cdr` to new
memory.]{.roman}  (perform (op vector-set!) (reg new-cars) (reg new)
(reg oldcr)) (assign oldcr (op vector-ref) (reg the-cdrs) (reg old))
(perform (op vector-set!) (reg new-cdrs) (reg new) (reg oldcr))  [;;
Construct the broken heart.]{.roman}  (perform (op vector-set!) (reg
the-cars) (reg old) (const broken-heart)) (perform (op vector-set!) (reg
the-cdrs) (reg old) (reg new)) (goto (reg relocate-continue))
already-moved (assign new (op vector-ref) (reg the-cdrs) (reg old))
(goto (reg relocate-continue))
:::

At the very end of the garbage-collection process, we interchange the
role of old and new memories by interchanging pointers: interchanging
`the/cars` with `new/cars`, and `the/cdrs` with `new/cdrs`. We will then
be ready to perform another garbage collection the next time memory runs
out.

::: scheme
gc-flip (assign temp (reg the-cdrs)) (assign the-cdrs (reg new-cdrs))
(assign new-cdrs (reg temp)) (assign temp (reg the-cars)) (assign
the-cars (reg new-cars)) (assign new-cars (reg temp))
:::

## The Explicit-Control Evaluator {#Section 5.4}

In [Section 5.1](#Section 5.1) we saw how to transform simple Scheme
programs into descriptions of register machines. We will now perform
this transformation on a more complex program, the metacircular
evaluator of [Section 4.1.1](#Section 4.1.1)--[Section
4.1.4](#Section 4.1.4), which shows how the behavior of a Scheme
interpreter can be described in terms of the procedures `eval` and
`apply`. The *explicit-control evaluator* that we develop in this
section shows how the underlying procedure-calling and argument-passing
mechanisms used in the evaluation process can be described in terms of
operations on registers and stacks. In addition, the explicit-control
evaluator can serve as an implementation of a Scheme interpreter,
written in a language that is very similar to the native machine
language of conventional computers. The evaluator can be executed by the
register-machine simulator of [Section 5.2](#Section 5.2).
Alternatively, it can be used as a starting point for building a
machine-language implementation of a Scheme evaluator, or even a
special-purpose machine for evaluating Scheme expressions. [Figure
5.16](#Figure 5.16) shows such a hardware implementation: a silicon chip
that acts as an evaluator for Scheme. The chip designers started with
the data-path and controller specifications for a register machine
similar to the evaluator described in this section and used design
automation programs to construct the integrated-circuit layout.[^304]

[]{#Figure 5.16 label="Figure 5.16"}

![image](fig/chap5/chip.jpg){width="91mm"}

> **Figure 5.16:** A silicon-chip implementation of an evaluator for
> Scheme.

#### Registers and operations {#registers-and-operations .unnumbered}

In designing the explicit-control evaluator, we must specify the
operations to be used in our register machine. We described the
metacircular evaluator in terms of abstract syntax, using procedures
such as `quoted?` and `make/procedure`. In implementing the register
machine, we could expand these procedures into sequences of elementary
list-structure memory operations, and implement these operations on our
register machine. However, this would make our evaluator very long,
obscuring the basic structure with details. To clarify the presentation,
we will include as primitive operations of the register machine the
syntax procedures given in [Section 4.1.2](#Section 4.1.2) and the
procedures for representing environments and other run-time data given
in sections [Section 4.1.3](#Section 4.1.3) and [Section
4.1.4](#Section 4.1.4). In order to completely specify an evaluator that
could be programmed in a low-level machine language or implemented in
hardware, we would replace these operations by more elementary
operations, using the list-structure implementation we described in
[Section 5.3](#Section 5.3).

Our Scheme evaluator register machine includes a stack and seven
registers: `exp`, `env`, `val`, `continue`, `proc`, `argl`, and `unev`.
`exp` is used to hold the expression to be evaluated, and `env` contains
the environment in which the evaluation is to be performed. At the end
of an evaluation, `val` contains the value obtained by evaluating the
expression in the designated environment. The `continue` register is
used to implement recursion, as explained in [Section
5.1.4](#Section 5.1.4). (The evaluator needs to call itself recursively,
since evaluating an expression requires evaluating its subexpressions.)
The registers `proc`, `argl`, and `unev` are used in evaluating
combinations.

We will not provide a data-path diagram to show how the registers and
operations of the evaluator are connected, nor will we give the complete
list of machine operations. These are implicit in the evaluator's
controller, which will be presented in detail.

### The Core of the Explicit-Control Evaluator {#Section 5.4.1}

The central element in the evaluator is the sequence of instructions
beginning at `eval/dispatch`. This corresponds to the `eval` procedure
of the metacircular evaluator described in [Section
4.1.1](#Section 4.1.1). When the controller starts at `eval/dispatch`,
it evaluates the expression specified by `exp` in the environment
specified by `env`. When evaluation is complete, the controller will go
to the entry point stored in `continue`, and the `val` register will
hold the value of the expression. As with the metacircular `eval`, the
structure of `eval/dispatch` is a case analysis on the syntactic type of
the expression to be evaluated.[^305]

::: scheme
eval-dispatch (test (op self-evaluating?) (reg exp)) (branch (label
ev-self-eval)) (test (op variable?) (reg exp)) (branch (label
ev-variable)) (test (op quoted?) (reg exp)) (branch (label ev-quoted))
(test (op assignment?) (reg exp)) (branch (label ev-assignment)) (test
(op definition?) (reg exp)) (branch (label ev-definition)) (test (op
if?) (reg exp)) (branch (label ev-if)) (test (op lambda?) (reg exp))
(branch (label ev-lambda)) (test (op begin?) (reg exp)) (branch (label
ev-begin)) (test (op application?) (reg exp)) (branch (label
ev-application)) (goto (label unknown-expression-type))
:::

#### Evaluating simple expressions {#evaluating-simple-expressions .unnumbered}

Numbers and strings (which are self-evaluating), variables, quotations,
and `lambda` expressions have no subexpressions to be evaluated. For
these, the evaluator simply places the correct value in the `val`
register and continues execution at the entry point specified by
`continue`. Evaluation of simple expressions is performed by the
following controller code:

::: scheme
ev-self-eval (assign val (reg exp)) (goto (reg continue)) ev-variable
(assign val (op lookup-variable-value) (reg exp) (reg env)) (goto (reg
continue)) ev-quoted (assign val (op text-of-quotation) (reg exp)) (goto
(reg continue)) ev-lambda (assign unev (op lambda-parameters) (reg exp))
(assign exp (op lambda-body) (reg exp)) (assign val (op make-procedure)
(reg unev) (reg exp) (reg env)) (goto (reg continue))
:::

Observe how `ev/lambda` uses the `unev` and `exp` registers to hold the
parameters and body of the lambda expression so that they can be passed
to the `make/procedure` operation, along with the environment in `env`.

#### Evaluating procedure applications {#evaluating-procedure-applications .unnumbered}

A procedure application is specified by a combination containing an
operator and operands. The operator is a subexpression whose value is a
procedure, and the operands are subexpressions whose values are the
arguments to which the procedure should be applied. The metacircular
`eval` handles applications by calling itself recursively to evaluate
each element of the combination, and then passing the results to
`apply`, which performs the actual procedure application. The
explicit-control evaluator does the same thing; these recursive calls
are implemented by `goto` instructions, together with use of the stack
to save registers that will be restored after the recursive call
returns. Before each call we will be careful to identify which registers
must be saved (because their values will be needed later).[^306]

We begin the evaluation of an application by evaluating the operator to
produce a procedure, which will later be applied to the evaluated
operands. To evaluate the operator, we move it to the `exp` register and
go to `eval/dispatch`. The environment in the `env` register is already
the correct one in which to evaluate the operator. However, we save
`env` because we will need it later to evaluate the operands. We also
extract the operands into `unev` and save this on the stack. We set up
`continue` so that `eval/dispatch` will resume at `ev/appl/did/operator`
after the operator has been evaluated. First, however, we save the old
value of `continue`, which tells the controller where to continue after
the application.

::: scheme
ev-application (save continue) (save env) (assign unev (op operands)
(reg exp)) (save unev) (assign exp (op operator) (reg exp)) (assign
continue (label ev-appl-did-operator)) (goto (label eval-dispatch))
:::

Upon returning from evaluating the operator subexpression, we proceed to
evaluate the operands of the combination and to accumulate the resulting
arguments in a list, held in `argl`. First we restore the unevaluated
operands and the environment. We initialize `argl` to an empty list.
Then we assign to the `proc` register the procedure that was produced by
evaluating the operator. If there are no operands, we go directly to
`apply/dispatch`. Otherwise we save `proc` on the stack and start the
argument-evaluation loop:[^307]

::: scheme
ev-appl-did-operator (restore unev)  [; the operands]{.roman}  (restore
env) (assign argl (op empty-arglist)) (assign proc (reg val))  [; the
operator]{.roman}  (test (op no-operands?) (reg unev)) (branch (label
apply-dispatch)) (save proc)
:::

Each cycle of the argument-evaluation loop evaluates an operand from the
list in `unev` and accumulates the result into `argl`. To evaluate an
operand, we place it in the `exp` register and go to `eval/dispatch`,
after setting `continue` so that execution will resume with the
argument-accumulation phase. But first we save the arguments accumulated
so far (held in `argl`), the environment (held in `env`), and the
remaining operands to be evaluated (held in `unev`). A special case is
made for the evaluation of the last operand, which is handled at
`ev/appl/last/arg`.

::: scheme
ev-appl-operand-loop (save argl) (assign exp (op first-operand) (reg
unev)) (test (op last-operand?) (reg unev)) (branch (label
ev-appl-last-arg)) (save env) (save unev) (assign continue (label
ev-appl-accumulate-arg)) (goto (label eval-dispatch))
:::

When an operand has been evaluated, the value is accumulated into the
list held in `argl`. The operand is then removed from the list of
unevaluated operands in `unev`, and the argument-evaluation continues.

::: scheme
ev-appl-accumulate-arg (restore unev) (restore env) (restore argl)
(assign argl (op adjoin-arg) (reg val) (reg argl)) (assign unev (op
rest-operands) (reg unev)) (goto (label ev-appl-operand-loop))
:::

Evaluation of the last argument is handled differently. There is no need
to save the environment or the list of unevaluated operands before going
to `eval/dispatch`, since they will not be required after the last
operand is evaluated. Thus, we return from the evaluation to a special
entry point `ev/appl/accum/last/arg`, which restores the argument list,
accumulates the new argument, restores the saved procedure, and goes off
to perform the application.[^308]

::: scheme
ev-appl-last-arg (assign continue (label ev-appl-accum-last-arg)) (goto
(label eval-dispatch)) ev-appl-accum-last-arg (restore argl) (assign
argl (op adjoin-arg) (reg val) (reg argl)) (restore proc) (goto (label
apply-dispatch))
:::

The details of the argument-evaluation loop determine the order in which
the interpreter evaluates the operands of a combination (e.g., left to
right or right to left---see [Exercise 3.8](#Exercise 3.8)). This order
is not determined by the metacircular evaluator, which inherits its
control structure from the underlying Scheme in which it is
implemented.[^309] Because the `first/operand` selector (used in
`ev/appl/operand/loop` to extract successive operands from `unev`) is
implemented as `car` and the `rest/operands` selector is implemented as
`cdr`, the explicit-control evaluator will evaluate the operands of a
combination in left-to-right order.

#### Procedure application {#procedure-application .unnumbered}

The entry point `apply/dispatch` corresponds to the `apply` procedure of
the metacircular evaluator. By the time we get to `apply/dispatch`, the
`proc` register contains the procedure to apply and `argl` contains the
list of evaluated arguments to which it must be applied. The saved value
of `continue` (originally passed to `eval/dispatch` and saved at
`ev/application`), which tells where to return with the result of the
procedure application, is on the stack. When the application is
complete, the controller transfers to the entry point specified by the
saved `continue`, with the result of the application in `val`. As with
the metacircular `apply`, there are two cases to consider. Either the
procedure to be applied is a primitive or it is a compound procedure.

::: scheme
apply-dispatch (test (op primitive-procedure?) (reg proc)) (branch
(label primitive-apply)) (test (op compound-procedure?) (reg proc))
(branch (label compound-apply)) (goto (label unknown-procedure-type))
:::

We assume that each primitive is implemented so as to obtain its
arguments from `argl` and place its result in `val`. To specify how the
machine handles primitives, we would have to provide a sequence of
controller instructions to implement each primitive and arrange for
`primitive/apply` to dispatch to the instructions for the primitive
identified by the contents of `proc`. Since we are interested in the
structure of the evaluation process rather than the details of the
primitives, we will instead just use an `apply/primitive/procedure`
operation that applies the procedure in `proc` to the arguments in
`argl`. For the purpose of simulating the evaluator with the simulator
of [Section 5.2](#Section 5.2) we use the procedure
`apply/primitive/procedure`, which calls on the underlying Scheme system
to perform the application, just as we did for the metacircular
evaluator in [Section 4.1.4](#Section 4.1.4). After computing the value
of the primitive application, we restore `continue` and go to the
designated entry point.

::: scheme
primitive-apply (assign val (op apply-primitive-procedure) (reg proc)
(reg argl)) (restore continue) (goto (reg continue))
:::

To apply a compound procedure, we proceed just as with the metacircular
evaluator. We construct a frame that binds the procedure's parameters to
the arguments, use this frame to extend the environment carried by the
procedure, and evaluate in this extended environment the sequence of
expressions that forms the body of the procedure. `ev/sequence`,
described below in [Section 5.4.2](#Section 5.4.2), handles the
evaluation of the sequence.

::: scheme
compound-apply (assign unev (op procedure-parameters) (reg proc))
(assign env (op procedure-environment) (reg proc)) (assign env (op
extend-environment) (reg unev) (reg argl) (reg env)) (assign unev (op
procedure-body) (reg proc)) (goto (label ev-sequence))
:::

`compound/apply` is the only place in the interpreter where the `env`
register is ever assigned a new value. Just as in the metacircular
evaluator, the new environment is constructed from the environment
carried by the procedure, together with the argument list and the
corresponding list of variables to be bound.

### Sequence Evaluation and Tail Recursion {#Section 5.4.2}

The portion of the explicit-control evaluator at `ev/sequence` is
analogous to the metacircular evaluator's `eval/sequence` procedure. It
handles sequences of expressions in procedure bodies or in explicit
`begin` expressions.

Explicit `begin` expressions are evaluated by placing the sequence of
expressions to be evaluated in `unev`, saving `continue` on the stack,
and jumping to `ev/sequence`.

::: scheme
ev-begin (assign unev (op begin-actions) (reg exp)) (save continue)
(goto (label ev-sequence))
:::

The implicit sequences in procedure bodies are handled by jumping to
`ev/sequence` from `compound/apply`, at which point `continue` is
already on the stack, having been saved at `ev/application`.

The entries at `ev/sequence` and `ev/sequence/continue` form a loop that
successively evaluates each expression in a sequence. The list of
unevaluated expressions is kept in `unev`. Before evaluating each
expression, we check to see if there are additional expressions to be
evaluated in the sequence. If so, we save the rest of the unevaluated
expressions (held in `unev`) and the environment in which these must be
evaluated (held in `env`) and call `eval/dispatch` to evaluate the
expression. The two saved registers are restored upon the return from
this evaluation, at `ev/sequence/continue`.

The final expression in the sequence is handled differently, at the
entry point `ev/sequence/last/exp`. Since there are no more expressions
to be evaluated after this one, we need not save `unev` or `env` before
going to `eval/dispatch`. The value of the whole sequence is the value
of the last expression, so after the evaluation of the last expression
there is nothing left to do except continue at the entry point currently
held on the stack (which was saved by `ev/application` or `ev/begin`.)
Rather than setting up `continue` to arrange for `eval/dispatch` to
return here and then restoring `continue` from the stack and continuing
at that entry point, we restore `continue` from the stack before going
to `eval/dispatch`, so that `eval/dispatch` will continue at that entry
point after evaluating the expression.

::: scheme
ev-sequence (assign exp (op first-exp) (reg unev)) (test (op last-exp?)
(reg unev)) (branch (label ev-sequence-last-exp)) (save unev) (save env)
(assign continue (label ev-sequence-continue)) (goto (label
eval-dispatch)) ev-sequence-continue (restore env) (restore unev)
(assign unev (op rest-exps) (reg unev)) (goto (label ev-sequence))
ev-sequence-last-exp (restore continue) (goto (label eval-dispatch))
:::

#### Tail recursion {#tail-recursion .unnumbered}

In [Chapter 1](#Chapter 1) we said that the process described by a
procedure such as

::: scheme
(define (sqrt-iter guess x) (if (good-enough? guess x) guess (sqrt-iter
(improve guess x) x)))
:::

is an iterative process. Even though the procedure is syntactically
recursive (defined in terms of itself), it is not logically necessary
for an evaluator to save information in passing from one call to
`sqrt/iter` to the next.[^310] An evaluator that can execute a procedure
such as `sqrt/iter` without requiring increasing storage as the
procedure continues to call itself is called a *tail-recursive*
evaluator. The metacircular implementation of the evaluator in [Chapter
4](#Chapter 4) does not specify whether the evaluator is tail-recursive,
because that evaluator inherits its mechanism for saving state from the
underlying Scheme. With the explicit-control evaluator, however, we can
trace through the evaluation process to see when procedure calls cause a
net accumulation of information on the stack.

Our evaluator is tail-recursive, because in order to evaluate the final
expression of a sequence we transfer directly to `eval/dispatch` without
saving any information on the stack. Hence, evaluating the final
expression in a sequence---even if it is a procedure call (as in
`sqrt/iter`, where the `if` expression, which is the last expression in
the procedure body, reduces to a call to `sqrt/iter`)---will not cause
any information to be accumulated on the stack.[^311]

If we did not think to take advantage of the fact that it was
unnecessary to save information in this case, we might have implemented
`eval/sequence` by treating all the expressions in a sequence in the
same way---saving the registers, evaluating the expression, returning to
restore the registers, and repeating this until all the expressions have
been evaluated:[^312]

::: scheme
ev-sequence (test (op no-more-exps?) (reg unev)) (branch (label
ev-sequence-end)) (assign exp (op first-exp) (reg unev)) (save unev)
(save env) (assign continue (label ev-sequence-continue)) (goto (label
eval-dispatch)) ev-sequence-continue (restore env) (restore unev)
(assign unev (op rest-exps) (reg unev)) (goto (label ev-sequence))
ev-sequence-end (restore continue) (goto (reg continue))
:::

This may seem like a minor change to our previous code for evaluation of
a sequence: The only difference is that we go through the save-restore
cycle for the last expression in a sequence as well as for the others.
The interpreter will still give the same value for any expression. But
this change is fatal to the tail-recursive implementation, because we
must now return after evaluating the final expression in a sequence in
order to undo the (useless) register saves. These extra saves will
accumulate during a nest of procedure calls. Consequently, processes
such as `sqrt/iter` will require space proportional to the number of
iterations rather than requiring constant space. This difference can be
significant. For example, with tail recursion, an infinite loop can be
expressed using only the procedure-call mechanism:

::: scheme
(define (count n) (newline) (display n) (count (+ n 1)))
:::

Without tail recursion, such a procedure would eventually run out of
stack space, and expressing a true iteration would require some control
mechanism other than procedure call.

### Conditionals, Assignments, and Definitions {#Section 5.4.3}

As with the metacircular evaluator, special forms are handled by
selectively evaluating fragments of the expression. For an `if`
expression, we must evaluate the predicate and decide, based on the
value of predicate, whether to evaluate the consequent or the
alternative.

Before evaluating the predicate, we save the `if` expression itself so
that we can later extract the consequent or alternative. We also save
the environment, which we will need later in order to evaluate the
consequent or the alternative, and we save `continue`, which we will
need later in order to return to the evaluation of the expression that
is waiting for the value of the `if`.

::: scheme
ev-if (save exp)  [; save expression for later]{.roman}  (save env)
(save continue) (assign continue (label ev-if-decide)) (assign exp (op
if-predicate) (reg exp)) (goto (label eval-dispatch))  [; evaluate the
predicate]{.roman}
:::

When we return from evaluating the predicate, we test whether it was
true or false and, depending on the result, place either the consequent
or the alternative in `exp` before going to `eval/dispatch`. Notice that
restoring `env` and `continue` here sets up `eval/dispatch` to have the
correct environment and to continue at the right place to receive the
value of the `if` expression.

::: scheme
ev-if-decide (restore continue) (restore env) (restore exp) (test (op
true?) (reg val)) (branch (label ev-if-consequent)) ev-if-alternative
(assign exp (op if-alternative) (reg exp)) (goto (label eval-dispatch))
ev-if-consequent (assign exp (op if-consequent) (reg exp)) (goto (label
eval-dispatch))
:::

#### Assignments and definitions {#assignments-and-definitions-1 .unnumbered}

Assignments are handled by `ev/assignment`, which is reached from
`eval/dispatch` with the assignment expression in `exp`. The code at
`ev/assignment` first evaluates the value part of the expression and
then installs the new value in the environment. `set/variable/value!` is
assumed to be available as a machine operation.

::: scheme
ev-assignment (assign unev (op assignment-variable) (reg exp)) (save
unev)  [; save variable for later]{.roman}  (assign exp (op
assignment-value) (reg exp)) (save env) (save continue) (assign continue
(label ev-assignment-1)) (goto (label eval-dispatch))  [; evaluate the
assignment value]{.roman}  ev-assignment-1 (restore continue) (restore
env) (restore unev) (perform (op set-variable-value!) (reg unev) (reg
val) (reg env)) (assign val (const ok)) (goto (reg continue))
:::

Definitions are handled in a similar way:

::: scheme
ev-definition (assign unev (op definition-variable) (reg exp)) (save
unev)  [; save variable for later]{.roman}  (assign exp (op
definition-value) (reg exp)) (save env) (save continue) (assign continue
(label ev-definition-1)) (goto (label eval-dispatch))  [; evaluate the
definition value]{.roman}  ev-definition-1 (restore continue) (restore
env) (restore unev) (perform (op define-variable!) (reg unev) (reg val)
(reg env)) (assign val (const ok)) (goto (reg continue))
:::

> **[]{#Exercise 5.23 label="Exercise 5.23"}Exercise 5.23:** Extend the
> evaluator to handle derived expressions such as `cond`, `let`, and so
> on ([Section 4.1.2](#Section 4.1.2)). You may "cheat" and assume that
> the syntax transformers such as `cond/>if` are available as machine
> operations.[^313]

> **[]{#Exercise 5.24 label="Exercise 5.24"}Exercise 5.24:** Implement
> `cond` as a new basic special form without reducing it to `if`. You
> will have to construct a loop that tests the predicates of successive
> `cond` clauses until you find one that is true, and then use
> `ev/sequence` to evaluate the actions of the clause.

> **[]{#Exercise 5.25 label="Exercise 5.25"}Exercise 5.25:** Modify the
> evaluator so that it uses normal-order evaluation, based on the lazy
> evaluator of [Section 4.2](#Section 4.2).

### Running the Evaluator {#Section 5.4.4}

With the implementation of the explicit-control evaluator we come to the
end of a development, begun in [Chapter 1](#Chapter 1), in which we have
explored successively more precise models of the evaluation process. We
started with the relatively informal substitution model, then extended
this in [Chapter 3](#Chapter 3) to the environment model, which enabled
us to deal with state and change. In the metacircular evaluator of
[Chapter 4](#Chapter 4), we used Scheme itself as a language for making
more explicit the environment structure constructed during evaluation of
an expression. Now, with register machines, we have taken a close look
at the evaluator's mechanisms for storage management, argument passing,
and control. At each new level of description, we have had to raise
issues and resolve ambiguities that were not apparent at the previous,
less precise treatment of evaluation. To understand the behavior of the
explicit-control evaluator, we can simulate it and monitor its
performance.

We will install a driver loop in our evaluator machine. This plays the
role of the `driver/loop` procedure of [Section 4.1.4](#Section 4.1.4).
The evaluator will repeatedly print a prompt, read an expression,
evaluate the expression by going to `eval/dispatch`, and print the
result. The following instructions form the beginning of the
explicit-control evaluator's controller sequence:[^314]

::: scheme
read-eval-print-loop (perform (op initialize-stack)) (perform (op
prompt-for-input) (const \";;EC-Eval input:\")) (assign exp (op read))
(assign env (op get-global-environment)) (assign continue (label
print-result)) (goto (label eval-dispatch)) print-result (perform (op
announce-output) (const \";;EC-Eval value:\")) (perform (op user-print)
(reg val)) (goto (label read-eval-print-loop))
:::

When we encounter an error in a procedure (such as the "unknown
procedure type error" indicated at `apply/dispatch`), we print an error
message and return to the driver loop.[^315]

::: scheme
unknown-expression-type (assign val (const
unknown-expression-type-error)) (goto (label signal-error))
unknown-procedure-type (restore continue)  [; clean up stack (from
`apply/dispatch`)]{.roman}  (assign val (const
unknown-procedure-type-error)) (goto (label signal-error)) signal-error
(perform (op user-print) (reg val)) (goto (label read-eval-print-loop))
:::

For the purposes of the simulation, we initialize the stack each time
through the driver loop, since it might not be empty after an error
(such as an undefined variable) interrupts an evaluation.[^316]

If we combine all the code fragments presented in [Section
5.4.1](#Section 5.4.1)--[Section 5.4.4](#Section 5.4.4), we can create
an evaluator machine model that we can run using the register-machine
simulator of [Section 5.2](#Section 5.2).

::: scheme
(define eceval (make-machine '(exp env val proc argl continue unev)
eceval-operations '(read-eval-print-loop
 $\color{SchemeDark}\langle$  *entire machine controller as given
above*  $\color{SchemeDark}\rangle$  )))
:::

We must define Scheme procedures to simulate the operations used as
primitives by the evaluator. These are the same procedures we used for
the metacircular evaluator in [Section 4.1](#Section 4.1), together with
the few additional ones defined in footnotes throughout [Section
5.4](#Section 5.4).

::: scheme
(define eceval-operations (list (list 'self-evaluating? self-evaluating)
 $\color{SchemeDark}\langle$  *complete list of operations for eceval
machine*  $\color{SchemeDark}\rangle$ ))
:::

Finally, we can initialize the global environment and run the evaluator:

::: scheme
(define the-global-environment (setup-environment)) (start eceval)  *;;;
EC-Eval input:*  (define (append x y) (if (null? x) y (cons (car x)
(append (cdr x) y))))  *;;; EC-Eval value:*   *ok*   *;;; EC-Eval
input:*  (append '(a b c) '(d e f))  *;;; EC-Eval value:*   *(a b c d e
f)*
:::

Of course, evaluating expressions in this way will take much longer than
if we had directly typed them into Scheme, because of the multiple
levels of simulation involved. Our expressions are evaluated by the
explicit-control-evaluator machine, which is being simulated by a Scheme
program, which is itself being evaluated by the Scheme interpreter.

#### Monitoring the performance of the evaluator {#monitoring-the-performance-of-the-evaluator .unnumbered}

Simulation can be a powerful tool to guide the implementation of
evaluators. Simulations make it easy not only to explore variations of
the register-machine design but also to monitor the performance of the
simulated evaluator. For example, one important factor in performance is
how efficiently the evaluator uses the stack. We can observe the number
of stack operations required to evaluate various expressions by defining
the evaluator register machine with the version of the simulator that
collects statistics on stack use ([Section 5.2.4](#Section 5.2.4)), and
adding an instruction at the evaluator's `print/result` entry point to
print the statistics:

::: scheme
print-result (perform (op print-stack-statistics))  [; added
instruction]{.roman}  (perform (op announce-output) (const \";;; EC-Eval
value:\"))  $\dots$   [; same as before]{.roman}
:::

Interactions with the evaluator now look like this:

::: scheme
 *;;; EC-Eval input:*  (define (factorial n) (if (= n 1) 1 (\*
(factorial (- n 1)) n)))  *(total-pushes = 3 maximum-depth = 3)*   *;;;
EC-Eval value:*   *ok*   *;;; EC-Eval input:*  (factorial 5)
 *(total-pushes = 144 maximum-depth = 28)*   *;;; EC-Eval value:*
 *120*
:::

Note that the driver loop of the evaluator reinitializes the stack at
the start of each interaction, so that the statistics printed will refer
only to stack operations used to evaluate the previous expression.

> **[]{#Exercise 5.26 label="Exercise 5.26"}Exercise 5.26:** Use the
> monitored stack to explore the tail-recursive property of the
> evaluator ([Section 5.4.2](#Section 5.4.2)). Start the evaluator and
> define the iterative `factorial` procedure from [Section
> 1.2.1](#Section 1.2.1):
>
> ::: scheme
> (define (factorial n) (define (iter product counter) (if (\> counter
> n) product (iter (\* counter product) (+ counter 1)))) (iter 1 1))
> :::
>
> Run the procedure with some small values of $n$. Record the maximum
> stack depth and the number of pushes required to compute $n!$ for each
> of these values.
>
> a.  You will find that the maximum depth required to evaluate $n!$ is
>     independent of $n$. What is that depth?
>
> b.  Determine from your data a formula in terms of $n$ for the total
>     number of push operations used in evaluating $n!$ for any
>     $n \ge 1$. Note that the number of operations used is a linear
>     function of $n$ and is thus determined by two constants.

> **[]{#Exercise 5.27 label="Exercise 5.27"}Exercise 5.27:** For
> comparison with [Exercise 5.26](#Exercise 5.26), explore the behavior
> of the following procedure for computing factorials recursively:
>
> ::: scheme
> (define (factorial n) (if (= n 1) 1 (\* (factorial (- n 1)) n)))
> :::
>
> By running this procedure with the monitored stack, determine, as a
> function of $n$, the maximum depth of the stack and the total number
> of pushes used in evaluating $n!$ for $n \ge 1$. (Again, these
> functions will be linear.) Summarize your experiments by filling in
> the following table with the appropriate expressions in terms of $n$:
>
> $$\vbox{
> \offinterlineskip
> \halign{
> \strut  \hfil \quad #\quad \hfil & \vrule
>     \hfil \quad #\quad \hfil & \vrule
>     \hfil \quad #\quad \hfil \cr
>
>                 & Maximum depth & Number of pushes \cr
>     \noalign{\hrule}
>     Recursive       &       &  \cr
>     factorial       &       &  \cr
>     \noalign{\hrule}
>     Iterative       &       &  \cr
>     factorial       &       &  \cr
> }
> }$$
>
> The maximum depth is a measure of the amount of space used by the
> evaluator in carrying out the computation, and the number of pushes
> correlates well with the time required.

> **[]{#Exercise 5.28 label="Exercise 5.28"}Exercise 5.28:** Modify the
> definition of the evaluator by changing `eval/sequence` as described
> in [Section 5.4.2](#Section 5.4.2) so that the evaluator is no longer
> tail-recursive. Rerun your experiments from [Exercise
> 5.26](#Exercise 5.26) and [Exercise 5.27](#Exercise 5.27) to
> demonstrate that both versions of the `factorial` procedure now
> require space that grows linearly with their input.

> **[]{#Exercise 5.29 label="Exercise 5.29"}Exercise 5.29:** Monitor the
> stack operations in the tree-recursive Fibonacci computation:
>
> ::: scheme
> (define (fib n) (if (\< n 2) n (+ (fib (- n 1)) (fib (- n 2)))))
> :::
>
> a.  Give a formula in terms of $n$ for the maximum depth of the stack
>     required to compute ${\rm Fib}(n)$ for $n \ge 2$. Hint: In
>     [Section 1.2.2](#Section 1.2.2) we argued that the space used by
>     this process grows linearly with $n$.
>
> b.  Give a formula for the total number of pushes used to compute
>     ${\rm Fib}(n)$ for $n \ge 2$. You should find that the number of
>     pushes (which correlates well with the time used) grows
>     exponentially with $n$. Hint: Let $S(n)$ be the number of pushes
>     used in computing ${\rm Fib}(n)$. You should be able to argue that
>     there is a formula that expresses $S(n)$ in terms of $S(n - 1)$,
>     $S(n - 2)$, and some fixed "overhead" constant $k$ that is
>     independent of $n$. Give the formula, and say what $k$ is. Then
>     show that $S(n)$ can be expressed as $a\cdot{\rm Fib}(n + 1) + b$
>     and give the values of $a$ and $b$.

> **[]{#Exercise 5.30 label="Exercise 5.30"}Exercise 5.30:** Our
> evaluator currently catches and signals only two kinds of
> errors---unknown expression types and unknown procedure types. Other
> errors will take us out of the evaluator read-eval-print loop. When we
> run the evaluator using the register-machine simulator, these errors
> are caught by the underlying Scheme system. This is analogous to the
> computer crashing when a user program makes an error.[^317] It is a
> large project to make a real error system work, but it is well worth
> the effort to understand what is involved here.
>
> a.  Errors that occur in the evaluation process, such as an attempt to
>     access an unbound variable, could be caught by changing the lookup
>     operation to make it return a distinguished condition code, which
>     cannot be a possible value of any user variable. The evaluator can
>     test for this condition code and then do what is necessary to go
>     to `signal/error`. Find all of the places in the evaluator where
>     such a change is necessary and fix them. This is lots of work.
>
> b.  Much worse is the problem of handling errors that are signaled by
>     applying primitive procedures, such as an attempt to divide by
>     zero or an attempt to extract the `car` of a symbol. In a
>     professionally written high-quality system, each primitive
>     application is checked for safety as part of the primitive. For
>     example, every call to `car` could first check that the argument
>     is a pair. If the argument is not a pair, the application would
>     return a distinguished condition code to the evaluator, which
>     would then report the failure. We could arrange for this in our
>     register-machine simulator by making each primitive procedure
>     check for applicability and returning an appropriate distinguished
>     condition code on failure. Then the `primitive/apply` code in the
>     evaluator can check for the condition code and go to
>     `signal/error` if necessary. Build this structure and make it
>     work. This is a major project.

## Compilation {#Section 5.5}

The explicit-control evaluator of [Section 5.4](#Section 5.4) is a
register machine whose controller interprets Scheme programs. In this
section we will see how to run Scheme programs on a register machine
whose controller is not a Scheme interpreter.

The explicit-control evaluator machine is universal---it can carry out
any computational process that can be described in Scheme. The
evaluator's controller orchestrates the use of its data paths to perform
the desired computation. Thus, the evaluator's data paths are universal:
They are sufficient to perform any computation we desire, given an
appropriate controller.[^318]

Commercial general-purpose computers are register machines organized
around a collection of registers and operations that constitute an
efficient and convenient universal set of data paths. The controller for
a general-purpose machine is an interpreter for a register-machine
language like the one we have been using. This language is called the
*native language* of the machine, or simply *machine language*. Programs
written in machine language are sequences of instructions that use the
machine's data paths. For example, the explicit-control evaluator's
instruction sequence can be thought of as a machine-language program for
a general-purpose computer rather than as the controller for a
specialized interpreter machine.

There are two common strategies for bridging the gap between
higher-level languages and register-machine languages. The
explicit-control evaluator illustrates the strategy of interpretation.
An interpreter written in the native language of a machine configures
the machine to execute programs written in a language (called the
*source language*) that may differ from the native language of the
machine performing the evaluation. The primitive procedures of the
source language are implemented as a library of subroutines written in
the native language of the given machine. A program to be interpreted
(called the *source program*) is represented as a data structure. The
interpreter traverses this data structure, analyzing the source program.
As it does so, it simulates the intended behavior of the source program
by calling appropriate primitive subroutines from the library.

In this section, we explore the alternative strategy of *compilation*. A
compiler for a given source language and machine translates a source
program into an equivalent program (called the *object program*) written
in the machine's native language. The compiler that we implement in this
section translates programs written in Scheme into sequences of
instructions to be executed using the explicit-control evaluator
machine's data paths.[^319]

Compared with interpretation, compilation can provide a great increase
in the efficiency of program execution, as we will explain below in the
overview of the compiler. On the other hand, an interpreter provides a
more powerful environment for interactive program development and
debugging, because the source program being executed is available at run
time to be examined and modified. In addition, because the entire
library of primitives is present, new programs can be constructed and
added to the system during debugging.

In view of the complementary advantages of compilation and
interpretation, modern program-development environments pursue a mixed
strategy. Lisp interpreters are generally organized so that interpreted
procedures and compiled procedures can call each other. This enables a
programmer to compile those parts of a program that are assumed to be
debugged, thus gaining the efficiency advantage of compilation, while
retaining the interpretive mode of execution for those parts of the
program that are in the flux of interactive development and debugging.
In [Section 5.5.7](#Section 5.5.7), after we have implemented the
compiler, we will show how to interface it with our interpreter to
produce an integrated interpreter-compiler development system.

#### An overview of the compiler {#an-overview-of-the-compiler .unnumbered}

Our compiler is much like our interpreter, both in its structure and in
the function it performs. Accordingly, the mechanisms used by the
compiler for analyzing expressions will be similar to those used by the
interpreter. Moreover, to make it easy to interface compiled and
interpreted code, we will design the compiler to generate code that
obeys the same conventions of register usage as the interpreter: The
environment will be kept in the `env` register, argument lists will be
accumulated in `argl`, a procedure to be applied will be in `proc`,
procedures will return their answers in `val`, and the location to which
a procedure should return will be kept in `continue`. In general, the
compiler translates a source program into an object program that
performs essentially the same register operations as would the
interpreter in evaluating the same source program.

This description suggests a strategy for implementing a rudimentary
compiler: We traverse the expression in the same way the interpreter
does. When we encounter a register instruction that the interpreter
would perform in evaluating the expression, we do not execute the
instruction but instead accumulate it into a sequence. The resulting
sequence of instructions will be the object code. Observe the efficiency
advantage of compilation over interpretation. Each time the interpreter
evaluates an expression---for example, `(f 84 96)`---it performs the
work of classifying the expression (discovering that this is a procedure
application) and testing for the end of the operand list (discovering
that there are two operands). With a compiler, the expression is
analyzed only once, when the instruction sequence is generated at
compile time. The object code produced by the compiler contains only the
instructions that evaluate the operator and the two operands, assemble
the argument list, and apply the procedure (in `proc`) to the arguments
(in `argl`).

This is the same kind of optimization we implemented in the analyzing
evaluator of [Section 4.1.7](#Section 4.1.7). But there are further
opportunities to gain efficiency in compiled code. As the interpreter
runs, it follows a process that must be applicable to any expression in
the language. In contrast, a given segment of compiled code is meant to
execute some particular expression. This can make a big difference, for
example in the use of the stack to save registers. When the interpreter
evaluates an expression, it must be prepared for any contingency. Before
evaluating a subexpression, the interpreter saves all registers that
will be needed later, because the subexpression might require an
arbitrary evaluation. A compiler, on the other hand, can exploit the
structure of the particular expression it is processing to generate code
that avoids unnecessary stack operations.

As a case in point, consider the combination `(f 84 96)`. Before the
interpreter evaluates the operator of the combination, it prepares for
this evaluation by saving the registers containing the operands and the
environment, whose values will be needed later. The interpreter then
evaluates the operator to obtain the result in `val`, restores the saved
registers, and finally moves the result from `val` to `proc`. However,
in the particular expression we are dealing with, the operator is the
symbol `f`, whose evaluation is accomplished by the machine operation
`lookup/variable/value`, which does not alter any registers. The
compiler that we implement in this section will take advantage of this
fact and generate code that evaluates the operator using the instruction

::: scheme
(assign proc (op lookup-variable-value) (const f) (reg env))
:::

This code not only avoids the unnecessary saves and restores but also
assigns the value of the lookup directly to `proc`, whereas the
interpreter would obtain the result in `val` and then move this to
`proc`.

A compiler can also optimize access to the environment. Having analyzed
the code, the compiler can in many cases know in which frame a
particular variable will be located and access that frame directly,
rather than performing the `lookup/variable/value` search. We will
discuss how to implement such variable access in [Section
5.5.6](#Section 5.5.6). Until then, however, we will focus on the kind
of register and stack optimizations described above. There are many
other optimizations that can be performed by a compiler, such as coding
primitive operations "in line" instead of using a general `apply`
mechanism (see [Exercise 5.38](#Exercise 5.38)); but we will not
emphasize these here. Our main goal in this section is to illustrate the
compilation process in a simplified (but still interesting) context.

### Structure of the Compiler {#Section 5.5.1}

In [Section 4.1.7](#Section 4.1.7) we modified our original metacircular
interpreter to separate analysis from execution. We analyzed each
expression to produce an execution procedure that took an environment as
argument and performed the required operations. In our compiler, we will
do essentially the same analysis. Instead of producing execution
procedures, however, we will generate sequences of instructions to be
run by our register machine.

The procedure `compile` is the top-level dispatch in the compiler. It
corresponds to the `eval` procedure of [Section 4.1.1](#Section 4.1.1),
the `analyze` procedure of [Section 4.1.7](#Section 4.1.7), and the
`eval/dispatch` entry point of the explicit-control-evaluator in
[Section 5.4.1](#Section 5.4.1). The compiler, like the interpreters,
uses the expression-syntax procedures defined in [Section
4.1.2](#Section 4.1.2).[^320] `compile` performs a case analysis on the
syntactic type of the expression to be compiled. For each type of
expression, it dispatches to a specialized *code generator*:

::: scheme
(define (compile exp target linkage) (cond ((self-evaluating? exp)
(compile-self-evaluating exp target linkage)) ((quoted? exp)
(compile-quoted exp target linkage)) ((variable? exp) (compile-variable
exp target linkage)) ((assignment? exp) (compile-assignment exp target
linkage)) ((definition? exp) (compile-definition exp target linkage))
((if? exp) (compile-if exp target linkage)) ((lambda? exp)
(compile-lambda exp target linkage)) ((begin? exp) (compile-sequence
(begin-actions exp) target linkage)) ((cond? exp) (compile (cond-\>if
exp) target linkage)) ((application? exp) (compile-application exp
target linkage)) (else (error \"Unknown expression type: COMPILE\"
exp))))
:::

#### Targets and linkages {#targets-and-linkages .unnumbered}

`compile` and the code generators that it calls take two arguments in
addition to the expression to compile. There is a *target*, which
specifies the register in which the compiled code is to return the value
of the expression. There is also a *linkage descriptor*, which describes
how the code resulting from the compilation of the expression should
proceed when it has finished its execution. The linkage descriptor can
require that the code do one of the following three things:

-   continue at the next instruction in sequence (this is specified by
    the linkage descriptor `next`),

-   return from the procedure being compiled (this is specified by the
    linkage descriptor `return`), or

-   jump to a named entry point (this is specified by using the
    designated label as the linkage descriptor).

For example, compiling the expression `5` (which is self-evaluating)
with a target of the `val` register and a linkage of `next` should
produce the instruction

::: scheme
(assign val (const 5))
:::

Compiling the same expression with a linkage of `return` should produce
the instructions

::: scheme
(assign val (const 5)) (goto (reg continue))
:::

In the first case, execution will continue with the next instruction in
the sequence. In the second case, we will return from a procedure call.
In both cases, the value of the expression will be placed into the
target `val` register.

#### Instruction sequences and stack usage {#instruction-sequences-and-stack-usage .unnumbered}

Each code generator returns an *instruction sequence* containing the
object code it has generated for the expression. Code generation for a
compound expression is accomplished by combining the output from simpler
code generators for component expressions, just as evaluation of a
compound expression is accomplished by evaluating the component
expressions.

The simplest method for combining instruction sequences is a procedure
called `append/instruction/sequences`. It takes as arguments any number
of instruction sequences that are to be executed sequentially; it
appends them and returns the combined sequence. That is, if
$\langle$*seq*$_1\rangle$ and $\langle$*seq*$_2\rangle$ are sequences of
instructions, then evaluating

::: scheme
(append-instruction-sequences
 $\color{SchemeDark}\langle$  *seq*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *seq*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ )
:::

produces the sequence

::: scheme
 $\color{SchemeDark}\langle$  *seq*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *seq*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$
:::

Whenever registers might need to be saved, the compiler's code
generators use `preserving`, which is a more subtle method for combining
instruction sequences. `preserving` takes three arguments: a set of
registers and two instruction sequences that are to be executed
sequentially. It appends the sequences in such a way that the contents
of each register in the set is preserved over the execution of the first
sequence, if this is needed for the execution of the second sequence.
That is, if the first sequence modifies the register and the second
sequence actually needs the register's original contents, then
`preserving` wraps a `save` and a `restore` of the register around the
first sequence before appending the sequences. Otherwise, `preserving`
simply returns the appended instruction sequences. Thus, for example,

::: scheme
(preserving (list
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *reg*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ )
 $\color{SchemeDark}\langle$  *seq*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 1}}\rangle$
 $\color{SchemeDark}\langle$  *seq*  $\color{SchemeDark}_{\hbox{\ttfamily\scriptsize 2}}\rangle$ )
:::

produces one of the following four sequences of instructions, depending
on how $\langle$*seq*$_1\rangle$ and $\langle$*seq*$_2\rangle$ use
$\langle$*reg*$_1\rangle$ and $\langle$*reg*$_2\rangle$:

$$\vbox{
\offinterlineskip
\halign{
\strut  \kern0.8em # \kern0.4em \hfil & \vrule
    \kern0.8em # \kern0.4em \hfil & \vrule
    \kern0.8em # \kern0.4em \hfil & \vrule
    \kern0.8em # \kern0.4em \hfil \cr

  $\langle{\mathit{seq}_1}\rangle$
& \hbox{\tt (save} $\langle{\mathit{reg}_1}\rangle${\tt)}
& \hbox{\tt (save} $\langle{\mathit{reg}_2}\rangle${\tt)}
& \hbox{\tt (save} $\langle{\mathit{reg}_2}\rangle${\tt)} \cr

  $\langle{\mathit{seq}_2}\rangle$
& $\langle{\mathit{seq}_1}\rangle$
& $\langle{\mathit{seq}_1}\rangle$
& \hbox{\tt (save} $\langle{\mathit{reg}_1}\rangle${\tt)} \cr

& \hbox{\tt (restore} $\langle{\mathit{reg}_1}\rangle${\tt)}
& \hbox{\tt (restore} $\langle{\mathit{reg}_2}\rangle${\tt)}
& $\langle{\mathit{seq}_1}\rangle$ \cr

& $\langle{\mathit{seq}_2}\rangle$
& $\langle{\mathit{seq}_2}\rangle$
& \hbox{\tt (restore} $\langle{\mathit{reg}_1}\rangle${\tt)} \cr

&  &
& \hbox{\tt (restore} $\langle{\mathit{reg}_2}\rangle${\tt)} \cr

&  &
& $\langle{\mathit{seq}_2}\rangle$ \cr
}
}$$

By using `preserving` to combine instruction sequences the compiler
avoids unnecessary stack operations. This also isolates the details of
whether or not to generate `save` and `restore` instructions within the
`preserving` procedure, separating them from the concerns that arise in
writing each of the individual code generators. In fact no `save` or
`restore` instructions are explicitly produced by the code generators.

In principle, we could represent an instruction sequence simply as a
list of instructions. `append/instruction/sequences` could then combine
instruction sequences by performing an ordinary list `append`. However,
`preserving` would then be a complex operation, because it would have to
analyze each instruction sequence to determine how the sequence uses its
registers. `preserving` would be inefficient as well as complex, because
it would have to analyze each of its instruction sequence arguments,
even though these sequences might themselves have been constructed by
calls to `preserving`, in which case their parts would have already been
analyzed. To avoid such repetitious analysis we will associate with each
instruction sequence some information about its register use. When we
construct a basic instruction sequence we will provide this information
explicitly, and the procedures that combine instruction sequences will
derive register-use information for the combined sequence from the
information associated with the component sequences.

An instruction sequence will contain three pieces of information:

-   the set of registers that must be initialized before the
    instructions in the sequence are executed (these registers are said
    to be *needed* by the sequence),

-   the set of registers whose values are modified by the instructions
    in the sequence, and

-   the actual instructions (also called *statements*) in the sequence.

We will represent an instruction sequence as a list of its three parts.
The constructor for instruction sequences is thus

::: scheme
(define (make-instruction-sequence needs modifies statements) (list
needs modifies statements))
:::

For example, the two-instruction sequence that looks up the value of the
variable `x` in the current environment, assigns the result to `val`,
and then returns, requires registers `env` and `continue` to have been
initialized, and modifies register `val`. This sequence would therefore
be constructed as

::: scheme
(make-instruction-sequence '(env continue) '(val) '((assign val (op
lookup-variable-value) (const x) (reg env)) (goto (reg continue))))
:::

We sometimes need to construct an instruction sequence with no
statements:

::: scheme
(define (empty-instruction-sequence) (make-instruction-sequence '() '()
'()))
:::

The procedures for combining instruction sequences are shown in [Section
5.5.4](#Section 5.5.4).

> **[]{#Exercise 5.31 label="Exercise 5.31"}Exercise 5.31:** In
> evaluating a procedure application, the explicit-control evaluator
> always saves and restores the `env` register around the evaluation of
> the operator, saves and restores `env` around the evaluation of each
> operand (except the final one), saves and restores `argl` around the
> evaluation of each operand, and saves and restores `proc` around the
> evaluation of the operand sequence. For each of the following
> combinations, say which of these `save` and `restore` operations are
> superfluous and thus could be eliminated by the compiler's
> `preserving` mechanism:
>
> ::: scheme
> (f 'x 'y) ((f) 'x 'y) (f (g 'x) y) (f (g 'x) 'y)
> :::

> **[]{#Exercise 5.32 label="Exercise 5.32"}Exercise 5.32:** Using the
> `preserving` mechanism, the compiler will avoid saving and restoring
> `env` around the evaluation of the operator of a combination in the
> case where the operator is a symbol. We could also build such
> optimizations into the evaluator. Indeed, the explicit-control
> evaluator of [Section 5.4](#Section 5.4) already performs a similar
> optimization, by treating combinations with no operands as a special
> case.
>
> a.  Extend the explicit-control evaluator to recognize as a separate
>     class of expressions combinations whose operator is a symbol, and
>     to take advantage of this fact in evaluating such expressions.
>
> b.  Alyssa P. Hacker suggests that by extending the evaluator to
>     recognize more and more special cases we could incorporate all the
>     compiler's optimizations, and that this would eliminate the
>     advantage of compilation altogether. What do you think of this
>     idea?

### Compiling Expressions {#Section 5.5.2}

In this section and the next we implement the code generators to which
the `compile` procedure dispatches.

#### Compiling linkage code {#compiling-linkage-code .unnumbered}

In general, the output of each code generator will end with
instructions---generated by the procedure `compile/linkage`---that
implement the required linkage. If the linkage is `return` then we must
generate the instruction `(goto (reg continue))`. This needs the
`continue` register and does not modify any registers. If the linkage is
`next`, then we needn't include any additional instructions. Otherwise,
the linkage is a label, and we generate a `goto` to that label, an
instruction that does not need or modify any registers.[^321]

::: scheme
(define (compile-linkage linkage) (cond ((eq? linkage 'return)
(make-instruction-sequence '(continue) '() '((goto (reg continue)))))
((eq? linkage 'next) (empty-instruction-sequence)) (else
(make-instruction-sequence '() '() '((goto (label ,linkage)))))))
:::

The linkage code is appended to an instruction sequence by `preserving`
the `continue` register, since a `return` linkage will require the
`continue` register: If the given instruction sequence modifies
`continue` and the linkage code needs it, `continue` will be saved and
restored.

::: scheme
(define (end-with-linkage linkage instruction-sequence) (preserving
'(continue) instruction-sequence (compile-linkage linkage)))
:::

#### Compiling simple expressions {#compiling-simple-expressions .unnumbered}

The code generators for self-evaluating expressions, quotations, and
variables construct instruction sequences that assign the required value
to the target register and then proceed as specified by the linkage
descriptor.

::: scheme
(define (compile-self-evaluating exp target linkage) (end-with-linkage
linkage (make-instruction-sequence '() (list target) '((assign ,target
(const ,exp)))))) (define (compile-quoted exp target linkage)
(end-with-linkage linkage (make-instruction-sequence '() (list target)
'((assign ,target (const ,(text-of-quotation exp))))))) (define
(compile-variable exp target linkage) (end-with-linkage linkage
(make-instruction-sequence '(env) (list target) '((assign ,target (op
lookup-variable-value) (const ,exp) (reg env))))))
:::

All these assignment instructions modify the target register, and the
one that looks up a variable needs the `env` register.

Assignments and definitions are handled much as they are in the
interpreter. We recursively generate code that computes the value to be
assigned to the variable, and append to it a two-instruction sequence
that actually sets or defines the variable and assigns the value of the
whole expression (the symbol `ok`) to the target register. The recursive
compilation has target `val` and linkage `next` so that the code will
put its result into `val` and continue with the code that is appended
after it. The appending is done preserving `env`, since the environment
is needed for setting or defining the variable and the code for the
variable value could be the compilation of a complex expression that
might modify the registers in arbitrary ways.

::: scheme
(define (compile-assignment exp target linkage) (let ((var
(assignment-variable exp)) (get-value-code (compile (assignment-value
exp) 'val 'next))) (end-with-linkage linkage (preserving '(env)
get-value-code (make-instruction-sequence '(env val) (list target)
'((perform (op set-variable-value!) (const ,var) (reg val) (reg env))
(assign ,target (const ok)))))))) (define (compile-definition exp target
linkage) (let ((var (definition-variable exp)) (get-value-code (compile
(definition-value exp) 'val 'next))) (end-with-linkage linkage
(preserving '(env) get-value-code (make-instruction-sequence '(env val)
(list target) '((perform (op define-variable!) (const ,var) (reg val)
(reg env)) (assign ,target (const ok))))))))
:::

The appended two-instruction sequence requires `env` and `val` and
modifies the target. Note that although we preserve `env` for this
sequence, we do not preserve `val`, because the `get/value/code` is
designed to explicitly place its result in `val` for use by this
sequence. (In fact, if we did preserve `val`, we would have a bug,
because this would cause the previous contents of `val` to be restored
right after the `get/value/code` is run.)

#### Compiling conditional expressions {#compiling-conditional-expressions .unnumbered}

The code for an `if` expression compiled with a given target and linkage
has the form

::: scheme
 $\color{SchemeDark}\langle$  *compilation of predicate, target *val*,
linkage *next**  $\color{SchemeDark}\rangle$  (test (op false?) (reg
val)) (branch (label false-branch)) true-branch
 $\color{SchemeDark}\langle$  *compilation of consequent with given
target*   *and given linkage or
*after-if**  $\color{SchemeDark}\rangle$  false-branch
 $\color{SchemeDark}\langle$  *compilation of alternative with given
target and linkage*  $\color{SchemeDark}\rangle$  after-if
:::

To generate this code, we compile the predicate, consequent, and
alternative, and combine the resulting code with instructions to test
the predicate result and with newly generated labels to mark the true
and false branches and the end of the conditional.[^322] In this
arrangement of code, we must branch around the true branch if the test
is false. The only slight complication is in how the linkage for the
true branch should be handled. If the linkage for the conditional is
`return` or a label, then the true and false branches will both use this
same linkage. If the linkage is `next`, the true branch ends with a jump
around the code for the false branch to the label at the end of the
conditional.

::: scheme
(define (compile-if exp target linkage) (let ((t-branch (make-label
'true-branch)) (f-branch (make-label 'false-branch)) (after-if
(make-label 'after-if))) (let ((consequent-linkage (if (eq? linkage
'next) after-if linkage))) (let ((p-code (compile (if-predicate exp)
'val 'next)) (c-code (compile (if-consequent exp) target
consequent-linkage)) (a-code (compile (if-alternative exp) target
linkage))) (preserving '(env continue) p-code
(append-instruction-sequences (make-instruction-sequence '(val) '()
'((test (op false?) (reg val)) (branch (label ,f-branch))))
(parallel-instruction-sequences (append-instruction-sequences t-branch
c-code) (append-instruction-sequences f-branch a-code)) after-if))))))
:::

`env` is preserved around the predicate code because it could be needed
by the true and false branches, and `continue` is preserved because it
could be needed by the linkage code in those branches. The code for the
true and false branches (which are not executed sequentially) is
appended using a special combiner `parallel/instruction/sequences`
described in [Section 5.5.4](#Section 5.5.4).

Note that `cond` is a derived expression, so all that the compiler needs
to do handle it is to apply the `cond/>if` transformer (from [Section
4.1.2](#Section 4.1.2)) and compile the resulting `if` expression.

#### Compiling sequences {#compiling-sequences .unnumbered}

The compilation of sequences (from procedure bodies or explicit `begin`
expressions) parallels their evaluation. Each expression of the sequence
is compiled---the last expression with the linkage specified for the
sequence, and the other expressions with linkage `next` (to execute the
rest of the sequence). The instruction sequences for the individual
expressions are appended to form a single instruction sequence, such
that `env` (needed for the rest of the sequence) and `continue`
(possibly needed for the linkage at the end of the sequence) are
preserved.

::: scheme
(define (compile-sequence seq target linkage) (if (last-exp? seq)
(compile (first-exp seq) target linkage) (preserving '(env continue)
(compile (first-exp seq) target 'next) (compile-sequence (rest-exps seq)
target linkage))))
:::

#### Compiling `lambda` expressions {#compiling-lambda-expressions .unnumbered}

`lambda` expressions construct procedures. The object code for a
`lambda` expression must have the form

::: scheme
 $\color{SchemeDark}\langle$  *construct procedure object and assign it
to target register*  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *linkage*  $\color{SchemeDark}\rangle$
:::

When we compile the `lambda` expression, we also generate the code for
the procedure body. Although the body won't be executed at the time of
procedure construction, it is convenient to insert it into the object
code right after the code for the `lambda`. If the linkage for the
`lambda` expression is a label or `return`, this is fine. But if the
linkage is `next`, we will need to skip around the code for the
procedure body by using a linkage that jumps to a label that is inserted
after the body. The object code thus has the form

::: scheme
 $\color{SchemeDark}\langle$  *construct procedure object and assign it
to target register*  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *code for given
linkage*  $\color{SchemeDark}\rangle$   *or*
 `(goto (label after/lambda))`
 $\color{SchemeDark}\langle$  *compilation of procedure
body*  $\color{SchemeDark}\rangle$  after-lambda
:::

`compile/lambda` generates the code for constructing the procedure
object followed by the code for the procedure body. The procedure object
will be constructed at run time by combining the current environment
(the environment at the point of definition) with the entry point to the
compiled procedure body (a newly generated label).[^323]

::: scheme
(define (compile-lambda exp target linkage) (let ((proc-entry
(make-label 'entry)) (after-lambda (make-label 'after-lambda))) (let
((lambda-linkage (if (eq? linkage 'next) after-lambda linkage)))
(append-instruction-sequences (tack-on-instruction-sequence
(end-with-linkage lambda-linkage (make-instruction-sequence '(env) (list
target) '((assign ,target (op make-compiled-procedure) (label
,proc-entry) (reg env))))) (compile-lambda-body exp proc-entry))
after-lambda))))
:::

`compile/lambda` uses the special combiner
`tack/on/instruction/sequence` rather than
`append/instruction/sequences` ([Section 5.5.4](#Section 5.5.4)) to
append the procedure body to the `lambda` expression code, because the
body is not part of the sequence of instructions that will be executed
when the combined sequence is entered; rather, it is in the sequence
only because that was a convenient place to put it.

`compile/lambda/body` constructs the code for the body of the procedure.
This code begins with a label for the entry point. Next come
instructions that will cause the run-time evaluation environment to
switch to the correct environment for evaluating the procedure
body---namely, the definition environment of the procedure, extended to
include the bindings of the formal parameters to the arguments with
which the procedure is called. After this comes the code for the
sequence of expressions that makes up the procedure body. The sequence
is compiled with linkage `return` and target `val` so that it will end
by returning from the procedure with the procedure result in `val`.

::: scheme
(define (compile-lambda-body exp proc-entry) (let ((formals
(lambda-parameters exp))) (append-instruction-sequences
(make-instruction-sequence '(env proc argl) '(env) '(,proc-entry (assign
env (op compiled-procedure-env) (reg proc)) (assign env (op
extend-environment) (const ,formals) (reg argl) (reg env))))
(compile-sequence (lambda-body exp) 'val 'return))))
:::

### Compiling Combinations {#Section 5.5.3}

The essence of the compilation process is the compilation of procedure
applications. The code for a combination compiled with a given target
and linkage has the form

::: scheme
 $\color{SchemeDark}\langle$  *compilation of operator, target *proc*,
linkage *next**  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *evaluate operands and construct argument
list in *argl**  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *compilation of procedure call with given
target and linkage*  $\color{SchemeDark}\rangle$
:::

The registers `env`, `proc`, and `argl` may have to be saved and
restored during evaluation of the operator and operands. Note that this
is the only place in the compiler where a target other than `val` is
specified.

The required code is generated by `compile/application`. This
recursively compiles the operator, to produce code that puts the
procedure to be applied into `proc`, and compiles the operands, to
produce code that evaluates the individual operands of the application.
The instruction sequences for the operands are combined (by
`construct/arglist`) with code that constructs the list of arguments in
`argl`, and the resulting argument-list code is combined with the
procedure code and the code that performs the procedure call (produced
by `compile/procedure/call`). In appending the code sequences, the `env`
register must be preserved around the evaluation of the operator (since
evaluating the operator might modify `env`, which will be needed to
evaluate the operands), and the `proc` register must be preserved around
the construction of the argument list (since evaluating the operands
might modify `proc`, which will be needed for the actual procedure
application). `continue` must also be preserved throughout, since it is
needed for the linkage in the procedure call.

::: scheme
(define (compile-application exp target linkage) (let ((proc-code
(compile (operator exp) 'proc 'next)) (operand-codes (map (lambda
(operand) (compile operand 'val 'next)) (operands exp)))) (preserving
'(env continue) proc-code (preserving '(proc continue)
(construct-arglist operand-codes) (compile-procedure-call target
linkage)))))
:::

The code to construct the argument list will evaluate each operand into
`val` and then `cons` that value onto the argument list being
accumulated in `argl`. Since we `cons` the arguments onto `argl` in
sequence, we must start with the last argument and end with the first,
so that the arguments will appear in order from first to last in the
resulting list. Rather than waste an instruction by initializing `argl`
to the empty list to set up for this sequence of evaluations, we make
the first code sequence construct the initial `argl`. The general form
of the argument-list construction is thus as follows:

::: scheme
 $\color{SchemeDark}\langle$  *compilation of last operand, targeted to
*val**  $\color{SchemeDark}\rangle$  (assign argl (op list) (reg val))
 $\color{SchemeDark}\langle$  *compilation of next operand, targeted to
*val**  $\color{SchemeDark}\rangle$  (assign argl (op cons) (reg val)
(reg argl))  $\dots$   $\color{SchemeDark}\langle$  *compilation of
first operand, targeted to *val**  $\color{SchemeDark}\rangle$  (assign
argl (op cons) (reg val) (reg argl))
:::

`argl` must be preserved around each operand evaluation except the first
(so that arguments accumulated so far won't be lost), and `env` must be
preserved around each operand evaluation except the last (for use by
subsequent operand evaluations).

Compiling this argument code is a bit tricky, because of the special
treatment of the first operand to be evaluated and the need to preserve
`argl` and `env` in different places. The `construct/arglist` procedure
takes as arguments the code that evaluates the individual operands. If
there are no operands at all, it simply emits the instruction

::: scheme
(assign argl (const ()))
:::

Otherwise, `construct/arglist` creates code that initializes `argl` with
the last argument, and appends code that evaluates the rest of the
arguments and adjoins them to `argl` in succession. In order to process
the arguments from last to first, we must reverse the list of operand
code sequences from the order supplied by `compile/application`.

::: scheme
(define (construct-arglist operand-codes) (let ((operand-codes (reverse
operand-codes))) (if (null? operand-codes) (make-instruction-sequence
'() '(argl) '((assign argl (const ())))) (let ((code-to-get-last-arg
(append-instruction-sequences (car operand-codes)
(make-instruction-sequence '(val) '(argl) '((assign argl (op list) (reg
val))))))) (if (null? (cdr operand-codes)) code-to-get-last-arg
(preserving '(env) code-to-get-last-arg (code-to-get-rest-args (cdr
operand-codes))))))))

(define (code-to-get-rest-args operand-codes) (let ((code-for-next-arg
(preserving '(argl) (car operand-codes) (make-instruction-sequence '(val
argl) '(argl) '((assign argl (op cons) (reg val) (reg argl))))))) (if
(null? (cdr operand-codes)) code-for-next-arg (preserving '(env)
code-for-next-arg (code-to-get-rest-args (cdr operand-codes))))))
:::

#### Applying procedures {#applying-procedures .unnumbered}

After evaluating the elements of a combination, the compiled code must
apply the procedure in `proc` to the arguments in `argl`. The code
performs essentially the same dispatch as the `apply` procedure in the
metacircular evaluator of [Section 4.1.1](#Section 4.1.1) or the
`apply/dispatch` entry point in the explicit-control evaluator of
[Section 5.4.1](#Section 5.4.1). It checks whether the procedure to be
applied is a primitive procedure or a compiled procedure. For a
primitive procedure, it uses `apply/primitive/procedure`; we will see
shortly how it handles compiled procedures. The procedure-application
code has the following form:

::: scheme
(test (op primitive-procedure?) (reg proc)) (branch (label
primitive-branch)) compiled-branch  $\color{SchemeDark}\langle$  *code
to apply compiled procedure with given target*   *and appropriate
linkage*  $\color{SchemeDark}\rangle$  primitive-branch (assign
 $\color{SchemeDark}\langle$  *target*  $\color{SchemeDark}\rangle$  (op
apply-primitive-procedure) (reg proc) (reg argl))
 $\color{SchemeDark}\langle$  *linkage*  $\color{SchemeDark}\rangle$
after-call
:::

Observe that the compiled branch must skip around the primitive branch.
Therefore, if the linkage for the original procedure call was `next`,
the compound branch must use a linkage that jumps to a label that is
inserted after the primitive branch. (This is similar to the linkage
used for the true branch in `compile/if`.)

::: scheme
(define (compile-procedure-call target linkage) (let ((primitive-branch
(make-label 'primitive-branch)) (compiled-branch (make-label
'compiled-branch)) (after-call (make-label 'after-call)))

(let ((compiled-linkage (if (eq? linkage 'next) after-call linkage)))
(append-instruction-sequences (make-instruction-sequence '(proc) '()
'((test (op primitive-procedure?) (reg proc)) (branch (label
,primitive-branch)))) (parallel-instruction-sequences
(append-instruction-sequences compiled-branch (compile-proc-appl target
compiled-linkage)) (append-instruction-sequences primitive-branch
(end-with-linkage linkage (make-instruction-sequence '(proc argl) (list
target) '((assign ,target (op apply-primitive-procedure) (reg proc) (reg
argl))))))) after-call))))
:::

The primitive and compound branches, like the true and false branches in
`compile/if`, are appended using `parallel/instruction/sequences` rather
than the ordinary `append/instruction/sequences`, because they will not
be executed sequentially.

#### Applying compiled procedures {#applying-compiled-procedures .unnumbered}

The code that handles procedure application is the most subtle part of
the compiler, even though the instruction sequences it generates are
very short. A compiled procedure (as constructed by `compile/lambda`)
has an entry point, which is a label that designates where the code for
the procedure starts. The code at this entry point computes a result in
`val` and returns by executing the instruction `(goto (reg continue))`.
Thus, we might expect the code for a compiled-procedure application (to
be generated by `compile/proc/appl`) with a given target and linkage to
look like this if the linkage is a label

::: scheme
(assign continue (label proc-return)) (assign val (op
compiled-procedure-entry) (reg proc)) (goto (reg val)) proc-return
(assign
 $\color{SchemeDark}\langle$  *target*  $\color{SchemeDark}\rangle$
(reg val))  [; included if target is not `val`]{.roman}  (goto (label
 $\color{SchemeDark}\langle$  *linkage*  $\color{SchemeDark}\rangle$ ))
 [; linkage code]{.roman}
:::

or like this if the linkage is `return`.

::: scheme
(save continue) (assign continue (label proc-return)) (assign val (op
compiled-procedure-entry) (reg proc)) (goto (reg val)) proc-return
(assign
 $\color{SchemeDark}\langle$  *target*  $\color{SchemeDark}\rangle$
(reg val))  [; included if target is not `val`]{.roman}  (restore
continue) (goto (reg continue))  [; linkage code]{.roman}
:::

This code sets up `continue` so that the procedure will return to a
label `proc/return` and jumps to the procedure's entry point. The code
at `proc/return` transfers the procedure's result from `val` to the
target register (if necessary) and then jumps to the location specified
by the linkage. (The linkage is always `return` or a label, because
`compile/procedure/call` replaces a `next` linkage for the
compound-procedure branch by an `after/call` label.)

In fact, if the target is not `val`, that is exactly the code our
compiler will generate.[^324] Usually, however, the target is `val` (the
only time the compiler specifies a different register is when targeting
the evaluation of an operator to `proc`), so the procedure result is put
directly into the target register and there is no need to return to a
special location that copies it. Instead, we simplify the code by
setting up `continue` so that the procedure will "return" directly to
the place specified by the caller's linkage:

::: scheme
 $\color{SchemeDark}\langle$  *set up *continue* for
linkage*  $\color{SchemeDark}\rangle$  (assign val (op
compiled-procedure-entry) (reg proc)) (goto (reg val))
:::

If the linkage is a label, we set up `continue` so that the procedure
will return to that label. (That is, the `(goto (reg continue))` the
procedure ends with becomes equivalent to the
`(goto (label `$\langle$*`linkage`*$\rangle$`))` at `proc/return`
above.)

::: scheme
(assign continue (label
 $\color{SchemeDark}\langle$  *linkage*  $\color{SchemeDark}\rangle$ ))
(assign val (op compiled-procedure-entry) (reg proc)) (goto (reg val))
:::

If the linkage is `return`, we don't need to set up `continue` at all:
It already holds the desired location. (That is, the
`(goto (reg continue))` the procedure ends with goes directly to the
place where the `(goto (reg continue))` at `proc/return` would have
gone.)

::: scheme
(assign val (op compiled-procedure-entry) (reg proc)) (goto (reg val))
:::

With this implementation of the `return` linkage, the compiler generates
tail-recursive code. Calling a procedure as the final step in a
procedure body does a direct transfer, without saving any information on
the stack.

Suppose instead that we had handled the case of a procedure call with a
linkage of `return` and a target of `val` as shown above for a non-`val`
target. This would destroy tail recursion. Our system would still give
the same value for any expression. But each time we called a procedure,
we would save `continue` and return after the call to undo the (useless)
save. These extra saves would accumulate during a nest of procedure
calls.[^325]

`compile/proc/appl` generates the above procedure-application code by
considering four cases, depending on whether the target for the call is
`val` and whether the linkage is `return`. Observe that the instruction
sequences are declared to modify all the registers, since executing the
procedure body can change the registers in arbitrary ways.[^326] Also
note that the code sequence for the case with target `val` and linkage
`return` is declared to need `continue`: Even though `continue` is not
explicitly used in the two-instruction sequence, we must be sure that
`continue` will have the correct value when we enter the compiled
procedure.

::: scheme
(define (compile-proc-appl target linkage) (cond ((and (eq? target 'val)
(not (eq? linkage 'return))) (make-instruction-sequence '(proc) all-regs
'((assign continue (label ,linkage)) (assign val (op
compiled-procedure-entry) (reg proc)) (goto (reg val))))) ((and (not
(eq? target 'val)) (not (eq? linkage 'return))) (let ((proc-return
(make-label 'proc-return))) (make-instruction-sequence '(proc) all-regs
'((assign continue (label ,proc-return)) (assign val (op
compiled-procedure-entry) (reg proc)) (goto (reg val)) ,proc-return
(assign ,target (reg val)) (goto (label ,linkage)))))) ((and (eq? target
'val) (eq? linkage 'return)) (make-instruction-sequence '(proc continue)
all-regs '((assign val (op compiled-procedure-entry) (reg proc)) (goto
(reg val))))) ((and (not (eq? target 'val)) (eq? linkage 'return))
(error \"return linkage, target not val: COMPILE\" target))))
:::

### Combining Instruction Sequences {#Section 5.5.4}

This section describes the details on how instruction sequences are
represented and combined. Recall from [Section 5.5.1](#Section 5.5.1)
that an instruction sequence is represented as a list of the registers
needed, the registers modified, and the actual instructions. We will
also consider a label (symbol) to be a degenerate case of an instruction
sequence, which doesn't need or modify any registers. So to determine
the registers needed and modified by instruction sequences we use the
selectors

::: scheme
(define (registers-needed s) (if (symbol? s) '() (car s))) (define
(registers-modified s) (if (symbol? s) '() (cadr s))) (define
(statements s) (if (symbol? s) (list s) (caddr s)))
:::

and to determine whether a given sequence needs or modifies a given
register we use the predicates

::: scheme
(define (needs-register? seq reg) (memq reg (registers-needed seq)))
(define (modifies-register? seq reg) (memq reg (registers-modified
seq)))
:::

In terms of these predicates and selectors, we can implement the various
instruction sequence combiners used throughout the compiler.

The basic combiner is `append/instruction/sequences`. This takes as
arguments an arbitrary number of instruction sequences that are to be
executed sequentially and returns an instruction sequence whose
statements are the statements of all the sequences appended together.
The subtle point is to determine the registers that are needed and
modified by the resulting sequence. It modifies those registers that are
modified by any of the sequences; it needs those registers that must be
initialized before the first sequence can be run (the registers needed
by the first sequence), together with those registers needed by any of
the other sequences that are not initialized (modified) by sequences
preceding it.

The sequences are appended two at a time by `append/2/sequences`. This
takes two instruction sequences `seq1` and `seq2` and returns the
instruction sequence whose statements are the statements of `seq1`
followed by the statements of `seq2`, whose modified registers are those
registers that are modified by either `seq1` or `seq2`, and whose needed
registers are the registers needed by `seq1` together with those
registers needed by `seq2` that are not modified by `seq1`. (In terms of
set operations, the new set of needed registers is the union of the set
of registers needed by `seq1` with the set difference of the registers
needed by `seq2` and the registers modified by `seq1`.) Thus,
`append/instruction/sequences` is implemented as follows:

::: scheme
(define (append-instruction-sequences . seqs) (define
(append-2-sequences seq1 seq2) (make-instruction-sequence (list-union
(registers-needed seq1) (list-difference (registers-needed seq2)
(registers-modified seq1))) (list-union (registers-modified seq1)
(registers-modified seq2)) (append (statements seq1) (statements
seq2)))) (define (append-seq-list seqs) (if (null? seqs)
(empty-instruction-sequence) (append-2-sequences (car seqs)
(append-seq-list (cdr seqs))))) (append-seq-list seqs))
:::

This procedure uses some simple operations for manipulating sets
represented as lists, similar to the (unordered) set representation
described in [Section 2.3.3](#Section 2.3.3):

::: scheme
(define (list-union s1 s2) (cond ((null? s1) s2) ((memq (car s1) s2)
(list-union (cdr s1) s2)) (else (cons (car s1) (list-union (cdr s1)
s2))))) (define (list-difference s1 s2) (cond ((null? s1) '()) ((memq
(car s1) s2) (list-difference (cdr s1) s2)) (else (cons (car s1)
(list-difference (cdr s1) s2)))))
:::

`preserving`, the second major instruction sequence combiner, takes a
list of registers `regs` and two instruction sequences `seq1` and `seq2`
that are to be executed sequentially. It returns an instruction sequence
whose statements are the statements of `seq1` followed by the statements
of `seq2`, with appropriate `save` and `restore` instructions around
`seq1` to protect the registers in `regs` that are modified by `seq1`
but needed by `seq2`. To accomplish this, `preserving` first creates a
sequence that has the required `save`s followed by the statements of
`seq1` followed by the required `restore`s. This sequence needs the
registers being saved and restored in addition to the registers needed
by `seq1`, and modifies the registers modified by `seq1` except for the
ones being saved and restored. This augmented sequence and `seq2` are
then appended in the usual way. The following procedure implements this
strategy recursively, walking down the list of registers to be
preserved:[^327]

::: scheme
(define (preserving regs seq1 seq2) (if (null? regs)
(append-instruction-sequences seq1 seq2) (let ((first-reg (car regs)))
(if (and (needs-register? seq2 first-reg) (modifies-register? seq1
first-reg)) (preserving (cdr regs) (make-instruction-sequence
(list-union (list first-reg) (registers-needed seq1)) (list-difference
(registers-modified seq1) (list first-reg)) (append '((save ,first-reg))
(statements seq1) '((restore ,first-reg)))) seq2) (preserving (cdr regs)
seq1 seq2)))))
:::

Another sequence combiner, `tack/on/instruction/sequence`, is used by
`compile/lambda` to append a procedure body to another sequence. Because
the procedure body is not "in line" to be executed as part of the
combined sequence, its register use has no impact on the register use of
the sequence in which it is embedded. We thus ignore the procedure
body's sets of needed and modified registers when we tack it onto the
other sequence.

::: scheme
(define (tack-on-instruction-sequence seq body-seq)
(make-instruction-sequence (registers-needed seq) (registers-modified
seq) (append (statements seq) (statements body-seq))))
:::

`compile/if` and `compile/procedure/call` use a special combiner called
`parallel/instruction/sequences` to append the two alternative branches
that follow a test. The two branches will never be executed
sequentially; for any particular evaluation of the test, one branch or
the other will be entered. Because of this, the registers needed by the
second branch are still needed by the combined sequence, even if these
are modified by the first branch.

::: scheme
(define (parallel-instruction-sequences seq1 seq2)
(make-instruction-sequence (list-union (registers-needed seq1)
(registers-needed seq2)) (list-union (registers-modified seq1)
(registers-modified seq2)) (append (statements seq1) (statements
seq2))))
:::

### An Example of Compiled Code {#Section 5.5.5}

Now that we have seen all the elements of the compiler, let us examine
an example of compiled code to see how things fit together. We will
compile the definition of a recursive `factorial` procedure by calling
`compile`:

::: scheme
(compile '(define (factorial n) (if (= n 1) 1 (\* (factorial (- n 1))
n))) 'val 'next)
:::

We have specified that the value of the `define` expression should be
placed in the `val` register. We don't care what the compiled code does
after executing the `define`, so our choice of `next` as the linkage
descriptor is arbitrary.

`compile` determines that the expression is a definition, so it calls
`compile/definition` to compile code to compute the value to be assigned
(targeted to `val`), followed by code to install the definition,
followed by code to put the value of the `define` (which is the symbol
`ok`) into the target register, followed finally by the linkage code.
`env` is preserved around the computation of the value, because it is
needed in order to install the definition. Because the linkage is
`next`, there is no linkage code in this case. The skeleton of the
compiled code is thus

::: scheme
 $\color{SchemeDark}\langle$  *save *env* if modified by code to compute
value*  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *compilation of definition value, target
*val*, linkage *next**  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *restore *env* if saved
above*  $\color{SchemeDark}\rangle$  (perform (op define-variable!)
(const factorial) (reg val) (reg env)) (assign val (const ok))
:::

The expression that is to be compiled to produce the value for the
variable `factorial` is a `lambda` expression whose value is the
procedure that computes factorials. `compile` handles this by calling
`compile/lambda`, which compiles the procedure body, labels it as a new
entry point, and generates the instruction that will combine the
procedure body at the new entry point with the run-time environment and
assign the result to `val`. The sequence then skips around the compiled
procedure code, which is inserted at this point. The procedure code
itself begins by extending the procedure's definition environment by a
frame that binds the formal parameter `n` to the procedure argument.
Then comes the actual procedure body. Since this code for the value of
the variable doesn't modify the `env` register, the optional `save` and
`restore` shown above aren't generated. (The procedure code at `entry2`
isn't executed at this point, so its use of `env` is irrelevant.)
Therefore, the skeleton for the compiled code becomes

::: scheme
(assign val (op make-compiled-procedure) (label entry2) (reg env)) (goto
(label after-lambda1)) entry2 (assign env (op compiled-procedure-env)
(reg proc)) (assign env (op extend-environment) (const (n)) (reg argl)
(reg env))  $\color{SchemeDark}\langle$  *compilation of procedure
body*  $\color{SchemeDark}\rangle$  after-lambda1 (perform (op
define-variable!) (const factorial) (reg val) (reg env)) (assign val
(const ok))
:::

A procedure body is always compiled (by `compile/lambda/body`) as a
sequence with target `val` and linkage `return`. The sequence in this
case consists of a single `if` expression:

::: scheme
(if (= n 1) 1 (\* (factorial (- n 1)) n))
:::

`compile/if` generates code that first computes the predicate (targeted
to `val`), then checks the result and branches around the true branch if
the predicate is false. `env` and `continue` are preserved around the
predicate code, since they may be needed for the rest of the `if`
expression. Since the `if` expression is the final expression (and only
expression) in the sequence making up the procedure body, its target is
`val` and its linkage is `return`, so the true and false branches are
both compiled with target `val` and linkage `return`. (That is, the
value of the conditional, which is the value computed by either of its
branches, is the value of the procedure.)

::: scheme
 $\color{SchemeDark}\langle$  *save *continue*, *env* if modified by
predicate and needed by branches*  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *compilation of predicate, target *val*,
linkage *next**  $\color{SchemeDark}\rangle$
 $\color{SchemeDark}\langle$  *restore *continue*, *env* if saved
above*  $\color{SchemeDark}\rangle$  (test (op false?) (reg val))
(branch (label false-branch4)) true-branch5
 $\color{SchemeDark}\langle$  *compilation of true branch, target *val*,
linkage *return**  $\color{SchemeDark}\rangle$  false-branch4
 $\color{SchemeDark}\langle$  *compilation of false branch, target
*val*, linkage *return**  $\color{SchemeDark}\rangle$  after-if3
:::

The predicate `(= n 1)` is a procedure call. This looks up the operator
(the symbol `=`) and places this value in `proc`. It then assembles the
arguments `1` and the value of `n` into `argl`. Then it tests whether
`proc` contains a primitive or a compound procedure, and dispatches to a
primitive branch or a compound branch accordingly. Both branches resume
at the `after/call` label. The requirements to preserve registers around
the evaluation of the operator and operands don't result in any saving
of registers, because in this case those evaluations don't modify the
registers in question.

::: scheme
(assign proc (op lookup-variable-value) (const =) (reg env)) (assign val
(const 1)) (assign argl (op list) (reg val)) (assign val (op
lookup-variable-value) (const n) (reg env)) (assign argl (op cons) (reg
val) (reg argl)) (test (op primitive-procedure?) (reg proc)) (branch
(label primitive-branch17)) compiled-branch16 (assign continue (label
after-call15)) (assign val (op compiled-procedure-entry) (reg proc))
(goto (reg val)) primitive-branch17 (assign val (op
apply-primitive-procedure) (reg proc) (reg argl)) after-call15
:::

The true branch, which is the constant 1, compiles (with target `val`
and linkage `return`) to

::: scheme
(assign val (const 1)) (goto (reg continue))
:::

The code for the false branch is another procedure call, where the
procedure is the value of the symbol `*`, and the arguments are `n` and
the result of another procedure call (a call to `factorial`). Each of
these calls sets up `proc` and `argl` and its own primitive and compound
branches. [Figure 5.17](#Figure 5.17) shows the complete compilation of
the definition of the `factorial` procedure. Notice that the possible
`save` and `restore` of `continue` and `env` around the predicate, shown
above, are in fact generated, because these registers are modified by
the procedure call in the predicate and needed for the procedure call
and the `return` linkage in the branches.

> **[]{#Exercise 5.33 label="Exercise 5.33"}Exercise 5.33:** Consider
> the following definition of a factorial procedure, which is slightly
> different from the one given above:
>
> ::: scheme
> (define (factorial-alt n) (if (= n 1) 1 (\* n (factorial-alt (- n
> 1)))))
> :::
>
> Compile this procedure and compare the resulting code with that
> produced for `factorial`. Explain any differences you find. Does
> either program execute more efficiently than the other?

> **[]{#Exercise 5.34 label="Exercise 5.34"}Exercise 5.34:** Compile the
> iterative factorial procedure
>
> ::: scheme
> (define (factorial n) (define (iter product counter) (if (\> counter
> n) product (iter (\* counter product) (+ counter 1)))) (iter 1 1))
> :::
>
> Annotate the resulting code, showing the essential difference between
> the code for iterative and recursive versions of `factorial` that
> makes one process build up stack space and the other run in constant
> stack space.

> **[]{#Figure 5.17 label="Figure 5.17"}Figure 5.17:** $\downarrow$
> Compilation of the definition of the `factorial` procedure.
>
> ::: smallscheme
>  [;; construct the procedure and skip over code for the procedure
> body]{.roman}  (assign val (op make-compiled-procedure) (label entry2)
> (reg env)) (goto (label after-lambda1)) entry2  [; calls to
> `factorial` will enter here]{.roman}  (assign env (op
> compiled-procedure-env) (reg proc)) (assign env (op
> extend-environment) (const (n)) (reg argl) (reg env))  [;; begin
> actual procedure body]{.roman}  (save continue) (save env)  [;;
> compute `(= n 1)`]{.roman}  (assign proc (op lookup-variable-value)
> (const =) (reg env)) (assign val (const 1)) (assign argl (op list)
> (reg val)) (assign val (op lookup-variable-value) (const n) (reg env))
> (assign argl (op cons) (reg val) (reg argl)) (test (op
> primitive-procedure?) (reg proc)) (branch (label primitive-branch17))
> compiled-branch16 (assign continue (label after-call15)) (assign val
> (op compiled-procedure-entry) (reg proc)) (goto (reg val))
> primitive-branch17 (assign val (op apply-primitive-procedure) (reg
> proc) (reg argl)) after-call15  [; `val` now contains result of
> `(= n 1)`]{.roman}  (restore env) (restore continue) (test (op false?)
> (reg val)) (branch (label false-branch4)) true-branch5  [; return
> 1]{.roman}  (assign val (const 1)) (goto (reg continue)) false-branch4
>  [;; compute and return `(* (factorial (- n 1)) n)`]{.roman}  (assign
> proc (op lookup-variable-value) (const \*) (reg env)) (save continue)
> (save proc)  [; save `*`]{.roman}  procedure (assign val (op
> lookup-variable-value) (const n) (reg env)) (assign argl (op list)
> (reg val)) (save argl)  [; save partial argument list for
> `*`]{.roman}   [;; compute `(factorial (- n 1))`, which is the other
> argument for `*`]{.roman}  (assign proc (op lookup-variable-value)
> (const factorial) (reg env)) (save proc)  [; save `factorial`
> procedure]{.roman}   [;; compute `(- n 1)`, which is the argument for
> `factorial`]{.roman}  (assign proc (op lookup-variable-value) (const
> -) (reg env)) (assign val (const 1)) (assign argl (op list) (reg val))
> (assign val (op lookup-variable-value) (const n) (reg env)) (assign
> argl (op cons) (reg val) (reg argl)) (test (op primitive-procedure?)
> (reg proc)) (branch (label primitive-branch8)) compiled-branch7
> (assign continue (label after-call6)) (assign val (op
> compiled-procedure-entry) (reg proc)) (goto (reg val))
> primitive-branch8 (assign val (op apply-primitive-procedure) (reg
> proc) (reg argl)) after-call6  [; `val` now contains result of
> `(- n 1)`]{.roman}  (assign argl (op list) (reg val)) (restore proc)
>  [; restore `factorial`]{.roman}   [;; apply `factorial`]{.roman}
> (test (op primitive-procedure?) (reg proc)) (branch (label
> primitive-branch11)) compiled-branch10 (assign continue (label
> after-call9)) (assign val (op compiled-procedure-entry) (reg proc))
> (goto (reg val)) primitive-branch11 (assign val (op
> apply-primitive-procedure) (reg proc) (reg argl)) after-call9  [;
> `val` now contains result of `(factorial (- n 1))`]{.roman}  (restore
> argl)  [; restore partial argument list for `*`]{.roman}  (assign argl
> (op cons) (reg val) (reg argl)) (restore proc)  [; restore
> `*`]{.roman}  (restore continue)  [;; apply `*` and return its
> value]{.roman}  (test (op primitive-procedure?) (reg proc)) (branch
> (label primitive-branch14)) compiled-branch13  [;; note that a
> compound procedure here is called tail-recursively]{.roman}  (assign
> val (op compiled-procedure-entry) (reg proc)) (goto (reg val))
> primitive-branch14 (assign val (op apply-primitive-procedure) (reg
> proc) (reg argl)) (goto (reg continue)) after-call12 after-if3
> after-lambda1  [;; assign the procedure to the variable
> `factorial`]{.roman}  (perform (op define-variable!) (const factorial)
> (reg val) (reg env)) (assign val (const ok))
> :::

> **[]{#Exercise 5.35 label="Exercise 5.35"}Exercise 5.35:** What
> expression was compiled to produce the code shown in [Figure
> 5.18](#Figure 5.18)?

> **[]{#Figure 5.18 label="Figure 5.18"}Figure 5.18:** $\downarrow$ An
> example of compiler output. See [Exercise 5.35](#Exercise 5.35).
>
> ::: smallscheme
> (assign val (op make-compiled-procedure) (label entry16) (reg env))
> (goto (label after-lambda15)) entry16 (assign env (op
> compiled-procedure-env) (reg proc)) (assign env (op
> extend-environment) (const (x)) (reg argl) (reg env)) (assign proc (op
> lookup-variable-value) (const +) (reg env)) (save continue) (save
> proc) (save env) (assign proc (op lookup-variable-value) (const g)
> (reg env)) (save proc) (assign proc (op lookup-variable-value) (const
> +) (reg env)) (assign val (const 2)) (assign argl (op list) (reg val))
> (assign val (op lookup-variable-value) (const x) (reg env)) (assign
> argl (op cons) (reg val) (reg argl)) (test (op primitive-procedure?)
> (reg proc)) (branch (label primitive-branch19)) compiled-branch18
> (assign continue (label after-call17)) (assign val (op
> compiled-procedure-entry) (reg proc)) (goto (reg val))
> primitive-branch19 (assign val (op apply-primitive-procedure) (reg
> proc) (reg argl)) after-call17 (assign argl (op list) (reg val))
> (restore proc) (test (op primitive-procedure?) (reg proc)) (branch
> (label primitive-branch22)) compiled-branch21 (assign continue (label
> after-call20)) (assign val (op compiled-procedure-entry) (reg proc))
> (goto (reg val)) primitive-branch22 (assign val (op
> apply-primitive-procedure) (reg proc) (reg argl)) after-call20 (assign
> argl (op list) (reg val)) (restore env) (assign val (op
> lookup-variable-value) (const x) (reg env)) (assign argl (op cons)
> (reg val) (reg argl)) (restore proc) (restore continue) (test (op
> primitive-procedure?) (reg proc)) (branch (label primitive-branch25))
> compiled-branch24 (assign val (op compiled-procedure-entry) (reg
> proc)) (goto (reg val)) primitive-branch25 (assign val (op
> apply-primitive-procedure) (reg proc) (reg argl)) (goto (reg
> continue)) after-call23 after-lambda15 (perform (op define-variable!)
> (const f) (reg val) (reg env)) (assign val (const ok))
> :::

> **[]{#Exercise 5.36 label="Exercise 5.36"}Exercise 5.36:** What order
> of evaluation does our compiler produce for operands of a combination?
> Is it left-to-right, right-to-left, or some other order? Where in the
> compiler is this order determined? Modify the compiler so that it
> produces some other order of evaluation. (See the discussion of order
> of evaluation for the explicit-control evaluator in [Section
> 5.4.1](#Section 5.4.1).) How does changing the order of operand
> evaluation affect the efficiency of the code that constructs the
> argument list?

> **[]{#Exercise 5.37 label="Exercise 5.37"}Exercise 5.37:** One way to
> understand the compiler's `preserving` mechanism for optimizing stack
> usage is to see what extra operations would be generated if we did not
> use this idea. Modify `preserving` so that it always generates the
> `save` and `restore` operations. Compile some simple expressions and
> identify the unnecessary stack operations that are generated. Compare
> the code to that generated with the `preserving` mechanism intact.

> **[]{#Exercise 5.38 label="Exercise 5.38"}Exercise 5.38:** Our
> compiler is clever about avoiding unnecessary stack operations, but it
> is not clever at all when it comes to compiling calls to the primitive
> procedures of the language in terms of the primitive operations
> supplied by the machine. For example, consider how much code is
> compiled to compute `(+ a 1)`: The code sets up an argument list in
> `argl`, puts the primitive addition procedure (which it finds by
> looking up the symbol `+` in the environment) into `proc`, and tests
> whether the procedure is primitive or compound. The compiler always
> generates code to perform the test, as well as code for primitive and
> compound branches (only one of which will be executed). We have not
> shown the part of the controller that implements primitives, but we
> presume that these instructions make use of primitive arithmetic
> operations in the machine's data paths. Consider how much less code
> would be generated if the compiler could *open-code* primitives---that
> is, if it could generate code to directly use these primitive machine
> operations. The expression `(+ a 1)` might be compiled into something
> as simple as[^328]
>
> ::: scheme
> (assign val (op lookup-variable-value) (const a) (reg env)) (assign
> val (op +) (reg val) (const 1))
> :::
>
> In this exercise we will extend our compiler to support open coding of
> selected primitives. Special-purpose code will be generated for calls
> to these primitive procedures instead of the general
> procedure-application code. In order to support this, we will augment
> our machine with special argument registers `arg1` and `arg2`. The
> primitive arithmetic operations of the machine will take their inputs
> from `arg1` and `arg2`. The results may be put into `val`, `arg1`, or
> `arg2`.
>
> The compiler must be able to recognize the application of an
> open-coded primitive in the source program. We will augment the
> dispatch in the `compile` procedure to recognize the names of these
> primitives in addition to the reserved words (the special forms) it
> currently recognizes.[^329] For each special form our compiler has a
> code generator. In this exercise we will construct a family of code
> generators for the open-coded primitives.
>
> a.  The open-coded primitives, unlike the special forms, all need
>     their operands evaluated. Write a code generator
>     `spread/arguments` for use by all the open-coding code generators.
>     `spread/arguments` should take an operand list and compile the
>     given operands targeted to successive argument registers. Note
>     that an operand may contain a call to an open-coded primitive, so
>     argument registers will have to be preserved during operand
>     evaluation.
>
> b.  For each of the primitive procedures `=`, `*`, `-`, and `+`, write
>     a code generator that takes a combination with that operator,
>     together with a target and a linkage descriptor, and produces code
>     to spread the arguments into the registers and then perform the
>     operation targeted to the given target with the given linkage. You
>     need only handle expressions with two operands. Make `compile`
>     dispatch to these code generators.
>
> c.  Try your new compiler on the `factorial` example. Compare the
>     resulting code with the result produced without open coding.
>
> d.  Extend your code generators for `+` and `*` so that they can
>     handle expressions with arbitrary numbers of operands. An
>     expression with more than two operands will have to be compiled
>     into a sequence of operations, each with only two inputs.

### Lexical Addressing {#Section 5.5.6}

One of the most common optimizations performed by compilers is the
optimization of variable lookup. Our compiler, as we have implemented it
so far, generates code that uses the `lookup/variable/value` operation
of the evaluator machine. This searches for a variable by comparing it
with each variable that is currently bound, working frame by frame
outward through the run-time environment. This search can be expensive
if the frames are deeply nested or if there are many variables. For
example, consider the problem of looking up the value of `x` while
evaluating the expression `(* x y z)` in an application of the procedure
that is returned by

::: scheme
(let ((x 3) (y 4)) (lambda (a b c d e) (let ((y (\* a b x)) (z (+ c d
x))) (\* x y z))))
:::

Since a `let` expression is just syntactic sugar for a `lambda`
combination, this expression is equivalent to

::: scheme
((lambda (x y) (lambda (a b c d e) ((lambda (y z) (\* x y z)) (\* a b x)
(+ c d x)))) 3 4)
:::

Each time `lookup/variable/value` searches for `x`, it must determine
that the symbol `x` is not `eq?` to `y` or `z` (in the first frame), nor
to `a`, `b`, `c`, `d`, or `e` (in the second frame). We will assume, for
the moment, that our programs do not use `define`---that variables are
bound only with `lambda`. Because our language is lexically scoped, the
run-time environment for any expression will have a structure that
parallels the lexical structure of the program in which the expression
appears.[^330] Thus, the compiler can know, when it analyzes the above
expression, that each time the procedure is applied the variable `x` in
`(* x y z)` will be found two frames out from the current frame and will
be the first variable in that frame.

We can exploit this fact by inventing a new kind of variable-lookup
operation, `lexical/address/lookup`, that takes as arguments an
environment and a *lexical address* that consists of two numbers: a
*frame number*, which specifies how many frames to pass over, and a
*displacement number*, which specifies how many variables to pass over
in that frame. `lexical/address/lookup` will produce the value of the
variable stored at that lexical address relative to the current
environment. If we add the `lexical/address/lookup` operation to our
machine, we can make the compiler generate code that references
variables using this operation, rather than `lookup/variable/value`.
Similarly, our compiled code can use a new `lexical/address/set!`
operation instead of `set/variable/value!`.

In order to generate such code, the compiler must be able to determine
the lexical address of a variable it is about to compile a reference to.
The lexical address of a variable in a program depends on where one is
in the code. For example, in the following program, the address of `x`
in expression $\langle$*e1*$\kern0.08em\rangle$ is (2, 0)---two frames
back and the first variable in the frame. At that point `y` is at
address (0, 0) and `c` is at address (1, 2). In expression
$\langle$*e2*$\kern0.09em\rangle$, `x` is at (1, 0), `y` is at (1, 1),
and `c` is at (0, 2).

::: scheme
((lambda (x y) (lambda (a b c d e) ((lambda (y z)
 $\color{SchemeDark}\langle$  *e1*  $\color{SchemeDark}\rangle$ )
 $\color{SchemeDark}\langle$  *e2*  $\color{SchemeDark}\rangle$  (+ c d
x)))) 3 4)
:::

One way for the compiler to produce code that uses lexical addressing is
to maintain a data structure called a *compile-time environment*. This
keeps track of which variables will be at which positions in which
frames in the run-time environment when a particular variable-access
operation is executed. The compile-time environment is a list of frames,
each containing a list of variables. (There will of course be no values
bound to the variables, since values are not computed at compile time.)
The compile-time environment becomes an additional argument to `compile`
and is passed along to each code generator. The top-level call to
`compile` uses an empty compile-time environment. When a `lambda` body
is compiled, `compile/lambda/body` extends the compile-time environment
by a frame containing the procedure's parameters, so that the sequence
making up the body is compiled with that extended environment. At each
point in the compilation, `compile/variable` and `compile/assignment`
use the compile-time environment in order to generate the appropriate
lexical addresses.

[Exercise 5.39](#Exercise 5.39) through [Exercise 5.43](#Exercise 5.43)
describe how to complete this sketch of the lexical-addressing strategy
in order to incorporate lexical lookup into the compiler. [Exercise
5.44](#Exercise 5.44) describes another use for the compile-time
environment.

> **[]{#Exercise 5.39 label="Exercise 5.39"}Exercise 5.39:** Write a
> procedure `lexical/address/lookup` that implements the new lookup
> operation. It should take two arguments---a lexical address and a
> run-time environment---and return the value of the variable stored at
> the specified lexical address. `lexical/address/lookup` should signal
> an error if the value of the variable is the symbol
> `*unassigned*`.[^331] Also write a procedure `lexical/address/set!`
> that implements the operation that changes the value of the variable
> at a specified lexical address.

> **[]{#Exercise 5.40 label="Exercise 5.40"}Exercise 5.40:** Modify the
> compiler to maintain the compile-time environment as described above.
> That is, add a compile-time-environment argument to `compile` and the
> various code generators, and extend it in `compile/lambda/body`.

> **[]{#Exercise 5.41 label="Exercise 5.41"}Exercise 5.41:** Write a
> procedure `find/variable` that takes as arguments a variable and a
> compile-time environment and returns the lexical address of the
> variable with respect to that environment. For example, in the program
> fragment that is shown above, the compile-time environment during the
> compilation of expression $\langle$*e1*$\kern0.08em\rangle$ is
> `((y z) (a b c d e) (x y))`. `find/variable` should produce
>
> ::: scheme
> (find-variable 'c '((y z) (a b c d e) (x y)))  *(1 2)*  (find-variable
> 'x '((y z) (a b c d e) (x y)))  *(2 0)*  (find-variable 'w '((y z) (a
> b c d e) (x y)))  *not-found*
> :::

> **[]{#Exercise 5.42 label="Exercise 5.42"}Exercise 5.42:** Using
> `find/variable` from [Exercise 5.41](#Exercise 5.41), rewrite
> `compile/variable` and `compile/assignment` to output lexical-address
> instructions. In cases where `find/variable` returns `not/found` (that
> is, where the variable is not in the compile-time environment), you
> should have the code generators use the evaluator operations, as
> before, to search for the binding. (The only place a variable that is
> not found at compile time can be is in the global environment, which
> is part of the run-time environment but is not part of the
> compile-time environment.[^332] Thus, if you wish, you may have the
> evaluator operations look directly in the global environment, which
> can be obtained with the operation `(op get/global/environment)`,
> instead of having them search the whole run-time environment found in
> `env`.) Test the modified compiler on a few simple cases, such as the
> nested `lambda` combination at the beginning of this section.

> **[]{#Exercise 5.43 label="Exercise 5.43"}Exercise 5.43:** We argued
> in [Section 4.1.6](#Section 4.1.6) that internal definitions for block
> structure should not be considered "real" `define`s. Rather, a
> procedure body should be interpreted as if the internal variables
> being defined were installed as ordinary `lambda` variables
> initialized to their correct values using `set!`. [Section
> 4.1.6](#Section 4.1.6) and [Exercise 4.16](#Exercise 4.16) showed how
> to modify the metacircular interpreter to accomplish this by scanning
> out internal definitions. Modify the compiler to perform the same
> transformation before it compiles a procedure body.

> **[]{#Exercise 5.44 label="Exercise 5.44"}Exercise 5.44:** In this
> section we have focused on the use of the compile-time environment to
> produce lexical addresses. But there are other uses for compile-time
> environments. For instance, in [Exercise 5.38](#Exercise 5.38) we
> increased the efficiency of compiled code by open-coding primitive
> procedures. Our implementation treated the names of open-coded
> procedures as reserved words. If a program were to rebind such a name,
> the mechanism described in [Exercise 5.38](#Exercise 5.38) would still
> open-code it as a primitive, ignoring the new binding. For example,
> consider the procedure
>
> ::: scheme
> (lambda (+ \* a b x y) (+ (\* a x) (\* b y)))
> :::
>
> which computes a linear combination of `x` and `y`. We might call it
> with arguments `+matrix`, `*matrix`, and four matrices, but the
> open-coding compiler would still open-code the `+` and the `*` in
> `(+ (* a x) (* b y))` as primitive `+` and `*`. Modify the open-coding
> compiler to consult the compile-time environment in order to compile
> the correct code for expressions involving the names of primitive
> procedures. (The code will work correctly as long as the program does
> not `define` or `set!` these names.)

### Interfacing Compiled Code to the Evaluator {#Section 5.5.7}

We have not yet explained how to load compiled code into the evaluator
machine or how to run it. We will assume that the
explicit-control-evaluator machine has been defined as in [Section
5.4.4](#Section 5.4.4), with the additional operations specified in
[Footnote 38](#Footnote 38). We will implement a procedure
`compile/and/go` that compiles a Scheme expression, loads the resulting
object code into the evaluator machine, and causes the machine to run
the code in the evaluator global environment, print the result, and
enter the evaluator's driver loop. We will also modify the evaluator so
that interpreted expressions can call compiled procedures as well as
interpreted ones. We can then put a compiled procedure into the machine
and use the evaluator to call it:

::: scheme
(compile-and-go '(define (factorial n) (if (= n 1) 1 (\* (factorial (- n
1)) n))))  *;;; EC-Eval value:*   *ok*   *;;; EC-Eval input:*
(factorial 5)  *;;; EC-Eval value:*   *120*
:::

To allow the evaluator to handle compiled procedures (for example, to
evaluate the call to `factorial` above), we need to change the code at
`apply/dispatch` ([Section 5.4.1](#Section 5.4.1)) so that it recognizes
compiled procedures (as distinct from compound or primitive procedures)
and transfers control directly to the entry point of the compiled
code:[^333]

::: scheme
apply-dispatch (test (op primitive-procedure?) (reg proc)) (branch
(label primitive-apply)) (test (op compound-procedure?) (reg proc))
(branch (label compound-apply)) (test (op compiled-procedure?) (reg
proc)) (branch (label compiled-apply)) (goto (label
unknown-procedure-type)) compiled-apply (restore continue) (assign val
(op compiled-procedure-entry) (reg proc)) (goto (reg val))
:::

Note the restore of `continue` at `compiled/apply`. Recall that the
evaluator was arranged so that at `apply/dispatch`, the continuation
would be at the top of the stack. The compiled code entry point, on the
other hand, expects the continuation to be in `continue`, so `continue`
must be restored before the compiled code is executed.

To enable us to run some compiled code when we start the evaluator
machine, we add a `branch` instruction at the beginning of the evaluator
machine, which causes the machine to go to a new entry point if the
`flag` register is set.[^334]

::: scheme
(branch (label external-entry))  [; branches if `flag` is set]{.roman}
read-eval-print-loop (perform (op initialize-stack))  $\dots$
:::

`external/entry` assumes that the machine is started with `val`
containing the location of an instruction sequence that puts a result
into `val` and ends with `(goto (reg continue))`. Starting at this entry
point jumps to the location designated by `val`, but first assigns
`continue` so that execution will return to `print/result`, which prints
the value in `val` and then goes to the beginning of the evaluator's
read-eval-print loop.[^335]

::: scheme
external-entry (perform (op initialize-stack)) (assign env (op
get-global-environment)) (assign continue (label print-result)) (goto
(reg val))
:::

Now we can use the following procedure to compile a procedure
definition, execute the compiled code, and run the read-eval-print loop
so we can try the procedure. Because we want the compiled code to return
to the location in `continue` with its result in `val`, we compile the
expression with a target of `val` and a linkage of `return`. In order to
transform the object code produced by the compiler into executable
instructions for the evaluator register machine, we use the procedure
`assemble` from the register-machine simulator ([Section
5.2.2](#Section 5.2.2)). We then initialize the `val` register to point
to the list of instructions, set the `flag` so that the evaluator will
go to `external/entry`, and start the evaluator.

::: scheme
(define (compile-and-go expression) (let ((instructions (assemble
(statements (compile expression 'val 'return)) eceval))) (set!
the-global-environment (setup-environment)) (set-register-contents!
eceval 'val instructions) (set-register-contents! eceval 'flag true)
(start eceval)))
:::

If we have set up stack monitoring, as at the end of [Section
5.4.4](#Section 5.4.4), we can examine the stack usage of compiled code:

::: scheme
(compile-and-go '(define (factorial n) (if (= n 1) 1 (\* (factorial (- n
1)) n))))  *(total-pushes = 0 maximum-depth = 0)*   *;;; EC-Eval
value:*   *ok*   *;;; EC-Eval input:*  (factorial 5)  *(total-pushes =
31 maximum-depth = 14)*   *;;; EC-Eval value:*   *120*
:::

Compare this example with the evaluation of `(factorial 5)` using the
interpreted version of the same procedure, shown at the end of [Section
5.4.4](#Section 5.4.4). The interpreted version required 144 pushes and
a maximum stack depth of 28. This illustrates the optimization that
results from our compilation strategy.

#### Interpretation and compilation {#interpretation-and-compilation .unnumbered}

With the programs in this section, we can now experiment with the
alternative execution strategies of interpretation and
compilation.[^336] An interpreter raises the machine to the level of the
user program; a compiler lowers the user program to the level of the
machine language. We can regard the Scheme language (or any programming
language) as a coherent family of abstractions erected on the machine
language. Interpreters are good for interactive program development and
debugging because the steps of program execution are organized in terms
of these abstractions, and are therefore more intelligible to the
programmer. Compiled code can execute faster, because the steps of
program execution are organized in terms of the machine language, and
the compiler is free to make optimizations that cut across the
higher-level abstractions.[^337]

The alternatives of interpretation and compilation also lead to
different strategies for porting languages to new computers. Suppose
that we wish to implement Lisp for a new machine. One strategy is to
begin with the explicit-control evaluator of [Section 5.4](#Section 5.4)
and translate its instructions to instructions for the new machine. A
different strategy is to begin with the compiler and change the code
generators so that they generate code for the new machine. The second
strategy allows us to run any Lisp program on the new machine by first
compiling it with the compiler running on our original Lisp system, and
linking it with a compiled version of the run-time library.[^338] Better
yet, we can compile the compiler itself, and run this on the new machine
to compile other Lisp programs.[^339] Or we can compile one of the
interpreters of [Section 4.1](#Section 4.1) to produce an interpreter
that runs on the new machine.

> **[]{#Exercise 5.45 label="Exercise 5.45"}Exercise 5.45:** By
> comparing the stack operations used by compiled code to the stack
> operations used by the evaluator for the same computation, we can
> determine the extent to which the compiler optimizes use of the stack,
> both in speed (reducing the total number of stack operations) and in
> space (reducing the maximum stack depth). Comparing this optimized
> stack use to the performance of a special-purpose machine for the same
> computation gives some indication of the quality of the compiler.
>
> a.  [Exercise 5.27](#Exercise 5.27) asked you to determine, as a
>     function of $n$, the number of pushes and the maximum stack depth
>     needed by the evaluator to compute $n!$ using the recursive
>     factorial procedure given above. [Exercise 5.14](#Exercise 5.14)
>     asked you to do the same measurements for the special-purpose
>     factorial machine shown in [Figure 5.11](#Figure 5.11). Now
>     perform the same analysis using the compiled `factorial`
>     procedure.
>
>     Take the ratio of the number of pushes in the compiled version to
>     the number of pushes in the interpreted version, and do the same
>     for the maximum stack depth. Since the number of operations and
>     the stack depth used to compute $n!$ are linear in $n$, these
>     ratios should approach constants as $n$ becomes large. What are
>     these constants? Similarly, find the ratios of the stack usage in
>     the special-purpose machine to the usage in the interpreted
>     version.
>
>     Compare the ratios for special-purpose versus interpreted code to
>     the ratios for compiled versus interpreted code. You should find
>     that the special-purpose machine does much better than the
>     compiled code, since the hand-tailored controller code should be
>     much better than what is produced by our rudimentary
>     general-purpose compiler.
>
> b.  Can you suggest improvements to the compiler that would help it
>     generate code that would come closer in performance to the
>     hand-tailored version?

> **[]{#Exercise 5.46 label="Exercise 5.46"}Exercise 5.46:** Carry out
> an analysis like the one in [Exercise 5.45](#Exercise 5.45) to
> determine the effectiveness of compiling the tree-recursive Fibonacci
> procedure
>
> ::: scheme
> (define (fib n) (if (\< n 2) n (+ (fib (- n 1)) (fib (- n 2)))))
> :::
>
> compared to the effectiveness of using the special-purpose Fibonacci
> machine of [Figure 5.12](#Figure 5.12). (For measurement of the
> interpreted performance, see [Exercise 5.29](#Exercise 5.29).) For
> Fibonacci, the time resource used is not linear in $n;$ hence the
> ratios of stack operations will not approach a limiting value that is
> independent of $n$.

> **[]{#Exercise 5.47 label="Exercise 5.47"}Exercise 5.47:** This
> section described how to modify the explicit-control evaluator so that
> interpreted code can call compiled procedures. Show how to modify the
> compiler so that compiled procedures can call not only primitive
> procedures and compiled procedures, but interpreted procedures as
> well. This requires modifying `compile/procedure/call` to handle the
> case of compound (interpreted) procedures. Be sure to handle all the
> same `target` and `linkage` combinations as in `compile/proc/appl`. To
> do the actual procedure application, the code needs to jump to the
> evaluator's `compound/apply` entry point. This label cannot be
> directly referenced in object code (since the assembler requires that
> all labels referenced by the code it is assembling be defined there),
> so we will add a register called `compapp` to the evaluator machine to
> hold this entry point, and add an instruction to initialize it:
>
> ::: scheme
> (assign compapp (label compound-apply)) (branch (label
> external-entry))  [; branches if `flag` is set]{.roman}
> read-eval-print-loop  $\dots$
> :::
>
> To test your code, start by defining a procedure `f` that calls a
> procedure `g`. Use `compile/and/go` to compile the definition of `f`
> and start the evaluator. Now, typing at the evaluator, define `g` and
> try to call `f`.

> **[]{#Exercise 5.48 label="Exercise 5.48"}Exercise 5.48:** The
> `compile/and/go` interface implemented in this section is awkward,
> since the compiler can be called only once (when the evaluator machine
> is started). Augment the compiler-interpreter interface by providing a
> `compile/and/run` primitive that can be called from within the
> explicit-control evaluator as follows:
>
> ::: scheme
>  *;;; EC-Eval input:*  (compile-and-run '(define (factorial n) (if (=
> n 1) 1 (\* (factorial (- n 1)) n))))  *;;; EC-Eval value:*   *ok*
>  *;;; EC-Eval input:*  (factorial 5)  *;;; EC-Eval value:*   *120*
> :::

> **[]{#Exercise 5.49 label="Exercise 5.49"}Exercise 5.49:** As an
> alternative to using the explicit-control evaluator's read-eval-print
> loop, design a register machine that performs a
> read-compile-execute-print loop. That is, the machine should run a
> loop that reads an expression, compiles it, assembles and executes the
> resulting code, and prints the result. This is easy to run in our
> simulated setup, since we can arrange to call the procedures `compile`
> and `assemble` as "register-machine operations."

> **[]{#Exercise 5.50 label="Exercise 5.50"}Exercise 5.50:** Use the
> compiler to compile the metacircular evaluator of [Section
> 4.1](#Section 4.1) and run this program using the register-machine
> simulator. (To compile more than one definition at a time, you can
> package the definitions in a `begin`.) The resulting interpreter will
> run very slowly because of the multiple levels of interpretation, but
> getting all the details to work is an instructive exercise.

> **[]{#Exercise 5.51 label="Exercise 5.51"}Exercise 5.51:** Develop a
> rudimentary implementation of Scheme in C (or some other low-level
> language of your choice) by translating the explicit-control evaluator
> of [Section 5.4](#Section 5.4) into C. In order to run this code you
> will need to also provide appropriate storage-allocation routines and
> other run-time support.

> **[]{#Exercise 5.52 label="Exercise 5.52"}Exercise 5.52:** As a
> counterpoint to [Exercise 5.51](#Exercise 5.51), modify the compiler
> so that it compiles Scheme procedures into sequences of C
> instructions. Compile the metacircular evaluator of [Section
> 4.1](#Section 4.1) to produce a Scheme interpreter written in C.

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# List of Exercises {#list-of-exercises .unnumbered}

[]{#List of Exercises label="List of Exercises"}

# List of Figures {#list-of-figures .unnumbered}

[]{#List of Figures label="List of Figures"}

# Colophon {#colophon .unnumbered}

[]{#Colophon label="Colophon"}

On the cover page is Agostino Ramelli's
bookwheel mechanism from 1588. It could be seen as an early hypertext
navigation aid. This image of the engraving is hosted by J. E. Johnson
of [New
Gottland](http://newgottland.com/2012/02/09/before-the-ereader-there-was-the-wheelreader/ramelli_bookwheel_1032px/).

The typefaces are Linux Libertine for body text and Linux Biolinum for
headings, both by Philipp H. Poll. Typewriter face is Inconsolata
created by Raph Levien and supplemented by Dimosthenis Kaponis and
Takashi Tanigawa in the form of Inconsolata lgc. The cover
page typeface is Alegreya, designed by Juan Pablo del Peral.

Graphic design and typography are done by Andres Raba. Texinfo source is
converted to LaTeX by a Perl script and compiled to pdf by
XeLaTeX. Diagrams are drawn with Inkscape.

[^1]: The *Lisp 1 Programmer's Manual* appeared in 1960, and the *Lisp
    1.5 Programmer's Manual* ([McCarthy et al.
    1965](#McCarthy et al. 1965)) was published in 1962. The early
    history of Lisp is described in [McCarthy 1978](#McCarthy 1978).

[^2]: The two dialects in which most major Lisp programs of the 1970s
    were written are MacLisp ([Moon 1978](#Moon 1978); [Pitman
    1983](#Pitman 1983)), developed at the mit Project
    mac, and Interlisp ([Teitelman
    1974](#Teitelman 1974)), developed at Bolt Beranek and Newman Inc.
    and the Xerox Palo Alto Research Center. Portable Standard Lisp
    ([Hearn 1969](#Hearn 1969); [Griss 1981](#Griss 1981)) was a Lisp
    dialect designed to be easily portable between different machines.
    MacLisp spawned a number of subdialects, such as Franz Lisp, which
    was developed at the University of California at Berkeley, and
    Zetalisp ([Moon and Weinreb 1981](#Moon and Weinreb 1981)), which
    was based on a special-purpose processor designed at the
    mit Artificial Intelligence Laboratory to run Lisp
    very efficiently. The Lisp dialect used in this book, called Scheme
    ([Steele and Sussman 1975](#Steele and Sussman 1975)), was invented
    in 1975 by Guy Lewis Steele Jr. and Gerald Jay Sussman of the
    mit Artificial Intelligence Laboratory and later
    reimplemented for instructional use at mit. Scheme
    became an ieee standard in 1990 ([IEEE
    1990](#IEEE 1990)). The Common Lisp dialect ([Steele
    1982](#Steele 1982), [Steele 1990](#Steele 1990)) was developed by
    the Lisp community to combine features from the earlier Lisp
    dialects to make an industrial standard for Lisp. Common Lisp became
    an ansi standard in 1994 ([ANSI 1994](#ANSI 1994)).

[^3]: One such special application was a breakthrough computation of
    scientific importance---an integration of the motion of the Solar
    System that extended previous results by nearly two orders of
    magnitude, and demonstrated that the dynamics of the Solar System is
    chaotic. This computation was made possible by new integration
    algorithms, a special-purpose compiler, and a special-purpose
    computer all implemented with the aid of software tools written in
    Lisp ([Abelson et al. 1992](#Abelson et al. 1992); [Sussman and
    Wisdom 1992](#Sussman and Wisdom 1992)).

[^4]: The characterization of numbers as "simple data" is a barefaced
    bluff. In fact, the treatment of numbers is one of the trickiest and
    most confusing aspects of any programming language. Some typical
    issues involved are these: Some computer systems distinguish
    *integers*, such as 2, from *real numbers*, such as 2.71. Is the
    real number 2.00 different from the integer 2? Are the arithmetic
    operations used for integers the same as the operations used for
    real numbers? Does 6 divided by 2 produce 3, or 3.0? How large a
    number can we represent? How many decimal places of accuracy can we
    represent? Is the range of integers the same as the range of real
    numbers? Above and beyond these questions, of course, lies a
    collection of issues concerning roundoff and truncation errors---the
    entire science of numerical analysis. Since our focus in this book
    is on large-scale program design rather than on numerical
    techniques, we are going to ignore these problems. The numerical
    examples in this chapter will exhibit the usual roundoff behavior
    that one observes when using arithmetic operations that preserve a
    limited number of decimal places of accuracy in noninteger
    operations.

[^5]: Throughout this book, when we wish to emphasize the distinction
    between the input typed by the user and the response printed by the
    interpreter, we will show the latter in slanted characters.

[^6]: Lisp systems typically provide features to aid the user in
    formatting expressions. Two especially useful features are one that
    automatically indents to the proper pretty-print position whenever a
    new line is started and one that highlights the matching left
    parenthesis whenever a right parenthesis is typed.

[^7]: Lisp obeys the convention that every expression has a value. This
    convention, together with the old reputation of Lisp as an
    inefficient language, is the source of the quip by Alan Perlis
    (paraphrasing Oscar Wilde) that "Lisp programmers know the value of
    everything but the cost of nothing."

[^8]: In this book, we do not show the interpreter's response to
    evaluating definitions, since this is highly
    implementation-dependent.

[^9]: [Chapter 3](#Chapter 3) will show that this notion of environment
    is crucial, both for understanding how the interpreter works and for
    implementing interpreters.

[^10]: It may seem strange that the evaluation rule says, as part of the
    first step, that we should evaluate the leftmost element of a
    combination, since at this point that can only be an operator such
    as `+` or `*` representing a built-in primitive procedure such as
    addition or multiplication. We will see later that it is useful to
    be able to work with combinations whose operators are themselves
    compound expressions.

[^11]: Special syntactic forms that are simply convenient alternative
    surface structures for things that can be written in more uniform
    ways are sometimes called *syntactic sugar*, to use a phrase coined
    by Peter Landin. In comparison with users of other languages, Lisp
    programmers, as a rule, are less concerned with matters of syntax.
    (By contrast, examine any Pascal manual and notice how much of it is
    devoted to descriptions of syntax.) This disdain for syntax is due
    partly to the flexibility of Lisp, which makes it easy to change
    surface syntax, and partly to the observation that many "convenient"
    syntactic constructs, which make the language less uniform, end up
    causing more trouble than they are worth when programs become large
    and complex. In the words of Alan Perlis, "Syntactic sugar causes
    cancer of the semicolon."

[^12]: Observe that there are two different operations being combined
    here: we are creating the procedure, and we are giving it the name
    `square`. It is possible, indeed important, to be able to separate
    these two notions---to create procedures without naming them, and to
    give names to procedures that have already been created. We will see
    how to do this in [Section 1.3.2](#Section 1.3.2).

[^13]: Throughout this book, we will describe the general syntax of
    expressions by using italic symbols delimited by angle
    brackets---e.g., $\langle$*name*$\kern0.08em\rangle$---to denote the
    "slots" in the expression to be filled in when such an expression is
    actually used.

[^14]: More generally, the body of the procedure can be a sequence of
    expressions. In this case, the interpreter evaluates each expression
    in the sequence in turn and returns the value of the final
    expression as the value of the procedure application.

[^15]: Despite the simplicity of the substitution idea, it turns out to
    be surprisingly complicated to give a rigorous mathematical
    definition of the substitution process. The problem arises from the
    possibility of confusion between the names used for the formal
    parameters of a procedure and the (possibly identical) names used in
    the expressions to which the procedure may be applied. Indeed, there
    is a long history of erroneous definitions of *substitution* in the
    literature of logic and programming semantics. See [Stoy
    1977](#Stoy 1977) for a careful discussion of substitution.

[^16]: In [Chapter 3](#Chapter 3) we will introduce *stream processing*,
    which is a way of handling apparently "infinite" data structures by
    incorporating a limited form of normal-order evaluation. In [Section
    4.2](#Section 4.2) we will modify the Scheme interpreter to produce
    a normal-order variant of Scheme.

[^17]: "Interpreted as either true or false" means this: In Scheme,
    there are two distinguished values that are denoted by the constants
    `#t` and `#f`. When the interpreter checks a predicate's value, it
    interprets `#f` as false. Any other value is treated as true. (Thus,
    providing `#t` is logically unnecessary, but it is convenient.) In
    this book we will use names `true` and `false`, which are associated
    with the values `#t` and `#f` respectively.

[^18]: `abs` also uses the "minus" operator `-`, which, when used with a
    single operand, as in `(- x)`, indicates negation.

[^19]: A minor difference between `if` and `cond` is that the
    $\langle{e}\rangle$ part of each `cond` clause may be a sequence of
    expressions. If the corresponding $\langle{p}\rangle$ is found to be
    true, the expressions $\langle{e}\rangle$ are evaluated in sequence
    and the value of the final expression in the sequence is returned as
    the value of the `cond`. In an `if` expression, however, the
    $\langle$*consequent*$\kern0.04em\rangle$ and
    $\langle$*alternative*$\kern0.04em\rangle$ must be single
    expressions.

[^20]: Declarative and imperative descriptions are intimately related,
    as indeed are mathematics and computer science. For instance, to say
    that the answer produced by a program is "correct" is to make a
    declarative statement about the program. There is a large amount of
    research aimed at establishing techniques for proving that programs
    are correct, and much of the technical difficulty of this subject
    has to do with negotiating the transition between imperative
    statements (from which programs are constructed) and declarative
    statements (which can be used to deduce things). In a related vein,
    an important current area in programming-language design is the
    exploration of so-called very high-level languages, in which one
    actually programs in terms of declarative statements. The idea is to
    make interpreters sophisticated enough so that, given "what is"
    knowledge specified by the programmer, they can generate "how to"
    knowledge automatically. This cannot be done in general, but there
    are important areas where progress has been made. We shall revisit
    this idea in [Chapter 4](#Chapter 4).

[^21]: This square-root algorithm is actually a special case of Newton's
    method, which is a general technique for finding roots of equations.
    The square-root algorithm itself was developed by Heron of
    Alexandria in the first century a.d. We will see how
    to express the general Newton's method as a Lisp procedure in
    [Section 1.3.4](#Section 1.3.4).

[^22]: We will usually give predicates names ending with question marks,
    to help us remember that they are predicates. This is just a
    stylistic convention. As far as the interpreter is concerned, the
    question mark is just an ordinary character.

[^23]: Observe that we express our initial guess as 1.0 rather than 1.
    This would not make any difference in many Lisp implementations.
    mit Scheme, however, distinguishes between exact
    integers and decimal values, and dividing two integers produces a
    rational number rather than a decimal. For example, dividing 10 by 6
    yields 5/3, while dividing 10.0 by 6.0 yields 1.6666666666666667.
    (We will learn how to implement arithmetic on rational numbers in
    [Section 2.1.1](#Section 2.1.1).) If we start with an initial guess
    of 1 in our square-root program, and $x$ is an exact integer, all
    subsequent values produced in the square-root computation will be
    rational numbers rather than decimals. Mixed operations on rational
    numbers and decimals always yield decimals, so starting with an
    initial guess of 1.0 forces all subsequent values to be decimals.

[^24]: Readers who are worried about the efficiency issues involved in
    using procedure calls to implement iteration should note the remarks
    on "tail recursion" in [Section 1.2.1](#Section 1.2.1).

[^25]: It is not even clear which of these procedures is a more
    efficient implementation. This depends upon the hardware available.
    There are machines for which the "obvious" implementation is the
    less efficient one. Consider a machine that has extensive tables of
    logarithms and antilogarithms stored in a very efficient manner.

[^26]: The concept of consistent renaming is actually subtle and
    difficult to define formally. Famous logicians have made
    embarrassing errors here.

[^27]: Lexical scoping dictates that free variables in a procedure are
    taken to refer to bindings made by enclosing procedure definitions;
    that is, they are looked up in the environment in which the
    procedure was defined. We will see how this works in detail in
    chapter 3 when we study environments and the detailed behavior of
    the interpreter.[]{#Footnote 28 label="Footnote 28"}

[^28]: Embedded definitions must come first in a procedure body. The
    management is not responsible for the consequences of running
    programs that intertwine definition and use.

[^29]: In a real program we would probably use the block structure
    introduced in the last section to hide the definition of
    `fact/iter`:

    ::: smallscheme
    (define (factorial n) (define (iter product counter) (if (\> counter
    n) product (iter (\* counter product) (+ counter 1)))) (iter 1 1))
    :::

    We avoided doing this here so as to minimize the number of things to
    think about at once.

[^30]: When we discuss the implementation of procedures on register
    machines in [Chapter 5](#Chapter 5), we will see that any iterative
    process can be realized "in hardware" as a machine that has a fixed
    set of registers and no auxiliary memory. In contrast, realizing a
    recursive process requires a machine that uses an auxiliary data
    structure known as a *stack*.

[^31]: Tail recursion has long been known as a compiler optimization
    trick. A coherent semantic basis for tail recursion was provided by
    Carl [Hewitt (1977)](#Hewitt (1977)), who explained it in terms of
    the "message-passing" model of computation that we shall discuss in
    [Chapter 3](#Chapter 3). Inspired by this, Gerald Jay Sussman and
    Guy Lewis Steele Jr. (see [Steele and Sussman
    1975](#Steele and Sussman 1975)) constructed a tail-recursive
    interpreter for Scheme. Steele later showed how tail recursion is a
    consequence of the natural way to compile procedure calls ([Steele
    1977](#Steele 1977)). The ieee standard for Scheme
    requires that Scheme implementations be tail-recursive.

[^32]: An example of this was hinted at in [Section
    1.1.3](#Section 1.1.3). The interpreter itself evaluates expressions
    using a tree-recursive process.

[^33]: For example, work through in detail how the reduction rule
    applies to the problem of making change for 10 cents using pennies
    and nickels.

[^34]: One approach to coping with redundant computations is to arrange
    matters so that we automatically construct a table of values as they
    are computed. Each time we are asked to apply the procedure to some
    argument, we first look to see if the value is already stored in the
    table, in which case we avoid performing the redundant computation.
    This strategy, known as *tabulation* or *memoization*, can be
    implemented in a straightforward way. Tabulation can sometimes be
    used to transform processes that require an exponential number of
    steps (such as `count/change`) into processes whose space and time
    requirements grow linearly with the input. See [Exercise
    3.27](#Exercise 3.27).

[^35]: The elements of Pascal's triangle are called the *binomial
    coefficients*, because the $n^{\mathrm{th}}$ row consists of the
    coefficients of the terms in the expansion of $(x + y)^n$. This
    pattern for computing the coefficients appeared in Blaise Pascal's
    1653 seminal work on probability theory, *Traité du triangle
    arithmétique*. According to [Knuth (1973)](#Knuth (1973)), the same
    pattern appears in the *Szu-yuen Yü-chien* ("The Precious Mirror of
    the Four Elements"), published by the Chinese mathematician Chu
    Shih-chieh in 1303, in the works of the twelfth-century Persian poet
    and mathematician Omar Khayyam, and in the works of the
    twelfth-century Hindu mathematician Bháscara Áchárya.

[^36]: These statements mask a great deal of oversimplification. For
    instance, if we count process steps as "machine operations" we are
    making the assumption that the number of machine operations needed
    to perform, say, a multiplication is independent of the size of the
    numbers to be multiplied, which is false if the numbers are
    sufficiently large. Similar remarks hold for the estimates of space.
    Like the design and description of a process, the analysis of a
    process can be carried out at various levels of abstraction.

[^37]: More precisely, the number of multiplications required is equal
    to 1 less than the log base 2 of $n$ plus the number of ones in the
    binary representation of $n$. This total is always less than twice
    the log base 2 of $n$. The arbitrary constants $k_1$ and $k_2$ in
    the definition of order notation imply that, for a logarithmic
    process, the base to which logarithms are taken does not matter, so
    all such processes are described as $\Theta(\log n)$.

[^38]: You may wonder why anyone would care about raising numbers to the
    1000th power. See [Section 1.2.6](#Section 1.2.6).

[^39]: This iterative algorithm is ancient. It appears in the
    *Chandah-sutra* by Áchárya Pingala, written before 200
    b.c. See [Knuth 1981](#Knuth 1981), section 4.6.3, for
    a full discussion and analysis of this and other methods of
    exponentiation.

[^40]: This algorithm, which is sometimes known as the "Russian peasant
    method" of multiplication, is ancient. Examples of its use are found
    in the Rhind Papyrus, one of the two oldest mathematical documents
    in existence, written about 1700 b.c. (and copied from
    an even older document) by an Egyptian scribe named A$\!$'h-mose.

[^41]: This exercise was suggested to us by Joe Stoy, based on an
    example in [Kaldewaij 1990](#Kaldewaij 1990).

[^42]: Euclid's Algorithm is so called because it appears in Euclid's
    *Elements* (Book 7, ca. 300 b.c.). According to [Knuth
    (1973)](#Knuth (1973)), it can be considered the oldest known
    nontrivial algorithm. The ancient Egyptian method of multiplication
    ([Exercise 1.18](#Exercise 1.18)) is surely older, but, as Knuth
    explains, Euclid's algorithm is the oldest known to have been
    presented as a general algorithm, rather than as a set of
    illustrative examples.

[^43]: This theorem was proved in 1845 by Gabriel Lamé, a French
    mathematician and engineer known chiefly for his contributions to
    mathematical physics. To prove the theorem, we consider pairs
    ($a_k, b_k$), where $a_k \ge
    b_k$, for which Euclid's Algorithm terminates in $k$ steps. The
    proof is based on the claim that, if $(a_{k+1}, b_{k+1}) \to
    (a_k, b_k) \to (a_{k-1}, b_{k-1})$ are three successive pairs in the
    reduction process, then we must have $b_{k+1} \ge
    b_k + b_{k-1}$. To verify the claim, consider that a reduction step
    is defined by applying the transformation $a_{k-1} = b_k,
    b_{k-1} =$ remainder of $a_k$ divided by $b_k$. The second equation
    means that $a_k = qb_k + b_{k-1}$ for some positive integer $q$. And
    since $q$ must be at least 1 we have $a_k
    = qb_k + b_{k-1} \ge b_k + b_{k-1}$. But in the previous reduction
    step we have $b_{k+1} = a_k$. Therefore,
    $b_{k+1} = a_k \ge b_k + b_{k-1}$. This verifies the claim. Now we
    can prove the theorem by induction on $k$, the number of steps that
    the algorithm requires to terminate. The result is true for $k =
    1$, since this merely requires that $b$ be at least as large as
    Fib(1) = 1. Now, assume that the result is true for all integers
    less than or equal to $k$ and establish the result for $k + 1$. Let
    $(a_{k+1},
    b_{k+1}) \to (a_k, b_k) \to (a_{k-1}, b_{k-1})$ be successive pairs
    in the reduction process. By our induction hypotheses, we have
    $b_{k-1} \ge {\rm Fib}(k - 1)$ and $b_k \ge {\rm Fib}(k)$. Thus,
    applying the claim we just proved together with the definition of
    the Fibonacci numbers gives $b_{k+1} \ge
    b_k + b_{k-1} \ge {\rm Fib}(k) + {\rm Fib}(k-1) =
    {\rm Fib}(k+1)$, which completes the proof of Lamé's Theorem.

[^44]: If $d$ is a divisor of $n$, then so is $n / d$. But $d$ and
    $n / d$ cannot both be greater than $\sqrt{n}$.

[^45]: Pierre de Fermat (1601-1665) is considered to be the founder of
    modern number theory. He obtained many important number-theoretic
    results, but he usually announced just the results, without
    providing his proofs. Fermat's Little Theorem was stated in a letter
    he wrote in 1640. The first published proof was given by Euler in
    1736 (and an earlier, identical proof was discovered in the
    unpublished manuscripts of Leibniz). The most famous of Fermat's
    results---known as Fermat's Last Theorem---was jotted down in 1637
    in his copy of the book *Arithmetic* (by the third-century Greek
    mathematician Diophantus) with the remark "I have discovered a truly
    remarkable proof, but this margin is too small to contain it."
    Finding a proof of Fermat's Last Theorem became one of the most
    famous challenges in number theory. A complete solution was finally
    given in 1995 by Andrew Wiles of Princeton University.

[^46]: The reduction steps in the cases where the exponent $e$ is
    greater than 1 are based on the fact that, for any integers $x$,
    $y$, and $m$, we can find the remainder of $x$ times $y$ modulo $m$
    by computing separately the remainders of $x$ modulo $m$ and $y$
    modulo $m$, multiplying these, and then taking the remainder of the
    result modulo $m$. For instance, in the case where $e$ is even, we
    compute the remainder of $b^{e / 2}$ modulo $m$, square this, and
    take the remainder modulo $m$. This technique is useful because it
    means we can perform our computation without ever having to deal
    with numbers much larger than $m$. (Compare [Exercise
    1.25](#Exercise 1.25).)

[^47]: []{#Footnote 1.47 label="Footnote 1.47"} Numbers that fool the
    Fermat test are called *Carmichael numbers*, and little is known
    about them other than that they are extremely rare. There are 255
    Carmichael numbers below 100,000,000. The smallest few are 561,
    1105, 1729, 2465, 2821, and 6601. In testing primality of very large
    numbers chosen at random, the chance of stumbling upon a value that
    fools the Fermat test is less than the chance that cosmic radiation
    will cause the computer to make an error in carrying out a "correct"
    algorithm. Considering an algorithm to be inadequate for the first
    reason but not for the second illustrates the difference between
    mathematics and engineering.

[^48]: One of the most striking applications of probabilistic prime
    testing has been to the field of cryptography. Although it is now
    computationally infeasible to factor an arbitrary 200-digit number,
    the primality of such a number can be checked in a few seconds with
    the Fermat test. This fact forms the basis of a technique for
    constructing "unbreakable codes" suggested by [Rivest et al.
    (1977)](#Rivest et al. (1977)). The resulting *RSA algorithm* has
    become a widely used technique for enhancing the security of
    electronic communications. Because of this and related developments,
    the study of prime numbers, once considered the epitome of a topic
    in "pure" mathematics to be studied only for its own sake, now turns
    out to have important practical applications to cryptography,
    electronic funds transfer, and information retrieval.

[^49]: This series, usually written in the equivalent form
    ${\pi\over4} = 1 - {1\over3} + {1\over5} - {1\over7} + \dots$, is
    due to Leibniz. We'll see how to use this as the basis for some
    fancy numerical tricks in [Section 3.5.3](#Section 3.5.3).

[^50]: Notice that we have used block structure ([Section
    1.1.8](#Section 1.1.8)) to embed the definitions of `pi/next` and
    `pi/term` within `pi/sum`, since these procedures are unlikely to be
    useful for any other purpose. We will see how to get rid of them
    altogether in [Section 1.3.2](#Section 1.3.2).

[^51]: The intent of [Exercise 1.31](#Exercise 1.31) through [Exercise
    1.33](#Exercise 1.33) is to demonstrate the expressive power that is
    attained by using an appropriate abstraction to consolidate many
    seemingly disparate operations. However, though accumulation and
    filtering are elegant ideas, our hands are somewhat tied in using
    them at this point since we do not yet have data structures to
    provide suitable means of combination for these abstractions. We
    will return to these ideas in [Section 2.2.3](#Section 2.2.3) when
    we show how to use *sequences* as interfaces for combining filters
    and accumulators to build even more powerful abstractions. We will
    see there how these methods really come into their own as a powerful
    and elegant approach to designing programs.

[^52]: This formula was discovered by the seventeenth-century English
    mathematician John Wallis.

[^53]: It would be clearer and less intimidating to people learning Lisp
    if a name more obvious than `lambda`, such as `make/procedure`, were
    used. But the convention is firmly entrenched. The notation is
    adopted from the λ-calculus, a mathematical formalism introduced by
    the mathematical logician Alonzo [Church (1941)](#Church (1941)).
    Church developed the λ-calculus to provide a rigorous foundation for
    studying the notions of function and function application. The
    λ-calculus has become a basic tool for mathematical investigations
    of the semantics of programming languages.

[^54]: Understanding internal definitions well enough to be sure a
    program means what we intend it to mean requires a more elaborate
    model of the evaluation process than we have presented in this
    chapter. The subtleties do not arise with internal definitions of
    procedures, however. We will return to this issue in [Section
    4.1.6](#Section 4.1.6), after we learn more about evaluation.

[^55]: We have used 0.001 as a representative "small" number to indicate
    a tolerance for the acceptable error in a calculation. The
    appropriate tolerance for a real calculation depends upon the
    problem to be solved and the limitations of the computer and the
    algorithm. This is often a very subtle consideration, requiring help
    from a numerical analyst or some other kind of magician.

[^56]: This can be accomplished using `error`, which takes as arguments
    a number of items that are printed as error messages.

[^57]: Try this during a boring lecture: Set your calculator to radians
    mode and then repeatedly press the `cos` button until you obtain the
    fixed point.

[^58]: $\mapsto$ (pronounced "maps to") is the mathematician's way of
    writing `lambda`. $y \mapsto x / y$ means `(lambda (y) (/ x y))`,
    that is, the function whose value at $y$ is $x / y$.

[^59]: Observe that this is a combination whose operator is itself a
    combination. [Exercise 1.4](#Exercise 1.4) already demonstrated the
    ability to form such combinations, but that was only a toy example.
    Here we begin to see the real need for such combinations---when
    applying a procedure that is obtained as the value returned by a
    higher-order procedure.

[^60]: See [Exercise 1.45](#Exercise 1.45) for a further generalization.

[^61]: Elementary calculus books usually describe Newton's method in
    terms of the sequence of approximations $x_{n+1} = x_n -
    g(x_n) / Dg(x_n)$. Having language for talking about processes and
    using the idea of fixed points simplifies the description of the
    method.

[^62]: Newton's method does not always converge to an answer, but it can
    be shown that in favorable cases each iteration doubles the
    number-of-digits accuracy of the approximation to the solution. In
    such cases, Newton's method will converge much more rapidly than the
    half-interval method.

[^63]: For finding square roots, Newton's method converges rapidly to
    the correct solution from any starting point.

[^64]: The notion of first-class status of programming-language elements
    is due to the British computer scientist Christopher Strachey
    (1916-1975).

[^65]: We'll see examples of this after we introduce data structures in
    [Chapter 2](#Chapter 2).

[^66]: The major implementation cost of first-class procedures is that
    allowing procedures to be returned as values requires reserving
    storage for a procedure's free variables even while the procedure is
    not executing. In the Scheme implementation we will study in
    [Section 4.1](#Section 4.1), these variables are stored in the
    procedure's environment.

[^67]: The ability to directly manipulate procedures provides an
    analogous increase in the expressive power of a programming
    language. For example, in [Section 1.3.1](#Section 1.3.1) we
    introduced the `sum` procedure, which takes a procedure `term` as an
    argument and computes the sum of the values of `term` over some
    specified interval. In order to define `sum`, it is crucial that we
    be able to speak of a procedure such as `term` as an entity in its
    own right, without regard for how `term` might be expressed with
    more primitive operations. Indeed, if we did not have the notion of
    "a procedure," it is doubtful that we would ever even think of the
    possibility of defining an operation such as `sum`. Moreover,
    insofar as performing the summation is concerned, the details of how
    `term` may be constructed from more primitive operations are
    irrelevant.

[^68]: The name `cons` stands for "construct." The names `car` and `cdr`
    derive from the original implementation of Lisp on the [ibm
    704]{.smallcaps}. That machine had an addressing scheme that allowed
    one to reference the "address" and "decrement" parts of a memory
    location. `car` stands for "Contents of Address part of Register"
    and `cdr` (pronounced "could-er") stands for "Contents of Decrement
    part of Register."

[^69]: Another way to define the selectors and constructor is

    ::: smallscheme
    (define make-rat cons) (define numer car) (define denom cdr)
    :::

    The first definition associates the name `make/rat` with the value
    of the expression `cons`, which is the primitive procedure that
    constructs pairs. Thus `make/rat` and `cons` are names for the same
    primitive constructor.

    Defining selectors and constructors in this way is efficient:
    Instead of `make/rat` *calling* `cons`, `make/rat` *is* `cons`, so
    there is only one procedure called, not two, when `make/rat` is
    called. On the other hand, doing this defeats debugging aids that
    trace procedure calls or put breakpoints on procedure calls: You may
    want to watch `make/rat` being called, but you certainly don't want
    to watch every call to `cons`.

    We have chosen not to use this style of definition in this book.

[^70]: `display` is the Scheme primitive for printing data. The Scheme
    primitive `newline` starts a new line for printing. Neither of these
    procedures returns a useful value, so in the uses of `print/rat`
    below, we show only what `print/rat` prints, not what the
    interpreter prints as the value returned by `print/rat`.

[^71]: Surprisingly, this idea is very difficult to formulate
    rigorously. There are two approaches to giving such a formulation.
    One, pioneered by C. A. R. [Hoare (1972)](#Hoare (1972)), is known
    as the method of *abstract models*. It formalizes the "procedures
    plus conditions" specification as outlined in the rational-number
    example above. Note that the condition on the rational-number
    representation was stated in terms of facts about integers (equality
    and division). In general, abstract models define new kinds of data
    objects in terms of previously defined types of data objects.
    Assertions about data objects can therefore be checked by reducing
    them to assertions about previously defined data objects. Another
    approach, introduced by Zilles at mit, by Goguen,
    Thatcher, Wagner, and Wright at ibm (see [Thatcher et
    al. 1978](#Thatcher et al. 1978)), and by Guttag at Toronto (see
    [Guttag 1977](#Guttag 1977)), is called *algebraic specification*.
    It regards the "procedures" as elements of an abstract algebraic
    system whose behavior is specified by axioms that correspond to our
    "conditions," and uses the techniques of abstract algebra to check
    assertions about data objects. Both methods are surveyed in the
    paper by [Liskov and Zilles (1975)](#Liskov and Zilles (1975)).

[^72]: The use of the word "closure" here comes from abstract algebra,
    where a set of elements is said to be closed under an operation if
    applying the operation to elements in the set produces an element
    that is again an element of the set. The Lisp community also
    (unfortunately) uses the word "closure" to describe a totally
    unrelated concept: A closure is an implementation technique for
    representing procedures with free variables. We do not use the word
    "closure" in this second sense in this book.

[^73]: The notion that a means of combination should satisfy closure is
    a straightforward idea. Unfortunately, the data combiners provided
    in many popular programming languages do not satisfy closure, or
    make closure cumbersome to exploit. In Fortran or Basic, one
    typically combines data elements by assembling them into
    arrays---but one cannot form arrays whose elements are themselves
    arrays. Pascal and C admit structures whose elements are structures.
    However, this requires that the programmer manipulate pointers
    explicitly, and adhere to the restriction that each field of a
    structure can contain only elements of a prespecified form. Unlike
    Lisp with its pairs, these languages have no built-in
    general-purpose glue that makes it easy to manipulate compound data
    in a uniform way. This limitation lies behind Alan Perlis's comment
    in his foreword to this book: "In Pascal the plethora of declarable
    data structures induces a specialization within functions that
    inhibits and penalizes casual cooperation. It is better to have 100
    functions operate on one data structure than to have 10 functions
    operate on 10 data structures."

[^74]: In this book, we use *list* to mean a chain of pairs terminated
    by the end-of-list marker. In contrast, the term *list structure*
    refers to any data structure made out of pairs, not just to lists.

[^75]: Since nested applications of `car` and `cdr` are cumbersome to
    write, Lisp dialects provide abbreviations for them---for instance,

    ::: smallscheme
    (cadr
     $\color{SchemeDark}\langle$  *arg*  $\color{SchemeDark}\rangle$ ) =
    (car (cdr
     $\color{SchemeDark}\langle$  *arg*  $\color{SchemeDark}\rangle$ ))
    :::

    The names of all such procedures start with `c` and end with `r`.
    Each `a` between them stands for a `car` operation and each `d` for
    a `cdr` operation, to be applied in the same order in which they
    appear in the name. The names `car` and `cdr` persist because simple
    combinations like `cadr` are pronounceable.

[^76]: It's remarkable how much energy in the standardization of Lisp
    dialects has been dissipated in arguments that are literally over
    nothing: Should `nil` be an ordinary name? Should the value of `nil`
    be a symbol? Should it be a list? Should it be a pair? In Scheme,
    `nil` is an ordinary name, which we use in this section as a
    variable whose value is the end-of-list marker (just as `true` is an
    ordinary variable that has a true value). Other dialects of Lisp,
    including Common Lisp, treat `nil` as a special symbol. The authors
    of this book, who have endured too many language standardization
    brawls, would like to avoid the entire issue. Once we have
    introduced quotation in [Section 2.3](#Section 2.3), we will denote
    the empty list as `’()` and dispense with the variable `nil`
    entirely.

[^77]: To define `f` and `g` using `lambda` we would write

    ::: smallscheme
    (define f (lambda (x y . z)
     $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ ))
    (define g (lambda w
     $\color{SchemeDark}\langle$  *body*  $\color{SchemeDark}\rangle$ ))
    :::

[^78]: []{#Footnote 12 label="Footnote 12"} Scheme standardly provides a
    `map` procedure that is more general than the one described here.
    This more general `map` takes a procedure of $n$ arguments, together
    with $n$ lists, and applies the procedure to all the first elements
    of the lists, all the second elements of the lists, and so on,
    returning a list of the results. For example:

    ::: smallscheme
    (map + (list 1 2 3) (list 40 50 60) (list 700 800 900))  *(741 852
    963)*  (map (lambda (x y) (+ x (\* 2 y))) (list 1 2 3) (list 4 5 6))
     *(9 12 15)*
    :::

[^79]: The order of the first two clauses in the `cond` matters, since
    the empty list satisfies `null?` and also is not a pair.

[^80]: This is, in fact, precisely the `fringe` procedure from [Exercise
    2.28](#Exercise 2.28). Here we've renamed it to emphasize that it is
    part of a family of general sequence-manipulation procedures.

[^81]: Richard [Waters (1979)](#Waters (1979)) developed a program that
    automatically analyzes traditional Fortran programs, viewing them in
    terms of maps, filters, and accumulations. He found that fully 90
    percent of the code in the Fortran Scientific Subroutine Package
    fits neatly into this paradigm. One of the reasons for the success
    of Lisp as a programming language is that lists provide a standard
    medium for expressing ordered collections so that they can be
    manipulated using higher-order operations. The programming language
    APL owes much of its power and appeal to a similar choice. In APL
    all data are represented as arrays, and there is a universal and
    convenient set of generic operators for all sorts of array
    operations.

[^82]: According to [Knuth 1981](#Knuth 1981), this rule was formulated
    by W. G. Horner early in the nineteenth century, but the method was
    actually used by Newton over a hundred years earlier. Horner's rule
    evaluates the polynomial using fewer additions and multiplications
    than does the straightforward method of first computing $a_n x^n$,
    then adding $a_{n-1}x^{n-1}$, and so on. In fact, it is possible to
    prove that any algorithm for evaluating arbitrary polynomials must
    use at least as many additions and multiplications as does Horner's
    rule, and thus Horner's rule is an optimal algorithm for polynomial
    evaluation. This was proved (for the number of additions) by A. M.
    Ostrowski in a 1954 paper that essentially founded the modern study
    of optimal algorithms. The analogous statement for multiplications
    was proved by V. Y. Pan in 1966. The book by [Borodin and Munro
    (1975)](#Borodin and Munro (1975)) provides an overview of these and
    other results about optimal algorithms.

[^83]: This definition uses the extended version of `map` described in
    [Footnote 12](#Footnote 12).

[^84]: This approach to nested mappings was shown to us by David Turner,
    whose languages KRC and Miranda provide elegant formalisms for
    dealing with these constructs. The examples in this section (see
    also [Exercise 2.42](#Exercise 2.42)) are adapted from [Turner
    1981](#Turner 1981). In [Section 3.5.3](#Section 3.5.3), we'll see
    how this approach generalizes to infinite sequences.

[^85]: We're representing a pair here as a list of two elements rather
    than as a Lisp pair. Thus, the "pair" $(i, j)$ is represented as
    `(list i j)`, not `(cons i j)`.

[^86]: The set $S - x$ is the set of all elements of $S$, excluding $x$.

[^87]: Semicolons in Scheme code are used to introduce *comments*.
    Everything from the semicolon to the end of the line is ignored by
    the interpreter. In this book we don't use many comments; we try to
    make our programs self-documenting by using descriptive names.

[^88]: The picture language is based on the language Peter Henderson
    created to construct images like M.C. Escher's "Square Limit"
    woodcut (see [Henderson 1982](#Henderson 1982)). The woodcut
    incorporates a repeated scaled pattern, similar to the arrangements
    drawn using the `square/limit` procedure in this section.

[^89]: William Barton Rogers (1804-1882) was the founder and first
    president of mit. A geologist and talented teacher, he
    taught at William and Mary College and at the University of
    Virginia. In 1859 he moved to Boston, where he had more time for
    research, worked on a plan for establishing a "polytechnic
    institute," and served as Massachusetts's first State Inspector of
    Gas Meters.

    When mit was established in 1861, Rogers was elected
    its first president. Rogers espoused an ideal of "useful learning"
    that was different from the university education of the time, with
    its overemphasis on the classics, which, as he wrote, "stand in the
    way of the broader, higher and more practical instruction and
    discipline of the natural and social sciences." This education was
    likewise to be different from narrow trade-school education. In
    Rogers's words:

    > The world-enforced distinction between the practical and the
    > scientific worker is utterly futile, and the whole experience of
    > modern times has demonstrated its utter worthlessness.

    Rogers served as president of mit until 1870, when he
    resigned due to ill health. In 1878 the second president of
    mit, John Runkle, resigned under the pressure of a
    financial crisis brought on by the Panic of 1873 and strain of
    fighting off attempts by Harvard to take over mit.
    Rogers returned to hold the office of president until 1881.

    Rogers collapsed and died while addressing mit's
    graduating class at the commencement exercises of 1882. Runkle
    quoted Rogers's last words in a memorial address delivered that same
    year:

    > "As I stand here today and see what the Institute is, $\dots$ I
    > call to mind the beginnings of science. I remember one hundred and
    > fifty years ago Stephen Hales published a pamphlet on the subject
    > of illuminating gas, in which he stated that his researches had
    > demonstrated that 128 grains of bituminous coal -- " "Bituminous
    > coal," these were his last words on earth. Here he bent forward,
    > as if consulting some notes on the table before him, then slowly
    > regaining an erect position, threw up his hands, and was
    > translated from the scene of his earthly labors and triumphs to
    > "the tomorrow of death," where the mysteries of life are solved,
    > and the disembodied spirit finds unending satisfaction in
    > contemplating the new and still unfathomable mysteries of the
    > infinite future.

    In the words of Francis A. Walker (mit's third
    president):

    > All his life he had borne himself most faithfully and heroically,
    > and he died as so good a knight would surely have wished, in
    > harness, at his post, and in the very part and act of public duty.

[^90]: Equivalently, we could write

    ::: smallscheme
    (define flipped-pairs (square-of-four identity flip-vert identity
    flip-vert))
    :::

[^91]: `rotate180` rotates a painter by 180 degrees (see [Exercise
    2.50](#Exercise 2.50)). Instead of `rotate180` we could say
    `(compose flip/vert flip/horiz)`, using the `compose` procedure from
    [Exercise 1.42](#Exercise 1.42).

[^92]: `frame/coord/map` uses the vector operations described in
    [Exercise 2.46](#Exercise 2.46) below, which we assume have been
    implemented using some representation for vectors. Because of data
    abstraction, it doesn't matter what this vector representation is,
    so long as the vector operations behave correctly.

[^93]: `segments/>painter` uses the representation for line segments
    described in [Exercise 2.48](#Exercise 2.48) below. It also uses the
    `for/each` procedure described in [Exercise 2.23](#Exercise 2.23).

[^94]: For example, the `rogers` painter of [Figure 2.11](#Figure 2.11)
    was constructed from a gray-level image. For each point in a given
    frame, the `rogers` painter determines the point in the image that
    is mapped to it under the frame coordinate map, and shades it
    accordingly. By allowing different types of painters, we are
    capitalizing on the abstract data idea discussed in [Section
    2.1.3](#Section 2.1.3), where we argued that a rational-number
    representation could be anything at all that satisfies an
    appropriate condition. Here we're using the fact that a painter can
    be implemented in any way at all, so long as it draws something in
    the designated frame. [Section 2.1.3](#Section 2.1.3) also showed
    how pairs could be implemented as procedures. Painters are our
    second example of a procedural representation for data.

[^95]: `rotate90` is a pure rotation only for square frames, because it
    also stretches and shrinks the image to fit into the rotated frame.

[^96]: The diamond-shaped images in [Figure 2.10](#Figure 2.10) and
    [Figure 2.11](#Figure 2.11) were created with `squash/inwards`
    applied to `wave` and `rogers`.

[^97]: [Section 3.3.4](#Section 3.3.4) describes one such language.

[^98]: Allowing quotation in a language wreaks havoc with the ability to
    reason about the language in simple terms, because it destroys the
    notion that equals can be substituted for equals. For example, three
    is one plus two, but the word "three" is not the phrase "one plus
    two." Quotation is powerful because it gives us a way to build
    expressions that manipulate other expressions (as we will see when
    we write an interpreter in [Chapter 4](#Chapter 4)). But allowing
    statements in a language that talk about other statements in that
    language makes it very difficult to maintain any coherent principle
    of what "equals can be substituted for equals" should mean. For
    example, if we know that the evening star is the morning star, then
    from the statement "the evening star is Venus" we can deduce "the
    morning star is Venus." However, given that "John knows that the
    evening star is Venus" we cannot infer that "John knows that the
    morning star is Venus."

[^99]: The single quote is different from the double quote we have been
    using to enclose character strings to be printed. Whereas the single
    quote can be used to denote lists or symbols, the double quote is
    used only with character strings. In this book, the only use for
    character strings is as items to be printed.

[^100]: Strictly, our use of the quotation mark violates the general
    rule that all compound expressions in our language should be
    delimited by parentheses and look like lists. We can recover this
    consistency by introducing a special form `quote`, which serves the
    same purpose as the quotation mark. Thus, we would type `(quote a)`
    instead of `’a`, and we would type `(quote (a b c))` instead of
    `’(a b c)`. This is precisely how the interpreter works. The
    quotation mark is just a single-character abbreviation for wrapping
    the next complete expression with `quote` to form
    $\hbox{\ttfamily(quote}\;\langle\kern0.06em\hbox{\ttfamily\slshape expression}\kern0.08em\rangle\hbox{\ttfamily)}$.
    This is important because it maintains the principle that any
    expression seen by the interpreter can be manipulated as a data
    object. For instance, we could construct the expression
    `(car ’(a b c))`, which is the same as `(car (quote (a b c)))`, by
    evaluating `(list ’car (list ’quote ’(a b c)))`.

[^101]: We can consider two symbols to be "the same" if they consist of
    the same characters in the same order. Such a definition skirts a
    deep issue that we are not yet ready to address: the meaning of
    "sameness" in a programming language. We will return to this in
    [Chapter 3](#Chapter 3) ([Section 3.1.3](#Section 3.1.3)).

[^102]: In practice, programmers use `equal?` to compare lists that
    contain numbers as well as symbols. Numbers are not considered to be
    symbols. The question of whether two numerically equal numbers (as
    tested by `=`) are also `eq?` is highly implementation-dependent. A
    better definition of `equal?` (such as the one that comes as a
    primitive in Scheme) would also stipulate that if `a` and `b` are
    both numbers, then `a` and `b` are `equal?` if they are numerically
    equal.

[^103]: If we want to be more formal, we can specify "consistent with
    the interpretations given above" to mean that the operations satisfy
    a collection of rules such as these:

    $\bullet$ For any set `S` and any object `x`,
    `(element/of/set? x (adjoin/set x S))` is true (informally:
    "Adjoining an object to a set produces a set that contains the
    object").

    $\bullet$ For any sets `S` and `T` and any object `x`,
    `(element/of/set? x (union/set S T))` is equal to
    `(or (element/of/set? x S) (element/of/set? x T))` (informally: "The
    elements of `(union S T)` are the elements that are in `S` or in
    `T`").

    $\bullet$ For any object `x`, `(element/of/set? x ’())` is false
    (informally: "No object is an element of the empty set").

[^104]: Halving the size of the problem at each step is the
    distinguishing characteristic of logarithmic growth, as we saw with
    the fast-exponentiation algorithm of [Section 1.2.4](#Section 1.2.4)
    and the half-interval search method of [Section
    1.3.3](#Section 1.3.3).

[^105]: We are representing sets in terms of trees, and trees in terms
    of lists---in effect, a data abstraction built upon a data
    abstraction. We can regard the procedures `entry`, `left/branch`,
    `right/branch`, and `make/tree` as a way of isolating the
    abstraction of a "binary tree" from the particular way we might wish
    to represent such a tree in terms of list structure.

[^106]: Examples of such structures include *B-trees* and *red-black
    trees*. There is a large literature on data structures devoted to
    this problem. See [Cormen et al. 1990](#Cormen et al. 1990).

[^107]: [Exercise 2.63](#Exercise 2.63) through [Exercise
    2.65](#Exercise 2.65) are due to Paul Hilfinger.

[^108]: See [Hamming 1980](#Hamming 1980) for a discussion of the
    mathematical properties of Huffman codes.

[^109]: In actual computational systems, rectangular form is preferable
    to polar form most of the time because of roundoff errors in
    conversion between rectangular and polar form. This is why the
    complex-number example is unrealistic. Nevertheless, it provides a
    clear illustration of the design of a system using generic
    operations and a good introduction to the more substantial systems
    to be developed later in this chapter.

[^110]: The arctangent function referred to here, computed by Scheme's
    `atan` procedure, is defined so as to take two arguments $y$ and $x$
    and to return the angle whose tangent is $y / x$. The signs of the
    arguments determine the quadrant of the angle.

[^111]: We use the list `(rectangular)` rather than the symbol
    `rectangular` to allow for the possibility of operations with
    multiple arguments, not all of the same type.

[^112]: The type the constructors are installed under needn't be a list
    because a constructor is always used to make an object of one
    particular type.

[^113]: `apply/generic` uses the dotted-tail notation described in
    [Exercise 2.20](#Exercise 2.20), because different generic
    operations may take different numbers of arguments. In
    `apply/generic`, `op` has as its value the first argument to
    `apply/generic` and `args` has as its value a list of the remaining
    arguments.

    `apply/generic` also uses the primitive procedure `apply`, which
    takes two arguments, a procedure and a list. `apply` applies the
    procedure, using the elements in the list as arguments. For example,

    ::: smallscheme
    (apply + (list 1 2 3 4))
    :::

    returns 10.

[^114]: One limitation of this organization is it permits only generic
    procedures of one argument.

[^115]: We also have to supply an almost identical procedure to handle
    the types `(scheme/number complex)`.

[^116]: See [Exercise 2.82](#Exercise 2.82) for generalizations.

[^117]: If we are clever, we can usually get by with fewer than $n^2$
    coercion procedures. For instance, if we know how to convert from
    type 1 to type 2 and from type 2 to type 3, then we can use this
    knowledge to convert from type 1 to type 3. This can greatly
    decrease the number of coercion procedures we need to supply
    explicitly when we add a new type to the system. If we are willing
    to build the required amount of sophistication into our system, we
    can have it search the "graph" of relations among types and
    automatically generate those coercion procedures that can be
    inferred from the ones that are supplied explicitly.

[^118]: This statement, which also appears in the first edition of this
    book, is just as true now as it was when we wrote it twelve years
    ago. Developing a useful, general framework for expressing the
    relations among different types of entities (what philosophers call
    "ontology") seems intractably difficult. The main difference between
    the confusion that existed ten years ago and the confusion that
    exists now is that now a variety of inadequate ontological theories
    have been embodied in a plethora of correspondingly inadequate
    programming languages. For example, much of the complexity of
    object-oriented programming languages---and the subtle and confusing
    differences among contemporary object-oriented languages---centers
    on the treatment of generic operations on interrelated types. Our
    own discussion of computational objects in [Chapter 3](#Chapter 3)
    avoids these issues entirely. Readers familiar with object-oriented
    programming will notice that we have much to say in chapter 3 about
    local state, but we do not even mention "classes" or "inheritance."
    In fact, we suspect that these problems cannot be adequately
    addressed in terms of computer-language design alone, without also
    drawing on work in knowledge representation and automated reasoning.

[^119]: A real number can be projected to an integer using the `round`
    primitive, which returns the closest integer to its argument.

[^120]: On the other hand, we will allow polynomials whose coefficients
    are themselves polynomials in other variables. This will give us
    essentially the same representational power as a full multivariate
    system, although it does lead to coercion problems, as discussed
    below.

[^121]: For univariate polynomials, giving the value of a polynomial at
    a given set of points can be a particularly good representation.
    This makes polynomial arithmetic extremely simple. To obtain, for
    example, the sum of two polynomials represented in this way, we need
    only add the values of the polynomials at corresponding points. To
    transform back to a more familiar representation, we can use the
    Lagrange interpolation formula, which shows how to recover the
    coefficients of a polynomial of degree $n$ given the values of the
    polynomial at $n + 1$ points.

[^122]: This operation is very much like the ordered `union/set`
    operation we developed in [Exercise 2.62](#Exercise 2.62). In fact,
    if we think of the terms of the polynomial as a set ordered
    according to the power of the indeterminate, then the program that
    produces the term list for a sum is almost identical to `union/set`.

[^123]: To make this work completely smoothly, we should also add to our
    generic arithmetic system the ability to coerce a "number" to a
    polynomial by regarding it as a polynomial of degree zero whose
    coefficient is the number. This is necessary if we are going to
    perform operations such as

    $$[x^2 + (y + 1)x + 5] + [x^2 + 2x + 1],$$

    which requires adding the coefficient $y + 1$ to the coefficient 2.

[^124]: In these polynomial examples, we assume that we have implemented
    the generic arithmetic system using the type mechanism suggested in
    [Exercise 2.78](#Exercise 2.78). Thus, coefficients that are
    ordinary numbers will be represented as the numbers themselves
    rather than as pairs whose `car` is the symbol `scheme/number`.

[^125]: Although we are assuming that term lists are ordered, we have
    implemented `adjoin/term` to simply `cons` the new term onto the
    existing term list. We can get away with this so long as we
    guarantee that the procedures (such as `add/terms`) that use
    `adjoin/term` always call it with a higher-order term than appears
    in the list. If we did not want to make such a guarantee, we could
    have implemented `adjoin/term` to be similar to the `adjoin/set`
    constructor for the ordered-list representation of sets ([Exercise
    2.61](#Exercise 2.61)).

[^126]: The fact that Euclid's Algorithm works for polynomials is
    formalized in algebra by saying that polynomials form a kind of
    algebraic domain called a *Euclidean ring*. A Euclidean ring is a
    domain that admits addition, subtraction, and commutative
    multiplication, together with a way of assigning to each element $x$
    of the ring a positive integer "measure" $m(x)$ with the properties
    that $m(xy) \ge m(x)$ for any nonzero $x$ and $y$ and that, given
    any $x$ and $y$, there exists a $q$ such that $y = qx + r$ and
    either $r = 0$ or $m(r) < m(x)$. From an abstract point of view,
    this is what is needed to prove that Euclid's Algorithm works. For
    the domain of integers, the measure $m$ of an integer is the
    absolute value of the integer itself. For the domain of polynomials,
    the measure of a polynomial is its degree.

[^127]: In an implementation like mit Scheme, this
    produces a polynomial that is indeed a divisor of $Q_1$ and $Q_2$,
    but with rational coefficients. In many other Scheme systems, in
    which division of integers can produce limited-precision decimal
    numbers, we may fail to get a valid divisor.

[^128]: One extremely efficient and elegant method for computing
    polynomial gcds was discovered by Richard [Zippel
    (1979)](#Zippel (1979)). The method is a probabilistic algorithm, as
    is the fast test for primality that we discussed in [Chapter
    1](#Chapter
    1). Zippel's book ([Zippel 1993](#Zippel 1993)) describes this
    method, together with other ways to compute polynomial
    gcds.

[^129]: Actually, this is not quite true. One exception was the
    random-number generator in [Section 1.2.6](#Section 1.2.6). Another
    exception involved the operation/type tables we introduced in
    [Section 2.4.3](#Section 2.4.3), where the values of two calls to
    `get` with the same arguments depended on intervening calls to
    `put`. On the other hand, until we introduce assignment, we have no
    way to create such procedures ourselves.

[^130]: The value of a `set!` expression is implementation-dependent.
    `set!` should be used only for its effect, not for its value.

    The name `set!` reflects a naming convention used in Scheme:
    Operations that change the values of variables (or that change data
    structures, as we will see in [Section 3.3](#Section 3.3)) are given
    names that end with an exclamation point. This is similar to the
    convention of designating predicates by names that end with a
    question mark.

[^131]: We have already used `begin` implicitly in our programs, because
    in Scheme the body of a procedure can be a sequence of expressions.
    Also, the $\langle$*consequent*$\kern0.06em\rangle$ part of each
    clause in a `cond` expression can be a sequence of expressions
    rather than a single expression.

[^132]: In programming-language jargon, the variable `balance` is said
    to be *encapsulated* within the `new/withdraw` procedure.
    Encapsulation reflects the general system-design principle known as
    the *hiding principle*: One can make a system more modular and
    robust by protecting parts of the system from each other; that is,
    by providing information access only to those parts of the system
    that have a "need to know."

[^133]: In contrast with `new/withdraw` above, we do not have to use
    `let` to make `balance` a local variable, since formal parameters
    are already local. This will be clearer after the discussion of the
    environment model of evaluation in [Section 3.2](#Section 3.2). (See
    also [Exercise 3.10](#Exercise 3.10).)

[^134]: One common way to implement `rand/update` is to use the rule
    that $x$ is updated to $ax + b$ modulo $m$, where $a$, $b$, and $m$
    are appropriately chosen integers. Chapter 3 of [Knuth
    1981](#Knuth 1981) includes an extensive discussion of techniques
    for generating sequences of random numbers and establishing their
    statistical properties. Notice that the `rand/update` procedure
    computes a mathematical function: Given the same input twice, it
    produces the same output. Therefore, the number sequence produced by
    `rand/update` certainly is not "random," if by "random" we insist
    that each number in the sequence is unrelated to the preceding
    number. The relation between "real randomness" and so-called
    *pseudo-random* sequences, which are produced by well-determined
    computations and yet have suitable statistical properties, is a
    complex question involving difficult issues in mathematics and
    philosophy. Kolmogorov, Solomonoff, and Chaitin have made great
    progress in clarifying these issues; a discussion can be found in
    [Chaitin 1975](#Chaitin 1975).

[^135]: This theorem is due to E. Cesàro. See section 4.5.2 of [Knuth
    1981](#Knuth 1981) for a discussion and a proof.

[^136]: mit Scheme provides such a procedure. If `random`
    is given an exact integer (as in [Section 1.2.6](#Section 1.2.6)) it
    returns an exact integer, but if it is given a decimal value (as in
    this exercise) it returns a decimal value.

[^137]: We don't substitute for the occurrence of `balance` in the
    `set!` expression because the $\langle$*name*$\kern0.08em\rangle$ in
    a `set!` is not evaluated. If we did substitute for it, we would get
    `(set! 25 (- 25 amount))`, which makes no sense.

[^138]: The phenomenon of a single computational object being accessed
    by more than one name is known as *aliasing*. The joint bank account
    situation illustrates a very simple example of an alias. In [Section
    3.3](#Section 3.3) we will see much more complex examples, such as
    "distinct" compound data structures that share parts. Bugs can occur
    in our programs if we forget that a change to an object may also, as
    a "side effect," change a "different" object because the two
    "different" objects are actually a single object appearing under
    different aliases. These so-called *side-effect bugs* are so
    difficult to locate and to analyze that some people have proposed
    that programming languages be designed in such a way as to not allow
    side effects or aliasing ([Lampson et al.
    1981](#Lampson et al. 1981); [Morris et al.
    1980](#Morris et al. 1980)).

[^139]: In view of this, it is ironic that introductory programming is
    most often taught in a highly imperative style. This may be a
    vestige of a belief, common throughout the 1960s and 1970s, that
    programs that call procedures must inherently be less efficient than
    programs that perform assignments. ([Steele 1977](#Steele 1977)
    debunks this argument.) Alternatively it may reflect a view that
    step-by-step assignment is easier for beginners to visualize than
    procedure call. Whatever the reason, it often saddles beginning
    programmers with "should I set this variable before or after that
    one" concerns that can complicate programming and obscure the
    important ideas.

[^140]: Assignment introduces a subtlety into step 1 of the evaluation
    rule. As shown in [Exercise 3.8](#Exercise 3.8), the presence of
    assignment allows us to write expressions that will produce
    different values depending on the order in which the subexpressions
    in a combination are evaluated. Thus, to be precise, we should
    specify an evaluation order in step 1 (e.g., left to right or right
    to left). However, this order should always be considered to be an
    implementation detail, and one should never write programs that
    depend on some particular order. For instance, a sophisticated
    compiler might optimize a program by varying the order in which
    subexpressions are evaluated.

[^141]: If there is already a binding for the variable in the current
    frame, then the binding is changed. This is convenient because it
    allows redefinition of symbols; however, it also means that `define`
    can be used to change values, and this brings up the issues of
    assignment without explicitly using `set!`. Because of this, some
    people prefer redefinitions of existing symbols to signal errors or
    warnings.

[^142]: The environment model will not clarify our claim in [Section
    1.2.1](#Section 1.2.1) that the interpreter can execute a procedure
    such as `fact/iter` in a constant amount of space using tail
    recursion. We will discuss tail recursion when we deal with the
    control structure of the interpreter in [Section 5.4](#Section 5.4).

[^143]: Whether `W1` and `W2` share the same physical code stored in the
    computer, or whether they each keep a copy of the code, is a detail
    of the implementation. For the interpreter we implement in [Chapter
    4](#Chapter 4), the code is in fact shared.

[^144]: `set/car!` and `set/cdr!` return implementation-dependent
    values. Like `set!`, they should be used only for their effect.

[^145]: We see from this that mutation operations on lists can create
    "garbage" that is not part of any accessible structure. We will see
    in [Section 5.3.2](#Section 5.3.2) that Lisp memory-management
    systems include a *garbage collector*, which identifies and recycles
    the memory space used by unneeded pairs.

[^146]: `get/new/pair` is one of the operations that must be implemented
    as part of the memory management required by a Lisp implementation.
    We will discuss this in [Section 5.3.1](#Section 5.3.1).

[^147]: The two pairs are distinct because each call to `cons` returns a
    new pair. The symbols are shared; in Scheme there is a unique symbol
    with any given name. Since Scheme provides no way to mutate a
    symbol, this sharing is undetectable. Note also that the sharing is
    what enables us to compare symbols using `eq?`, which simply checks
    equality of pointers.

[^148]: The subtleties of dealing with sharing of mutable data objects
    reflect the underlying issues of "sameness" and "change" that were
    raised in [Section 3.1.3](#Section 3.1.3). We mentioned there that
    admitting change to our language requires that a compound object
    must have an "identity" that is something different from the pieces
    from which it is composed. In Lisp, we consider this "identity" to
    be the quality that is tested by `eq?`, i.e., by equality of
    pointers. Since in most Lisp implementations a pointer is
    essentially a memory address, we are "solving the problem" of
    defining the identity of objects by stipulating that a data object
    "itself$\kern0.1em$" is the information stored in some particular
    set of memory locations in the computer. This suffices for simple
    Lisp programs, but is hardly a general way to resolve the issue of
    "sameness" in computational models.

[^149]: On the other hand, from the viewpoint of implementation,
    assignment requires us to modify the environment, which is itself a
    mutable data structure. Thus, assignment and mutation are
    equipotent: Each can be implemented in terms of the other.

[^150]: If the first item is the final item in the queue, the front
    pointer will be the empty list after the deletion, which will mark
    the queue as empty; we needn't worry about updating the rear
    pointer, which will still point to the deleted item, because
    `empty/queue?` looks only at the front pointer.

[^151]: Be careful not to make the interpreter try to print a structure
    that contains cycles. (See [Exercise 3.13](#Exercise 3.13).)

[^152]: Because `assoc` uses `equal?`, it can recognize keys that are
    symbols, numbers, or list structure.

[^153]: Thus, the first backbone pair is the object that represents the
    table "itself$\kern0.1em$"; that is, a pointer to the table is a
    pointer to this pair. This same backbone pair always starts the
    table. If we did not arrange things in this way, `insert!` would
    have to return a new value for the start of the table when it added
    a new record.

[^154]: A full-adder is a basic circuit element used in adding two
    binary numbers. Here A and B are the bits at corresponding positions
    in the two numbers to be added, and $\rm C_{in}$ is the carry bit
    from the addition one place to the right. The circuit generates SUM,
    which is the sum bit in the corresponding position, and
    $\rm C_{out}$, which is the carry bit to be propagated to the left.

[^155]: []{#Footnote 27 label="Footnote 27"} These procedures are simply
    syntactic sugar that allow us to use ordinary procedural syntax to
    access the local procedures of objects. It is striking that we can
    interchange the role of "procedures" and "data" in such a simple
    way. For example, if we write `(wire ’get/signal)` we think of
    `wire` as a procedure that is called with the message `get/signal`
    as input. Alternatively, writing `(get/signal wire)` encourages us
    to think of `wire` as a data object that is the input to a procedure
    `get/signal`. The truth of the matter is that, in a language in
    which we can deal with procedures as objects, there is no
    fundamental difference between "procedures" and "data," and we can
    choose our syntactic sugar to allow us to program in whatever style
    we choose.

[^156]: The agenda is a headed list, like the tables in [Section
    3.3.3](#Section 3.3.3), but since the list is headed by the time, we
    do not need an additional dummy header (such as the `*table*` symbol
    used with tables).

[^157]: Observe that the `if` expression in this procedure has no
    $\langle$*alternative*$\kern0.08em\rangle$ expression. Such a
    "one-armed `if` statement" is used to decide whether to do
    something, rather than to select between two expressions. An `if`
    expression returns an unspecified value if the predicate is false
    and there is no $\langle$*alternative*$\kern0.08em\rangle$.

[^158]: In this way, the current time will always be the time of the
    action most recently processed. Storing this time at the head of the
    agenda ensures that it will still be available even if the
    associated time segment has been deleted.

[^159]: Constraint propagation first appeared in the incredibly
    forward-looking sketchpad system of Ivan [Sutherland
    (1963)](#Sutherland (1963)). A beautiful constraint-propagation
    system based on the Smalltalk language was developed by Alan
    [Borning (1977)](#Borning (1977)) at Xerox Palo Alto Research
    Center. Sussman, Stallman, and Steele applied constraint propagation
    to electrical circuit analysis ([Sussman and Stallman
    1975](#Sussman and Stallman 1975); [Sussman and Steele
    1980](#Sussman and Steele 1980)). TK!Solver ([Konopasek and
    Jayaraman 1984](#Konopasek and Jayaraman 1984)) is an extensive
    modeling environment based on constraints.

[^160]: The `setter` might not be a constraint. In our temperature
    example, we used `user` as the `setter`.

[^161]: The expression-oriented format is convenient because it avoids
    the need to name the intermediate expressions in a computation. Our
    original formulation of the constraint language is cumbersome in the
    same way that many languages are cumbersome when dealing with
    operations on compound data. For example, if we wanted to compute
    the product $(a + b) \cdot (c + d)$, where the variables represent
    vectors, we could work in "imperative style," using procedures that
    set the values of designated vector arguments but do not themselves
    return vectors as values:

    (v-sum a b temp1) (v-sum c d temp2) (v-prod temp1 temp2 answer)

    Alternatively, we could deal with expressions, using procedures that
    return vectors as values, and thus avoid explicitly mentioning
    `temp1` and `temp2`:

    (define answer (v-prod (v-sum a b) (v-sum c d)))

    Since Lisp allows us to return compound objects as values of
    procedures, we can transform our imperative-style constraint
    language into an expression-oriented style as shown in this
    exercise. In languages that are impoverished in handling compound
    objects, such as Algol, Basic, and Pascal (unless one explicitly
    uses Pascal pointer variables), one is usually stuck with the
    imperative style when manipulating compound objects. Given the
    advantage of the expression-oriented format, one might ask if there
    is any reason to have implemented the system in imperative style, as
    we did in this section. One reason is that the
    non-expression-oriented constraint language provides a handle on
    constraint objects (e.g., the value of the `adder` procedure) as
    well as on connector objects. This is useful if we wish to extend
    the system with new operations that communicate with constraints
    directly rather than only indirectly via operations on connectors.
    Although it is easy to implement the expression-oriented style in
    terms of the imperative implementation, it is very difficult to do
    the converse.

[^162]: Most real processors actually execute a few operations at a
    time, following a strategy called *pipelining*. Although this
    technique greatly improves the effective utilization of the
    hardware, it is used only to speed up the execution of a sequential
    instruction stream, while retaining the behavior of the sequential
    program.

[^163]: To quote some graffiti seen on a Cambridge building wall: "Time
    is a device that was invented to keep everything from happening at
    once."

[^164]: An even worse failure for this system could occur if the two
    `set!` operations attempt to change the balance simultaneously, in
    which case the actual data appearing in memory might end up being a
    random combination of the information being written by the two
    processes. Most computers have interlocks on the primitive
    memory-write operations, which protect against such simultaneous
    access. Even this seemingly simple kind of protection, however,
    raises implementation challenges in the design of multiprocessing
    computers, where elaborate *cache-coherence* protocols are required
    to ensure that the various processors will maintain a consistent
    view of memory contents, despite the fact that data may be
    replicated ("cached") among the different processors to increase the
    speed of memory access.

[^165]: The factorial program in [Section 3.1.3](#Section 3.1.3)
    illustrates this for a single sequential process.

[^166]: The columns show the contents of Peter's wallet, the joint
    account (in Bank1), Paul's wallet, and Paul's private account (in
    Bank2), before and after each withdrawal (W) and deposit (D). Peter
    withdraws \$10 from Bank1; Paul deposits \$5 in Bank2, then
    withdraws \$25 from Bank1.

[^167]: []{#Footnote 39 label="Footnote 39"} A more formal way to
    express this idea is to say that concurrent programs are inherently
    *nondeterministic*. That is, they are described not by single-valued
    functions, but by functions whose results are sets of possible
    values. In [Section 4.3](#Section 4.3) we will study a language for
    expressing nondeterministic computations.

[^168]: `parallel/execute` is not part of standard Scheme, but it can be
    implemented in mit Scheme. In our implementation, the
    new concurrent processes also run concurrently with the original
    Scheme process. Also, in our implementation, the value returned by
    `parallel/execute` is a special control object that can be used to
    halt the newly created processes.

[^169]: We have simplified `exchange` by exploiting the fact that our
    `deposit` message accepts negative amounts. (This is a serious bug
    in our banking system!)

[^170]: If the account balances start out as \$10, \$20, and \$30, then
    after any number of concurrent exchanges, the balances should still
    be \$10, \$20, and \$30 in some order. Serializing the deposits to
    individual accounts is not sufficient to guarantee this. See
    [Exercise 3.43](#Exercise 3.43).

[^171]: [Exercise 3.45](#Exercise 3.45) investigates why deposits and
    withdrawals are no longer automatically serialized by the account.

[^172]: The term "mutex" is an abbreviation for *mutual exclusion*. The
    general problem of arranging a mechanism that permits concurrent
    processes to safely share resources is called the mutual exclusion
    problem. Our mutex is a simple variant of the *semaphore* mechanism
    (see [Exercise 3.47](#Exercise 3.47)), which was introduced in the
    "THE" Multiprogramming System developed at the Technological
    University of Eindhoven and named for the university's initials in
    Dutch ([Dijkstra 1968a](#Dijkstra 1968a)). The acquire and release
    operations were originally called P and V, from the Dutch words
    *passeren* (to pass) and *vrijgeven* (to release), in reference to
    the semaphores used on railroad systems. Dijkstra's classic
    exposition ([Dijkstra 1968b](#Dijkstra 1968b)) was one of the first
    to clearly present the issues of concurrency control, and showed how
    to use semaphores to handle a variety of concurrency problems.

[^173]: In most time-shared operating systems, processes that are
    blocked by a mutex do not waste time "busy-waiting" as above.
    Instead, the system schedules another process to run while the first
    is waiting, and the blocked process is awakened when the mutex
    becomes available.

[^174]: In mit Scheme for a single processor, which uses a
    time-slicing model, `test/and/set!` can be implemented as follows:

    ::: smallscheme
    (define (test-and-set! cell) (without-interrupts (lambda () (if (car
    cell) true (begin (set-car! cell true) false)))))
    :::

    `without/interrupts` disables time-slicing interrupts while its
    procedure argument is being executed.

[^175]: There are many variants of such instructions---including
    test-and-set, test-and-clear, swap, compare-and-exchange,
    load-reserve, and store-conditional---whose design must be carefully
    matched to the machine's processor-memory interface. One issue that
    arises here is to determine what happens if two processes attempt to
    acquire the same resource at exactly the same time by using such an
    instruction. This requires some mechanism for making a decision
    about which process gets control. Such a mechanism is called an
    *arbiter*. Arbiters usually boil down to some sort of hardware
    device. Unfortunately, it is possible to prove that one cannot
    physically construct a fair arbiter that works 100% of the time
    unless one allows the arbiter an arbitrarily long time to make its
    decision. The fundamental phenomenon here was originally observed by
    the fourteenth-century French philosopher Jean Buridan in his
    commentary on Aristotle's *De caelo*. Buridan argued that a
    perfectly rational dog placed between two equally attractive sources
    of food will starve to death, because it is incapable of deciding
    which to go to first.

[^176]: The general technique for avoiding deadlock by numbering the
    shared resources and acquiring them in order is due to [Havender
    (1968)](#Havender (1968)). Situations where deadlock cannot be
    avoided require *deadlock-recovery* methods, which entail having
    processes "back out" of the deadlocked state and try again.
    Deadlock-recovery mechanisms are widely used in database management
    systems, a topic that is treated in detail in [Gray and Reuter
    1993](#Gray and Reuter 1993).

[^177]: One such alternative to serialization is called *barrier
    synchronization*. The programmer permits concurrent processes to
    execute as they please, but establishes certain synchronization
    points ("barriers") through which no process can proceed until all
    the processes have reached the barrier. Modern processors provide
    machine instructions that permit programmers to establish
    synchronization points at places where consistency is required. The
    PowerPC, for example, includes for this purpose two instructions
    called sync and eieio (Enforced In-order
    Execution of Input/Output).

[^178]: This may seem like a strange point of view, but there are
    systems that work this way. International charges to credit-card
    accounts, for example, are normally cleared on a per-country basis,
    and the charges made in different countries are periodically
    reconciled. Thus the account balance may be different in different
    countries.

[^179]: For distributed systems, this perspective was pursued by
    [Lamport (1978)](#Lamport (1978)), who showed how to use
    communication to establish "global clocks" that can be used to
    establish orderings on events in distributed systems.

[^180]: Physicists sometimes adopt this view by introducing the "world
    lines" of particles as a device for reasoning about motion. We've
    also already mentioned ([Section 2.2.3](#Section 2.2.3)) that this
    is the natural way to think about signal-processing systems. We will
    explore applications of streams to signal processing in [Section
    3.5.3](#Section 3.5.3).

[^181]: Assume that we have a predicate `prime?` (e.g., as in [Section
    1.2.6](#Section 1.2.6)) that tests for primality.

[^182]: In the mit implementation, `the/empty/stream` is
    the same as the empty list `’()`, and `stream/null?` is the same as
    `null?`.

[^183]: This should bother you. The fact that we are defining such
    similar procedures for streams and lists indicates that we are
    missing some underlying abstraction. Unfortunately, in order to
    exploit this abstraction, we will need to exert finer control over
    the process of evaluation than we can at present. We will discuss
    this point further at the end of [Section 3.5.4](#Section 3.5.4). In
    [Section 4.2](#Section 4.2), we'll develop a framework that unifies
    lists and streams.

[^184]: Although `stream/car` and `stream/cdr` can be defined as
    procedures, `cons/stream` must be a special form. If `cons/stream`
    were a procedure, then, according to our model of evaluation,
    evaluating
    `(cons/stream `$\langle$*`a`*$\rangle$` `$\langle$*`b`*$\rangle$`)`
    would automatically cause $\langle$*b*$\kern0.08em\rangle$ to be
    evaluated, which is precisely what we do not want to happen. For the
    same reason, `delay` must be a special form, though `force` can be
    an ordinary procedure.

[^185]: The numbers shown here do not really appear in the delayed
    expression. What actually appears is the original expression, in an
    environment in which the variables are bound to the appropriate
    numbers. For example, `(+ low 1)` with `low` bound to 10,000
    actually appears where `10001` is shown.

[^186]: There are many possible implementations of streams other than
    the one described in this section. Delayed evaluation, which is the
    key to making streams practical, was inherent in Algol 60's
    *call-by-name* parameter-passing method. The use of this mechanism
    to implement streams was first described by [Landin
    (1965)](#Landin (1965)). Delayed evaluation for streams was
    introduced into Lisp by [Friedman and Wise
    (1976)](#Friedman and Wise (1976)). In their implementation, `cons`
    always delays evaluating its arguments, so that lists automatically
    behave as streams. The memoizing optimization is also known as
    *call-by-need*. The Algol community would refer to our original
    delayed objects as *call-by-name thunks* and to the optimized
    versions as *call-by-need thunks*.

[^187]: Exercises such as [Exercise 3.51](#Exercise 3.51) and [Exercise
    3.52](#Exercise 3.52) are valuable for testing our understanding of
    how `delay` works. On the other hand, intermixing delayed evaluation
    with printing---and, even worse, with assignment---is extremely
    confusing, and instructors of courses on computer languages have
    traditionally tormented their students with examination questions
    such as the ones in this section. Needless to say, writing programs
    that depend on such subtleties is odious programming style. Part of
    the power of stream processing is that it lets us ignore the order
    in which events actually happen in our programs. Unfortunately, this
    is precisely what we cannot afford to do in the presence of
    assignment, which forces us to be concerned with time and change.

[^188]: Eratosthenes, a third-century b.c. Alexandrian
    Greek philosopher, is famous for giving the first accurate estimate
    of the circumference of the Earth, which he computed by observing
    shadows cast at noon on the day of the summer solstice.
    Eratosthenes's sieve method, although ancient, has formed the basis
    for special-purpose hardware "sieves" that, until recently, were the
    most powerful tools in existence for locating large primes. Since
    the 70s, however, these methods have been superseded by outgrowths
    of the probabilistic techniques discussed in [Section
    1.2.6](#Section 1.2.6).

[^189]: We have named these figures after Peter Henderson, who was the
    first person to show us diagrams of this sort as a way of thinking
    about stream processing. Each solid line represents a stream of
    values being transmitted. The dashed line from the `car` to the
    `cons` and the `filter` indicates that this is a single value rather
    than a stream.

[^190]: This uses the generalized version of `stream/map` from [Exercise
    3.50](#Exercise 3.50).

[^191]: This last point is very subtle and relies on the fact that
    $p_{n+1} \le p_n^2$. (Here, $p_k$ denotes the $k^{\mathrm{th}}$
    prime.) Estimates such as these are very difficult to establish. The
    ancient proof by Euclid that there are an infinite number of primes
    shows that $p_{n+1} \le p_1 p_2 \cdots p_n + 1$, and no
    substantially better result was proved until 1851, when the Russian
    mathematician P. L. Chebyshev established that $p_{n+1} \le 2p_n$
    for all $n$. This result, originally conjectured in 1845, is known
    as *Bertrand's hypothesis*. A proof can be found in section 22.3 of
    [Hardy and Wright 1960](#Hardy and Wright 1960).

[^192]: This exercise shows how call-by-need is closely related to
    ordinary memoization as described in [Exercise
    3.27](#Exercise 3.27). In that exercise, we used assignment to
    explicitly construct a local table. Our call-by-need stream
    optimization effectively constructs such a table automatically,
    storing values in the previously forced parts of the stream.

[^193]: We can't use `let` to bind the local variable `guesses`, because
    the value of `guesses` depends on `guesses` itself. [Exercise
    3.63](#Exercise 3.63) addresses why we want a local variable here.

[^194]: As in [Section 2.2.3](#Section 2.2.3), we represent a pair of
    integers as a list rather than a Lisp pair.

[^195]: See [Exercise 3.68](#Exercise 3.68) for some insight into why we
    chose this decomposition.

[^196]: The precise statement of the required property on the order of
    combination is as follows: There should be a function $f$ of two
    arguments such that the pair corresponding to element $i$ of the
    first stream and element $j$ of the second stream will appear as
    element number $f(i, j)$ of the output stream. The trick of using
    `interleave` to accomplish this was shown to us by David Turner, who
    employed it in the language KRC ([Turner 1981](#Turner 1981)).

[^197]: We will require that the weighting function be such that the
    weight of a pair increases as we move out along a row or down along
    a column of the array of pairs.

[^198]: To quote from G. H. Hardy's obituary of Ramanujan ([Hardy
    1921](#Hardy 1921)): "It was Mr. Littlewood (I believe) who remarked
    that 'every positive integer was one of his friends.' I remember
    once going to see him when he was lying ill at Putney. I had ridden
    in taxi-cab No. 1729, and remarked that the number seemed to me a
    rather dull one, and that I hoped it was not an unfavorable omen.
    'No,' he replied, 'it is a very interesting number; it is the
    smallest number expressible as the sum of two cubes in two different
    ways.' " The trick of using weighted pairs to generate the Ramanujan
    numbers was shown to us by Charles Leiserson.

[^199]: This procedure is not guaranteed to work in all Scheme
    implementations, although for any implementation there is a simple
    variation that will work. The problem has to do with subtle
    differences in the ways that Scheme implementations handle internal
    definitions. (See [Section 4.1.6](#Section 4.1.6).)

[^200]: This is a small reflection, in Lisp, of the difficulties that
    conventional strongly typed languages such as Pascal have in coping
    with higher-order procedures. In such languages, the programmer must
    specify the data types of the arguments and the result of each
    procedure: number, logical value, sequence, and so on. Consequently,
    we could not express an abstraction such as "map a given procedure
    `proc` over all the elements in a sequence" by a single higher-order
    procedure such as `stream/map`. Rather, we would need a different
    mapping procedure for each different combination of argument and
    result data types that might be specified for a `proc`. Maintaining
    a practical notion of "data type" in the presence of higher-order
    procedures raises many difficult issues. One way of dealing with
    this problem is illustrated by the language ML ([Gordon et al.
    1979](#Gordon et al. 1979)), whose "polymorphic data types" include
    templates for higher-order transformations between data types.
    Moreover, data types for most procedures in ML are never explicitly
    declared by the programmer. Instead, ML includes a
    *type-inferencing* mechanism that uses information in the
    environment to deduce the data types for newly defined procedures.

[^201]: Similarly in physics, when we observe a moving particle, we say
    that the position (state) of the particle is changing. However, from
    the perspective of the particle's world line in space-time there is
    no change involved.

[^202]: John Backus, the inventor of Fortran, gave high visibility to
    functional programming when he was awarded the acm
    Turing award in 1978. His acceptance speech ([Backus
    1978](#Backus 1978)) strongly advocated the functional approach. A
    good overview of functional programming is given in [Henderson
    1980](#Henderson 1980) and in [Darlington et al.
    1982](#Darlington et al. 1982).

[^203]: Observe that, for any two streams, there is in general more than
    one acceptable order of interleaving. Thus, technically, "merge" is
    a relation rather than a function---the answer is not a
    deterministic function of the inputs. We already mentioned
    ([Footnote 39](#Footnote 39)) that nondeterminism is essential when
    dealing with concurrency. The merge relation illustrates the same
    essential nondeterminism, from the functional perspective. In
    [Section 4.3](#Section 4.3), we will look at nondeterminism from yet
    another point of view.

[^204]: The object model approximates the world by dividing it into
    separate pieces. The functional model does not modularize along
    object boundaries. The object model is useful when the unshared
    state of the "objects" is much larger than the state that they
    share. An example of a place where the object viewpoint fails is
    quantum mechanics, where thinking of things as individual particles
    leads to paradoxes and confusions. Unifying the object view with the
    functional view may have little to do with programming, but rather
    with fundamental epistemological issues.

[^205]: The same idea is pervasive throughout all of engineering. For
    example, electrical engineers use many different languages for
    describing circuits. Two of these are the language of electrical
    *networks* and the language of electrical *systems*. The network
    language emphasizes the physical modeling of devices in terms of
    discrete electrical elements. The primitive objects of the network
    language are primitive electrical components such as resistors,
    capacitors, inductors, and transistors, which are characterized in
    terms of physical variables called voltage and current. When
    describing circuits in the network language, the engineer is
    concerned with the physical characteristics of a design. In
    contrast, the primitive objects of the system language are
    signal-processing modules such as filters and amplifiers. Only the
    functional behavior of the modules is relevant, and signals are
    manipulated without concern for their physical realization as
    voltages and currents. The system language is erected on the network
    language, in the sense that the elements of signal-processing
    systems are constructed from electrical networks. Here, however, the
    concerns are with the large-scale organization of electrical devices
    to solve a given application problem; the physical feasibility of
    the parts is assumed. This layered collection of languages is
    another example of the stratified design technique illustrated by
    the picture language of [Section 2.2.4](#Section 2.2.4).

[^206]: The most important features that our evaluator leaves out are
    mechanisms for handling errors and supporting debugging. For a more
    extensive discussion of evaluators, see [Friedman et al.
    1992](#Friedman et al. 1992), which gives an exposition of
    programming languages that proceeds via a sequence of evaluators
    written in Scheme.

[^207]: Even so, there will remain important aspects of the evaluation
    process that are not elucidated by our evaluator. The most important
    of these are the detailed mechanisms by which procedures call other
    procedures and return values to their callers. We will address these
    issues in [Chapter 5](#Chapter 5), where we take a closer look at
    the evaluation process by implementing the evaluator as a simple
    register machine.

[^208]: If we grant ourselves the ability to apply primitives, then what
    remains for us to implement in the evaluator? The job of the
    evaluator is not to specify the primitives of the language, but
    rather to provide the connective tissue---the means of combination
    and the means of abstraction---that binds a collection of primitives
    to form a language. Specifically:

    $\bullet$ The evaluator enables us to deal with nested expressions.
    For example, although simply applying primitives would suffice for
    evaluating the expression `(+ 1 6)`, it is not adequate for handling
    `(+ 1 (* 2 3))`. As far as the primitive procedure `+` is concerned,
    its arguments must be numbers, and it would choke if we passed it
    the expression `(* 2 3)` as an argument. One important role of the
    evaluator is to choreograph procedure composition so that `(* 2 3)`
    is reduced to 6 before being passed as an argument to `+`.

    $\bullet$ The evaluator allows us to use variables. For example, the
    primitive procedure for addition has no way to deal with expressions
    such as `(+ x 1)`. We need an evaluator to keep track of variables
    and obtain their values before invoking the primitive procedures.

    $\bullet$ The evaluator allows us to define compound procedures.
    This involves keeping track of procedure definitions, knowing how to
    use these definitions in evaluating expressions, and providing a
    mechanism that enables procedures to accept arguments.

    $\bullet$ The evaluator provides the special forms, which must be
    evaluated differently from procedure calls.

[^209]: We could have simplified the `application?` clause in `eval` by
    using `map` (and stipulating that `operands` returns a list) rather
    than writing an explicit `list/of/values` procedure. We chose not to
    use `map` here to emphasize the fact that the evaluator can be
    implemented without any use of higher-order procedures (and thus
    could be written in a language that doesn't have higher-order
    procedures), even though the language that it supports will include
    higher-order procedures.

[^210]: In this case, the language being implemented and the
    implementation language are the same. Contemplation of the meaning
    of `true?` here yields expansion of consciousness without the abuse
    of substance.

[^211]: This implementation of `define` ignores a subtle issue in the
    handling of internal definitions, although it works correctly in
    most cases. We will see what the problem is and how to solve it in
    [Section 4.1.6](#Section 4.1.6).

[^212]: As we said when we introduced `define` and `set!`, these values
    are implementation-dependent in Scheme---that is, the implementor
    can choose what value to return.

[^213]: As mentioned in [Section 2.3.1](#Section 2.3.1), the evaluator
    sees a quoted expression as a list beginning with `quote`, even if
    the expression is typed with the quotation mark. For example, the
    expression `’a` would be seen by the evaluator as `(quote a)`. See
    [Exercise 2.55](#Exercise 2.55).

[^214]: The value of an `if` expression when the predicate is false and
    there is no alternative is unspecified in Scheme; we have chosen
    here to make it false. We will support the use of the variables
    `true` and `false` in expressions to be evaluated by binding them in
    the global environment. See [Section 4.1.4](#Section 4.1.4).

[^215]: These selectors for a list of expressions---and the
    corresponding ones for a list of operands---are not intended as a
    data abstraction. They are introduced as mnemonic names for the
    basic list operations in order to make it easier to understand the
    explicit-control evaluator in [Section 5.4](#Section 5.4).

[^216]: The value of a `cond` expression when all the predicates are
    false and there is no `else` clause is unspecified in Scheme; we
    have chosen here to make it false.

[^217]: Practical Lisp systems provide a mechanism that allows a user to
    add new derived expressions and specify their implementation as
    syntactic transformations without modifying the evaluator. Such a
    user-defined transformation is called a *macro*. Although it is easy
    to add an elementary mechanism for defining macros, the resulting
    language has subtle name-conflict problems. There has been much
    research on mechanisms for macro definition that do not cause these
    difficulties. See, for example, [Kohlbecker 1986](#Kohlbecker 1986),
    [Clinger and Rees 1991](#Clinger and Rees 1991), and [Hanson
    1991](#Hanson 1991).

[^218]: Frames are not really a data abstraction in the following code:
    `set/variable/value!` and `define/variable!` use `set/car!` to
    directly modify the values in a frame. The purpose of the frame
    procedures is to make the environment-manipulation procedures easy
    to read.

[^219]: The drawback of this representation (as well as the variant in
    [Exercise 4.11](#Exercise 4.11)) is that the evaluator may have to
    search through many frames in order to find the binding for a given
    variable. (Such an approach is referred to as *deep binding*.) One
    way to avoid this inefficiency is to make use of a strategy called
    *lexical addressing*, which will be discussed in [Section
    5.5.6](#Section 5.5.6).

[^220]: Any procedure defined in the underlying Lisp can be used as a
    primitive for the metacircular evaluator. The name of a primitive
    installed in the evaluator need not be the same as the name of its
    implementation in the underlying Lisp; the names are the same here
    because the metacircular evaluator implements Scheme itself. Thus,
    for example, we could put `(list ’first car)` or
    `(list ’square (lambda (x) (* x x)))` in the list of
    `primitive/procedures`.

[^221]: `apply/in/underlying/scheme` is the `apply` procedure we have
    used in earlier chapters. The metacircular evaluator's `apply`
    procedure ([Section 4.1.1](#Section 4.1.1)) models the working of
    this primitive. Having two different things called `apply` leads to
    a technical problem in running the metacircular evaluator, because
    defining the metacircular evaluator's `apply` will mask the
    definition of the primitive. One way around this is to rename the
    metacircular `apply` to avoid conflict with the name of the
    primitive procedure. We have assumed instead that we have saved a
    reference to the underlying `apply` by doing

    ::: smallscheme
    (define apply-in-underlying-scheme apply)
    :::

    before defining the metacircular `apply`. This allows us to access
    the original version of `apply` under a different name.

[^222]: The primitive procedure `read` waits for input from the user,
    and returns the next complete expression that is typed. For example,
    if the user types `(+ 23 x)`, `read` returns a three-element list
    containing the symbol `+`, the number 23, and the symbol `x`. If the
    user types `’x`, `read` returns a two-element list containing the
    symbol `quote` and the symbol `x`.

[^223]: The fact that the machines are described in Lisp is inessential.
    If we give our evaluator a Lisp program that behaves as an evaluator
    for some other language, say C, the Lisp evaluator will emulate the
    C evaluator, which in turn can emulate any machine described as a C
    program. Similarly, writing a Lisp evaluator in C produces a C
    program that can execute any Lisp program. The deep idea here is
    that any evaluator can emulate any other. Thus, the notion of "what
    can in principle be computed" (ignoring practicalities of time and
    memory required) is independent of the language or the computer, and
    instead reflects an underlying notion of *computability*. This was
    first demonstrated in a clear way by Alan M. Turing (1912-1954),
    whose 1936 paper laid the foundations for theoretical computer
    science. In the paper, Turing presented a simple computational
    model---now known as a *Turing machine*---and argued that any
    "effective process" can be formulated as a program for such a
    machine. (This argument is known as the *Church-Turing thesis*.)
    Turing then implemented a universal machine, i.e., a Turing machine
    that behaves as an evaluator for Turing-machine programs. He used
    this framework to demonstrate that there are well-posed problems
    that cannot be computed by Turing machines (see [Exercise
    4.15](#Exercise 4.15)), and so by implication cannot be formulated
    as "effective processes." Turing went on to make fundamental
    contributions to practical computer science as well. For example, he
    invented the idea of structuring programs using general-purpose
    subroutines. See [Hodges 1983](#Hodges 1983) for a biography of
    Turing.

[^224]: Some people find it counterintuitive that an evaluator, which is
    implemented by a relatively simple procedure, can emulate programs
    that are more complex than the evaluator itself. The existence of a
    universal evaluator machine is a deep and wonderful property of
    computation. *Recursion theory*, a branch of mathematical logic, is
    concerned with logical limits of computation. Douglas Hofstadter's
    beautiful book *Gödel, Escher, Bach* explores some of these ideas
    ([Hofstadter 1979](#Hofstadter 1979)).

[^225]: Warning: This `eval` primitive is not identical to the `eval`
    procedure we implemented in [Section 4.1.1](#Section 4.1.1), because
    it uses *actual* Scheme environments rather than the sample
    environment structures we built in [Section 4.1.3](#Section 4.1.3).
    These actual environments cannot be manipulated by the user as
    ordinary lists; they must be accessed via `eval` or other special
    operations. Similarly, the `apply` primitive we saw earlier is not
    identical to the metacircular `apply`, because it uses actual Scheme
    procedures rather than the procedure objects we constructed in
    [Section 4.1.3](#Section 4.1.3) and [Section 4.1.4](#Section 4.1.4).

[^226]: The mit implementation of Scheme includes `eval`,
    as well as a symbol `user/initial/environment` that is bound to the
    initial environment in which the user's input expressions are
    evaluated.

[^227]: Although we stipulated that `halts?` is given a procedure
    object, notice that this reasoning still applies even if `halts?`
    can gain access to the procedure's text and its environment. This is
    Turing's celebrated *Halting Theorem*, which gave the first clear
    example of a *non-computable* problem, i.e., a well-posed task that
    cannot be carried out as a computational procedure.

[^228]: Wanting programs to not depend on this evaluation mechanism is
    the reason for the "management is not responsible" remark in
    [Footnote 28](#Footnote 28) of [Chapter 1](#Chapter 1). By insisting
    that internal definitions come first and do not use each other while
    the definitions are being evaluated, the ieee standard
    for Scheme leaves implementors some choice in the mechanism used to
    evaluate these definitions. The choice of one evaluation rule rather
    than another here may seem like a small issue, affecting only the
    interpretation of "badly formed" programs. However, we will see in
    [Section 5.5.6](#Section 5.5.6) that moving to a model of
    simultaneous scoping for internal definitions avoids some nasty
    difficulties that would otherwise arise in implementing a compiler.

[^229]: The ieee standard for Scheme allows for different
    implementation strategies by specifying that it is up to the
    programmer to obey this restriction, not up to the implementation to
    enforce it. Some Scheme implementations, including mit
    Scheme, use the transformation shown above. Thus, some programs that
    don't obey this restriction will in fact run in such
    implementations.

[^230]: The mit implementors of Scheme support Alyssa on
    the following grounds: Eva is in principle correct---the definitions
    should be regarded as simultaneous. But it seems difficult to
    implement a general, efficient mechanism that does what Eva
    requires. In the absence of such a mechanism, it is better to
    generate an error in the difficult cases of simultaneous definitions
    (Alyssa's notion) than to produce an incorrect answer (as Ben would
    have it).

[^231]: This example illustrates a programming trick for formulating
    recursive procedures without using `define`. The most general trick
    of this sort is the $Y$ *operator*, which can be used to give a
    "pure λ-calculus" implementation of recursion. (See [Stoy
    1977](#Stoy 1977) for details on the λ-calculus, and [Gabriel
    1988](#Gabriel 1988) for an exposition of the $Y$ operator in
    Scheme.)

[^232]: This technique is an integral part of the compilation process,
    which we shall discuss in [Chapter 5](#Chapter 5). Jonathan Rees
    wrote a Scheme interpreter like this in about 1982 for the T project
    ([Rees and Adams 1982](#Rees and Adams 1982)). Marc [Feeley
    (1986)](#Feeley (1986)) (see also [Feeley and Lapalme
    1987](#Feeley and Lapalme 1987)) independently invented this
    technique in his master's thesis.

[^233]: There is, however, an important part of the variable search that
    *can* be done as part of the syntactic analysis. As we will show in
    [Section 5.5.6](#Section 5.5.6), one can determine the position in
    the environment structure where the value of the variable will be
    found, thus obviating the need to scan the environment for the entry
    that matches the variable.

[^234]: See [Exercise 4.23](#Exercise 4.23) for some insight into the
    processing of sequences.

[^235]: Snarf: "To grab, especially a large document or file for the
    purpose of using it either with or without the owner's permission."
    Snarf down: "To snarf, sometimes with the connotation of absorbing,
    processing, or understanding." (These definitions were snarfed from
    [Steele et al. 1983](#Steele et al. 1983). See also [Raymond
    1993](#Raymond 1993).)

[^236]: The difference between the "lazy" terminology and the
    "normal-order" terminology is somewhat fuzzy. Generally, "lazy"
    refers to the mechanisms of particular evaluators, while
    "normal-order" refers to the semantics of languages, independent of
    any particular evaluation strategy. But this is not a hard-and-fast
    distinction, and the two terminologies are often used
    interchangeably.

[^237]: The "strict" versus "non-strict" terminology means essentially
    the same thing as "applicative-order" versus "normal-order," except
    that it refers to individual procedures and arguments rather than to
    the language as a whole. At a conference on programming languages
    you might hear someone say, "The normal-order language Hassle has
    certain strict primitives. Other procedures take their arguments by
    lazy evaluation."

[^238]: The word *thunk* was invented by an informal working group that
    was discussing the implementation of call-by-name in Algol 60. They
    observed that most of the analysis of ("thinking about") the
    expression could be done at compile time; thus, at run time, the
    expression would already have been "thunk" about ([Ingerman et al.
    1960](#Ingerman et al. 1960)).

[^239]: This is analogous to the use of `force` on the delayed objects
    that were introduced in [Chapter 3](#Chapter 3) to represent
    streams. The critical difference between what we are doing here and
    what we did in [Chapter 3](#Chapter 3) is that we are building
    delaying and forcing into the evaluator, and thus making this
    uniform and automatic throughout the language.

[^240]: Lazy evaluation combined with memoization is sometimes referred
    to as *call-by-need* argument passing, in contrast to *call-by-name*
    argument passing. (Call-by-name, introduced in Algol 60, is similar
    to non-memoized lazy evaluation.) As language designers, we can
    build our evaluator to memoize, not to memoize, or leave this an
    option for programmers ([Exercise 4.31](#Exercise 4.31)). As you
    might expect from [Chapter 3](#Chapter 3), these choices raise
    issues that become both subtle and confusing in the presence of
    assignments. (See [Exercise 4.27](#Exercise 4.27) and [Exercise
    4.29](#Exercise 4.29).) An excellent article by [Clinger
    (1982)](#Clinger (1982)) attempts to clarify the multiple dimensions
    of confusion that arise here.

[^241]: Notice that we also erase the `env` from the thunk once the
    expression's value has been computed. This makes no difference in
    the values returned by the interpreter. It does help save space,
    however, because removing the reference from the thunk to the `env`
    once it is no longer needed allows this structure to be
    *garbage-collected* and its space recycled, as we will discuss in
    [Section 5.3](#Section 5.3).

    Similarly, we could have allowed unneeded environments in the
    memoized delayed objects of [Section 3.5.1](#Section 3.5.1) to be
    garbage-collected, by having `memo/proc` do something like
    `(set! proc ’())` to discard the procedure `proc` (which includes
    the environment in which the `delay` was evaluated) after storing
    its value.

[^242]: This exercise demonstrates that the interaction between lazy
    evaluation and side effects can be very confusing. This is just what
    you might expect from the discussion in [Chapter 3](#Chapter 3).

[^243]: This is precisely the issue with the `unless` procedure, as in
    [Exercise 4.26](#Exercise 4.26).

[^244]: This is the procedural representation described in [Exercise
    2.4](#Exercise 2.4). Essentially any procedural representation
    (e.g., a message-passing implementation) would do as well. Notice
    that we can install these definitions in the lazy evaluator simply
    by typing them at the driver loop. If we had originally included
    `cons`, `car`, and `cdr` as primitives in the global environment,
    they will be redefined. (Also see [Exercise 4.33](#Exercise 4.33)
    and [Exercise 4.34](#Exercise 4.34).)

[^245]: This permits us to create delayed versions of more general kinds
    of list structures, not just sequences. [Hughes 1990](#Hughes 1990)
    discusses some applications of "lazy trees."

[^246]: We assume that we have previously defined a procedure `prime?`
    that tests whether numbers are prime. Even with `prime?` defined,
    the `prime/sum/pair` procedure may look suspiciously like the
    unhelpful "pseudo-Lisp" attempt to define the square-root function,
    which we described at the beginning of [Section
    1.1.7](#Section 1.1.7). In fact, a square-root procedure along those
    lines can actually be formulated as a nondeterministic program. By
    incorporating a search mechanism into the evaluator, we are eroding
    the distinction between purely declarative descriptions and
    imperative specifications of how to compute answers. We'll go even
    farther in this direction in [Section 4.4](#Section 4.4).

[^247]: The idea of `amb` for nondeterministic programming was first
    described in 1961 by John McCarthy (see [McCarthy
    1963](#McCarthy 1963)).

[^248]: In actuality, the distinction between nondeterministically
    returning a single choice and returning all choices depends somewhat
    on our point of view. From the perspective of the code that uses the
    value, the nondeterministic choice returns a single value. From the
    perspective of the programmer designing the code, the
    nondeterministic choice potentially returns all possible values, and
    the computation branches so that each value is investigated
    separately.

[^249]: One might object that this is a hopelessly inefficient
    mechanism. It might require millions of processors to solve some
    easily stated problem this way, and most of the time most of those
    processors would be idle. This objection should be taken in the
    context of history. Memory used to be considered just such an
    expensive commodity. In 1964 a megabyte of ram cost
    about \$400,000. Now every personal computer has many megabytes of
    ram, and most of the time most of that
    ram is unused. It is hard to underestimate the cost of
    mass-produced electronics.

[^250]: Automagically: "Automatically, but in a way which, for some
    reason (typically because it is too complicated, or too ugly, or
    perhaps even too trivial), the speaker doesn't feel like
    explaining." ([Steele et al. 1983](#Steele et al. 1983), [Raymond
    1993](#Raymond 1993))[]{#Footnote 4.47 label="Footnote 4.47"}

[^251]: The integration of automatic search strategies into programming
    languages has had a long and checkered history. The first
    suggestions that nondeterministic algorithms might be elegantly
    encoded in a programming language with search and automatic
    backtracking came from Robert [Floyd (1967)](#Floyd (1967)). Carl
    [Hewitt (1969)](#Hewitt (1969)) invented a programming language
    called Planner that explicitly supported automatic chronological
    backtracking, providing for a built-in depth-first search strategy.
    [Sussman et al. (1971)](#Sussman et al. (1971)) implemented a subset
    of this language, called MicroPlanner, which was used to support
    work in problem solving and robot planning. Similar ideas, arising
    from logic and theorem proving, led to the genesis in Edinburgh and
    Marseille of the elegant language Prolog (which we will discuss in
    [Section 4.4](#Section 4.4)). After sufficient frustration with
    automatic search, [McDermott and Sussman
    (1972)](#McDermott and Sussman (1972)) developed a language called
    Conniver, which included mechanisms for placing the search strategy
    under programmer control. This proved unwieldy, however, and
    [Sussman and Stallman 1975](#Sussman and Stallman 1975) found a more
    tractable approach while investigating methods of symbolic analysis
    for electrical circuits. They developed a non-chronological
    backtracking scheme that was based on tracing out the logical
    dependencies connecting facts, a technique that has come to be known
    as *dependency-directed backtracking*. Although their method was
    complex, it produced reasonably efficient programs because it did
    little redundant search. [Doyle (1979)](#Doyle (1979)) and
    [McAllester (1978; 1980)](#McAllester (1978; 1980)) generalized and
    clarified the methods of Stallman and Sussman, developing a new
    paradigm for formulating search that is now called *truth
    maintenance*. Modern problem-solving systems all use some form of
    truth-maintenance system as a substrate. See [Forbus and deKleer
    1993](#Forbus and deKleer 1993) for a discussion of elegant ways to
    build truth-maintenance systems and applications using truth
    maintenance. [Zabih et al. 1987](#Zabih et al. 1987) describes a
    nondeterministic extension to Scheme that is based on `amb`; it is
    similar to the interpreter described in this section, but more
    sophisticated, because it uses dependency-directed backtracking
    rather than chronological backtracking. [Winston
    1992](#Winston 1992) gives an introduction to both kinds of
    backtracking.

[^252]: Our program uses the following procedure to determine if the
    elements of a list are distinct:

    ::: smallscheme
    (define (distinct? items) (cond ((null? items) true) ((null? (cdr
    items)) true) ((member (car items) (cdr items)) false) (else
    (distinct? (cdr items)))))
    :::

    `member` is like `memq` except that it uses `equal?` instead of
    `eq?` to test for equality.

[^253]: This is taken from a booklet called "Problematical Recreations,"
    published in the 1960s by Litton Industries, where it is attributed
    to the *Kansas State Engineer*.

[^254]: Here we use the convention that the first element of each list
    designates the part of speech for the rest of the words in the list.

[^255]: Notice that `parse/word` uses `set!` to modify the unparsed
    input list. For this to work, our `amb` evaluator must undo the
    effects of `set!` operations when it backtracks.

[^256]: Observe that this definition is recursive---a verb may be
    followed by any number of prepositional phrases.

[^257]: This kind of grammar can become arbitrarily complex, but it is
    only a toy as far as real language understanding is concerned. Real
    natural-language understanding by computer requires an elaborate
    mixture of syntactic analysis and interpretation of meaning. On the
    other hand, even toy parsers can be useful in supporting flexible
    command languages for programs such as information-retrieval
    systems. [Winston 1992](#Winston 1992) discusses computational
    approaches to real language understanding and also the applications
    of simple grammars to command languages.

[^258]: Although Alyssa's idea works just fine (and is surprisingly
    simple), the sentences that it generates are a bit boring---they
    don't sample the possible sentences of this language in a very
    interesting way. In fact, the grammar is highly recursive in many
    places, and Alyssa's technique "falls into" one of these recursions
    and gets stuck. See [Exercise 4.50](#Exercise 4.50) for a way to
    deal with this.

[^259]: We chose to implement the lazy evaluator in [Section
    4.2](#Section 4.2) as a modification of the ordinary metacircular
    evaluator of [Section 4.1.1](#Section 4.1.1). In contrast, we will
    base the `amb` evaluator on the analyzing evaluator of [Section
    4.1.7](#Section 4.1.7), because the execution procedures in that
    evaluator provide a convenient framework for implementing
    backtracking.

[^260]: We assume that the evaluator supports `let` (see [Exercise
    4.22](#Exercise 4.22)), which we have used in our nondeterministic
    programs.

[^261]: We didn't worry about undoing definitions, since we can assume
    that internal definitions are scanned out ([Section
    4.1.6](#Section 4.1.6)).

[^262]: Logic programming has grown out of a long history of research in
    automatic theorem proving. Early theorem-proving programs could
    accomplish very little, because they exhaustively searched the space
    of possible proofs. The major breakthrough that made such a search
    plausible was the discovery in the early 1960s of the *unification
    algorithm* and the *resolution principle* ([Robinson
    1965](#Robinson 1965)). Resolution was used, for example, by [Green
    and Raphael (1968)](#Green and Raphael (1968)) (see also [Green
    1969](#Green 1969)) as the basis for a deductive question-answering
    system. During most of this period, researchers concentrated on
    algorithms that are guaranteed to find a proof if one exists. Such
    algorithms were difficult to control and to direct toward a proof.
    [Hewitt (1969)](#Hewitt (1969)) recognized the possibility of
    merging the control structure of a programming language with the
    operations of a logic-manipulation system, leading to the work in
    automatic search mentioned in [Section 4.3.1](#Section 4.3.1)
    ([Footnote 4.47](#Footnote 4.47)). At the same time that this was
    being done, Colmerauer, in Marseille, was developing rule-based
    systems for manipulating natural language (see [Colmerauer et al.
    1973](#Colmerauer et al. 1973)). He invented a programming language
    called Prolog for representing those rules. [Kowalski (1973;
    1979)](#Kowalski (1973; 1979)), in Edinburgh, recognized that
    execution of a Prolog program could be interpreted as proving
    theorems (using a proof technique called linear Horn-clause
    resolution). The merging of the last two strands led to the
    logic-programming movement. Thus, in assigning credit for the
    development of logic programming, the French can point to Prolog's
    genesis at the University of Marseille, while the British can
    highlight the work at the University of Edinburgh. According to
    people at mit, logic programming was developed by
    these groups in an attempt to figure out what Hewitt was talking
    about in his brilliant but impenetrable Ph.D. thesis. For a history
    of logic programming, see [Robinson 1983](#Robinson 1983).

[^263]: To see the correspondence between the rules and the procedure,
    let `x` in the procedure (where `x` is nonempty) correspond to
    `(cons u v)` in the rule. Then `z` in the rule corresponds to the
    `append` of `(cdr x)` and `y`.

[^264]: This certainly does not relieve the user of the entire problem
    of how to compute the answer. There are many different
    mathematically equivalent sets of rules for formulating the `append`
    relation, only some of which can be turned into effective devices
    for computing in any direction. In addition, sometimes "what is"
    information gives no clue "how to" compute an answer. For example,
    consider the problem of computing the $y$ such that $y^2 = x$.

[^265]: Interest in logic programming peaked during the early 80s when
    the Japanese government began an ambitious project aimed at building
    superfast computers optimized to run logic programming languages.
    The speed of such computers was to be measured in LIPS (Logical
    Inferences Per Second) rather than the usual FLOPS (FLoating-point
    Operations Per Second). Although the project succeeded in developing
    hardware and software as originally planned, the international
    computer industry moved in a different direction. See [Feigenbaum
    and Shrobe 1993](#Feigenbaum and Shrobe 1993) for an overview
    evaluation of the Japanese project. The logic programming community
    has also moved on to consider relational programming based on
    techniques other than simple pattern matching, such as the ability
    to deal with numerical constraints such as the ones illustrated in
    the constraint-propagation system of [Section
    3.3.5](#Section 3.3.5).

[^266]: This uses the dotted-tail notation introduced in [Exercise
    2.20](#Exercise 2.20).

[^267]: Actually, this description of `not` is valid only for simple
    cases. The real behavior of `not` is more complex. We will examine
    `not`'s peculiarities in sections [Section 4.4.2](#Section 4.4.2)
    and [Section 4.4.3](#Section 4.4.3).

[^268]: `lisp/value` should be used only to perform an operation not
    provided in the query language. In particular, it should not be used
    to test equality (since that is what the matching in the query
    language is designed to do) or inequality (since that can be done
    with the `same` rule shown below).

[^269]: Notice that we do not need `same` in order to make two things be
    the same: We just use the same pattern variable for each---in
    effect, we have one thing instead of two things in the first place.
    For example, see `?town` in the `lives/near` rule and
    `?middle/manager` in the `wheel` rule below. `same` is useful when
    we want to force two things to be different, such as `?person/1` and
    `?person/2` in the `lives/near` rule. Although using the same
    pattern variable in two parts of a query forces the same value to
    appear in both places, using different pattern variables does not
    force different values to appear. (The values assigned to different
    pattern variables may be the same or different.)

[^270]: We will also allow rules without bodies, as in `same`, and we
    will interpret such a rule to mean that the rule conclusion is
    satisfied by any values of the variables.

[^271]: Because matching is generally very expensive, we would like to
    avoid applying the full matcher to every element of the data base.
    This is usually arranged by breaking up the process into a fast,
    coarse match and the final match. The coarse match filters the data
    base to produce a small set of candidates for the final match. With
    care, we can arrange our data base so that some of the work of
    coarse matching can be done when the data base is constructed rather
    then when we want to select the candidates. This is called
    *indexing* the data base. There is a vast technology built around
    data-base-indexing schemes. Our implementation, described in
    [Section 4.4.4](#Section 4.4.4), contains a simple-minded form of
    such an optimization.

[^272]: But this kind of exponential explosion is not common in `and`
    queries because the added conditions tend to reduce rather than
    expand the number of frames produced.

[^273]: There is a large literature on data-base-management systems that
    is concerned with how to handle complex queries efficiently.

[^274]: There is a subtle difference between this filter implementation
    of `not` and the usual meaning of `not` in mathematical logic. See
    [Section 4.4.3](#Section 4.4.3).

[^275]: In one-sided pattern matching, all the equations that contain
    pattern variables are explicit and already solved for the unknown
    (the pattern variable).

[^276]: Another way to think of unification is that it generates the
    most general pattern that is a specialization of the two input
    patterns. That is, the unification of `(?x a)` and `((b ?y) ?z)` is
    `((b ?y) a)`, and the unification of `(?x a ?y)` and `(?y ?z a)`,
    discussed above, is `(a a a)`. For our implementation, it is more
    convenient to think of the result of unification as a frame rather
    than a pattern.

[^277]: Since unification is a generalization of matching, we could
    simplify the system by using the unifier to produce both streams.
    Treating the easy case with the simple matcher, however, illustrates
    how matching (as opposed to full-blown unification) can be useful in
    its own right.

[^278]: The reason we use streams (rather than lists) of frames is that
    the recursive application of rules can generate infinite numbers of
    values that satisfy a query. The delayed evaluation embodied in
    streams is crucial here: The system will print responses one by one
    as they are generated, regardless of whether there are a finite or
    infinite number of responses.

[^279]: That a particular method of inference is legitimate is not a
    trivial assertion. One must prove that if one starts with true
    premises, only true conclusions can be derived. The method of
    inference represented by rule applications is *modus ponens*, the
    familiar method of inference that says that if $A$ is true and *A
    implies B* is true, then we may conclude that $B$ is true.

[^280]: We must qualify this statement by agreeing that, in speaking of
    the "inference" accomplished by a logic program, we assume that the
    computation terminates. Unfortunately, even this qualified statement
    is false for our implementation of the query language (and also
    false for programs in Prolog and most other current logic
    programming languages) because of our use of `not` and `lisp/value`.
    As we will describe below, the `not` implemented in the query
    language is not always consistent with the `not` of mathematical
    logic, and `lisp/value` introduces additional complications. We
    could implement a language consistent with mathematical logic by
    simply removing `not` and `lisp/value` from the language and
    agreeing to write programs using only simple queries, `and`, and
    `or`. However, this would greatly restrict the expressive power of
    the language. One of the major concerns of research in logic
    programming is to find ways to achieve more consistency with
    mathematical logic without unduly sacrificing expressive power.

[^281]: This is not a problem of the logic but one of the procedural
    interpretation of the logic provided by our interpreter. We could
    write an interpreter that would not fall into a loop here. For
    example, we could enumerate all the proofs derivable from our
    assertions and our rules in a breadth-first rather than a
    depth-first order. However, such a system makes it more difficult to
    take advantage of the order of deductions in our programs. One
    attempt to build sophisticated control into such a program is
    described in [deKleer et al. 1977](#deKleer et al. 1977). Another
    technique, which does not lead to such serious control problems, is
    to put in special knowledge, such as detectors for particular kinds
    of loops ([Exercise 4.67](#Exercise 4.67)). However, there can be no
    general scheme for reliably preventing a system from going down
    infinite paths in performing deductions. Imagine a diabolical rule
    of the form "To show $P(x)$ is true, show that $P(f(x))$ is true,"
    for some suitably chosen function $f$.

[^282]: Consider the query `(not (baseball/fan (Bitdiddle Ben)))`. The
    system finds that `(baseball/fan (Bitdiddle Ben))` is not in the
    data base, so the empty frame does not satisfy the pattern and is
    not filtered out of the initial stream of frames. The result of the
    query is thus the empty frame, which is used to instantiate the
    input query to produce `(not (baseball/fan (Bitdiddle Ben)))`.

[^283]: A discussion and justification of this treatment of `not` can be
    found in the article by [Clark (1978)](#Clark (1978)).

[^284]: In general, unifying `?y` with an expression involving `?y`
    would require our being able to find a fixed point of the equation
    `?y` = $\langle$*expression involving `?y`*$\rangle$. It is
    sometimes possible to syntactically form an expression that appears
    to be the solution. For example, `?y` = `(f ?y)` seems to have the
    fixed point `(f (f (f `$\dots$` )))`, which we can produce by
    beginning with the expression `(f ?y)` and repeatedly substituting
    `(f ?y)` for `?y`. Unfortunately, not every such equation has a
    meaningful fixed point. The issues that arise here are similar to
    the issues of manipulating infinite series in mathematics. For
    example, we know that 2 is the solution to the equation
    $y = 1 + y / 2$. Beginning with the expression $1 + y / 2$ and
    repeatedly substituting $1 + y / 2$ for $y$ gives

    $$2 = y = 1 + {y \over 2} = 1 + {1\over2}\left(1 + {y \over 2}\right) =
        1 + {1\over2} + {y \over 4} = \dots ,$$

    which leads to

    $$2 = 1 + {1\over2} + {1\over4} + {1\over8} + \dots.$$

    However, if we try the same manipulation beginning with the
    observation that -1 is the solution to the equation $y = 1 + 2y$, we
    obtain

    $$-1 = y = 1 + 2y = 1 + 2(1 + 2y) = 1 + 2 + 4y = \dots,$$

    which leads to

    $$-1 = 1 + 2 + 4 + 8 + \dots.$$

    Although the formal manipulations used in deriving these two
    equations are identical, the first result is a valid assertion about
    infinite series but the second is not. Similarly, for our
    unification results, reasoning with an arbitrary syntactically
    constructed expression may lead to errors.

[^285]: Most Lisp systems give the user the ability to modify the
    ordinary `read` procedure to perform such transformations by
    defining *reader macro characters*. Quoted expressions are already
    handled in this way: The reader automatically translates
    `’expression` into `(quote expression)` before the evaluator sees
    it. We could arrange for `?expression` to be transformed into
    `(? expression)` in the same way; however, for the sake of clarity
    we have included the transformation procedure here explicitly.

    `expand/question/mark` and `contract/question/mark` use several
    procedures with `string` in their names. These are Scheme
    primitives.

[^286]: This assumption glosses over a great deal of complexity. Usually
    a large portion of the implementation of a Lisp system is dedicated
    to making reading and printing work.

[^287]: One might argue that we don't need to save the old `n`; after we
    decrement it and solve the subproblem, we could simply increment it
    to recover the old value. Although this strategy works for
    factorial, it cannot work in general, since the old value of a
    register cannot always be computed from the new one.

[^288]: In [Section 5.3](#Section 5.3) we will see how to implement a
    stack in terms of more primitive operations.

[^289]: Using the `receive` procedure here is a way to get
    `extract/labels` to effectively return two values---`labels` and
    `insts`---without explicitly making a compound data structure to
    hold them. An alternative implementation, which returns an explicit
    pair of values, is

    ::: smallscheme
    (define (extract-labels text) (if (null? text) (cons '() '()) (let
    ((result (extract-labels (cdr text)))) (let ((insts (car result))
    (labels (cdr result))) (let ((next-inst (car text))) (if (symbol?
    next-inst) (cons insts (cons (make-label-entry next-inst insts)
    labels)) (cons (cons (make-instruction next-inst) insts)
    labels)))))))
    :::

    which would be called by `assemble` as follows:

    ::: smallscheme
    (define (assemble controller-text machine) (let ((result
    (extract-labels controller-text))) (let ((insts (car result))
    (labels (cdr result))) (update-insts! insts labels machine) insts)))
    :::

    You can consider our use of `receive` as demonstrating an elegant
    way to return multiple values, or simply an excuse to show off a
    programming trick. An argument like `receive` that is the next
    procedure to be invoked is called a "continuation." Recall that we
    also used continuations to implement the backtracking control
    structure in the `amb` evaluator in [Section 4.3.3](#Section 4.3.3).

[^290]: We could represent memory as lists of items. However, the access
    time would then not be independent of the index, since accessing the
    $n^{\mathrm{th}}$ element of a list requires $n - 1$ `cdr`
    operations.

[^291]: For completeness, we should specify a `make/vector` operation
    that constructs vectors. However, in the present application we will
    use vectors only to model fixed divisions of the computer memory.

[^292]: This is precisely the same "tagged data" idea we introduced in
    [Chapter 2](#Chapter 2) for dealing with generic operations. Here,
    however, the data types are included at the primitive machine level
    rather than constructed through the use of lists.

[^293]: Type information may be encoded in a variety of ways, depending
    on the details of the machine on which the Lisp system is to be
    implemented. The execution efficiency of Lisp programs will be
    strongly dependent on how cleverly this choice is made, but it is
    difficult to formulate general design rules for good choices. The
    most straightforward way to implement typed pointers is to allocate
    a fixed set of bits in each pointer to be a *type field* that
    encodes the data type. Important questions to be addressed in
    designing such a representation include the following: How many type
    bits are required? How large must the vector indices be? How
    efficiently can the primitive machine instructions be used to
    manipulate the type fields of pointers? Machines that include
    special hardware for the efficient handling of type fields are said
    to have *tagged architectures*.

[^294]: This decision on the representation of numbers determines
    whether `eq?`, which tests equality of pointers, can be used to test
    for equality of numbers. If the pointer contains the number itself,
    then equal numbers will have the same pointer. But if the pointer
    contains the index of a location where the number is stored, equal
    numbers will be guaranteed to have equal pointers only if we are
    careful never to store the same number in more than one location.

[^295]: This is just like writing a number as a sequence of digits,
    except that each "digit" is a number between 0 and the largest
    number that can be stored in a single pointer.

[^296]: There are other ways of finding free storage. For example, we
    could link together all the unused pairs into a *free list*. Our
    free locations are consecutive (and hence can be accessed by
    incrementing a pointer) because we are using a compacting garbage
    collector, as we will see in [Section 5.3.2](#Section 5.3.2).

[^297]: This is essentially the implementation of `cons` in terms of
    `set/car!` and `set/cdr!`, as described in [Section
    3.3.1](#Section 3.3.1). The operation `get/new/pair` used in that
    implementation is realized here by the `free` pointer.

[^298]: This may not be true eventually, because memories may get large
    enough so that it would be impossible to run out of free memory in
    the lifetime of the computer. For example, there are about
    $3\cdot10^{13}$ microseconds in a year, so if we were to `cons` once
    per microsecond we would need about $10^{15}$ cells of memory to
    build a machine that could operate for 30 years without running out
    of memory. That much memory seems absurdly large by today's
    standards, but it is not physically impossible. On the other hand,
    processors are getting faster and a future computer may have large
    numbers of processors operating in parallel on a single memory, so
    it may be possible to use up memory much faster than we have
    postulated.

[^299]: We assume here that the stack is represented as a list as
    described in [Section 5.3.1](#Section 5.3.1), so that items on the
    stack are accessible via the pointer in the stack register.

[^300]: This idea was invented and first implemented by Minsky, as part
    of the implementation of Lisp for the pdp-1 at the
    mit Research Laboratory of Electronics. It was further
    developed by [Fenichel and Yochelson
    (1969)](#Fenichel and Yochelson (1969)) for use in the Lisp
    implementation for the Multics time-sharing system. Later, [Baker
    (1978)](#Baker (1978)) developed a "real-time" version of the
    method, which does not require the computation to stop during
    garbage collection. Baker's idea was extended by Hewitt, Lieberman,
    and Moon (see [Lieberman and Hewitt
    1983](#Lieberman and Hewitt 1983)) to take advantage of the fact
    that some structure is more volatile and other structure is more
    permanent.

    An alternative commonly used garbage-collection technique is the
    *mark-sweep* method. This consists of tracing all the structure
    accessible from the machine registers and marking each pair we
    reach. We then scan all of memory, and any location that is unmarked
    is "swept up" as garbage and made available for reuse. A full
    discussion of the mark-sweep method can be found in [Allen
    1978](#Allen 1978).

    The Minsky-Fenichel-Yochelson algorithm is the dominant algorithm in
    use for large-memory systems because it examines only the useful
    part of memory. This is in contrast to mark-sweep, in which the
    sweep phase must check all of memory. A second advantage of
    stop-and-copy is that it is a *compacting* garbage collector. That
    is, at the end of the garbage-collection phase the useful data will
    have been moved to consecutive memory locations, with all garbage
    pairs compressed out. This can be an extremely important performance
    consideration in machines with virtual memory, in which accesses to
    widely separated memory addresses may require extra paging
    operations.

[^301]: This list of registers does not include the registers used by
    the storage-allocation system---`root`, `the/cars`, `the/cdrs`, and
    the other registers that will be introduced in this section.

[^302]: The term *broken heart* was coined by David Cressey, who wrote a
    garbage collector for MDL, a dialect of Lisp developed at
    mit during the early 1970s.

[^303]: The garbage collector uses the low-level predicate
    `pointer/to/pair?` instead of the list-structure `pair?` operation
    because in a real system there might be various things that are
    treated as pairs for garbage-collection purposes. For example, in a
    Scheme system that conforms to the ieee standard a
    procedure object may be implemented as a special kind of "pair" that
    doesn't satisfy the `pair?` predicate. For simulation purposes,
    `pointer/to/pair?` can be implemented as `pair?`.

[^304]: See [Batali et al. 1982](#Batali et al. 1982) for more
    information on the chip and the method by which it was designed.

[^305]: In our controller, the dispatch is written as a sequence of
    `test` and `branch` instructions. Alternatively, it could have been
    written in a data-directed style (and in a real system it probably
    would have been) to avoid the need to perform sequential tests and
    to facilitate the definition of new expression types. A machine
    designed to run Lisp would probably include a `dispatch/on/type`
    instruction that would efficiently execute such data-directed
    dispatches.

[^306]: This is an important but subtle point in translating algorithms
    from a procedural language, such as Lisp, to a register-machine
    language. As an alternative to saving only what is needed, we could
    save all the registers (except `val`) before each recursive call.
    This is called a *framed-stack* discipline. This would work but
    might save more registers than necessary; this could be an important
    consideration in a system where stack operations are expensive.
    Saving registers whose contents will not be needed later may also
    hold onto useless data that could otherwise be garbage-collected,
    freeing space to be reused.

[^307]: We add to the evaluator data-structure procedures in [Section
    4.1.3](#Section 4.1.3) the following two procedures for manipulating
    argument lists:

    ::: smallscheme
    (define (empty-arglist) '()) (define (adjoin-arg arg arglist)
    (append arglist (list arg)))
    :::

    We also use an additional syntax procedure to test for the last
    operand in a combination:

    ::: smallscheme
    (define (last-operand? ops) (null? (cdr ops)))
    :::

[^308]: The optimization of treating the last operand specially is known
    as *evlis tail recursion* (see [Wand 1980](#Wand 1980)). We could be
    somewhat more efficient in the argument evaluation loop if we made
    evaluation of the first operand a special case too. This would
    permit us to postpone initializing `argl` until after evaluating the
    first operand, so as to avoid saving `argl` in this case. The
    compiler in [Section 5.5](#Section 5.5) performs this optimization.
    (Compare the `construct/arglist` procedure of [Section
    5.5.3](#Section 5.5.3).)

[^309]: The order of operand evaluation in the metacircular evaluator is
    determined by the order of evaluation of the arguments to `cons` in
    the procedure `list/of/values` of [Section 4.1.1](#Section 4.1.1)
    (see [Exercise 4.1](#Exercise 4.1)).

[^310]: We saw in [Section 5.1](#Section 5.1) how to implement such a
    process with a register machine that had no stack; the state of the
    process was stored in a fixed set of registers.

[^311]: This implementation of tail recursion in `ev/sequence` is one
    variety of a well-known optimization technique used by many
    compilers. In compiling a procedure that ends with a procedure call,
    one can replace the call by a jump to the called procedure's entry
    point. Building this strategy into the interpreter, as we have done
    in this section, provides the optimization uniformly throughout the
    language.

[^312]: We can define `no/more/exps?` as follows:

    ::: smallscheme
    (define (no-more-exps? seq) (null? seq))
    :::

[^313]: This isn't really cheating. In an actual implementation built
    from scratch, we would use our explicit-control evaluator to
    interpret a Scheme program that performs source-level
    transformations like `cond/>if` in a syntax phase that runs before
    execution.

[^314]: We assume here that `read` and the various printing operations
    are available as primitive machine operations, which is useful for
    our simulation, but completely unrealistic in practice. These are
    actually extremely complex operations. In practice, they would be
    implemented using low-level input-output operations such as
    transferring single characters to and from a device.

    To support the `get/global/environment` operation we define

    ::: smallscheme
    (define the-global-environment (setup-environment)) (define
    (get-global-environment) the-global-environment)
    :::

[^315]: There are other errors that we would like the interpreter to
    handle, but these are not so simple. See [Exercise
    5.30](#Exercise 5.30).

[^316]: We could perform the stack initialization only after errors, but
    doing it in the driver loop will be convenient for monitoring the
    evaluator's performance, as described below.

[^317]: Regrettably, this is the normal state of affairs in conventional
    compiler-based language systems such as C. In unix(tm)
    the system "dumps core," and in dos/Windows(tm) it
    becomes catatonic. The Macintosh(tm) displays a picture of an
    exploding bomb and offers you the opportunity to reboot the
    computer---if you're lucky.

[^318]: This is a theoretical statement. We are not claiming that the
    evaluator's data paths are a particularly convenient or efficient
    set of data paths for a general-purpose computer. For example, they
    are not very good for implementing high-performance floating-point
    calculations or calculations that intensively manipulate bit
    vectors.

[^319]: Actually, the machine that runs compiled code can be simpler
    than the interpreter machine, because we won't use the `exp` and
    `unev` registers. The interpreter used these to hold pieces of
    unevaluated expressions. With the compiler, however, these
    expressions get built into the compiled code that the register
    machine will run. For the same reason, we don't need the machine
    operations that deal with expression syntax. But compiled code will
    use a few additional machine operations (to represent compiled
    procedure objects) that didn't appear in the explicit-control
    evaluator machine.

[^320]: Notice, however, that our compiler is a Scheme program, and the
    syntax procedures that it uses to manipulate expressions are the
    actual Scheme procedures used with the metacircular evaluator. For
    the explicit-control evaluator, in contrast, we assumed that
    equivalent syntax operations were available as operations for the
    register machine. (Of course, when we simulated the register machine
    in Scheme, we used the actual Scheme procedures in our register
    machine simulation.)

[^321]: This procedure uses a feature of Lisp called *backquote* (or
    *quasiquote*) that is handy for constructing lists. Preceding a list
    with a backquote symbol is much like quoting it, except that
    anything in the list that is flagged with a comma is evaluated.

    For example, if the value of `linkage` is the symbol `branch25`,
    then the expression

    ::: smallscheme
    '((goto (label ,linkage)))
    :::

    evaluates to the list

    ::: smallscheme
    ((goto (label branch25)))
    :::

    Similarly, if the value of `x` is the list `(a b c)`, then

    ::: smallscheme
    '(1 2 ,(car x))
    :::

    evaluates to the list

    ::: smallscheme
    (1 2 a).
    :::

[^322]: We can't just use the labels `true/branch`, `false/branch`, and
    `after/if` as shown above, because there might be more than one `if`
    in the program. The compiler uses the procedure `make/label` to
    generate labels. `make/label` takes a symbol as argument and returns
    a new symbol that begins with the given symbol. For example,
    successive calls to `(make/label ’a)` would return `a1`, `a2`, and
    so on. `make/label` can be implemented similarly to the generation
    of unique variable names in the query language, as follows:

    ::: smallscheme
    (define label-counter 0) (define (new-label-number) (set!
    label-counter (+ 1 label-counter)) label-counter) (define
    (make-label name) (string-\>symbol (string-append (symbol-\>string
    name) (number-\>string (new-label-number)))))
    :::

[^323]: []{#Footnote 38 label="Footnote 38"} We need machine operations
    to implement a data structure for representing compiled procedures,
    analogous to the structure for compound procedures described in
    [Section 4.1.3](#Section 4.1.3):

    ::: smallscheme
    (define (make-compiled-procedure entry env) (list
    'compiled-procedure entry env)) (define (compiled-procedure? proc)
    (tagged-list? proc 'compiled-procedure)) (define
    (compiled-procedure-entry c-proc) (cadr c-proc)) (define
    (compiled-procedure-env c-proc) (caddr c-proc))
    :::

[^324]: Actually, we signal an error when the target is not `val` and
    the linkage is `return`, since the only place we request `return`
    linkages is in compiling procedures, and our convention is that
    procedures return their values in `val`.

[^325]: Making a compiler generate tail-recursive code might seem like a
    straightforward idea. But most compilers for common languages,
    including C and Pascal, do not do this, and therefore these
    languages cannot represent iterative processes in terms of procedure
    call alone. The difficulty with tail recursion in these languages is
    that their implementations use the stack to store procedure
    arguments and local variables as well as return addresses. The
    Scheme implementations described in this book store arguments and
    variables in memory to be garbage-collected. The reason for using
    the stack for variables and arguments is that it avoids the need for
    garbage collection in languages that would not otherwise require it,
    and is generally believed to be more efficient. Sophisticated Lisp
    compilers can, in fact, use the stack for arguments without
    destroying tail recursion. (See [Hanson 1990](#Hanson 1990) for a
    description.) There is also some debate about whether stack
    allocation is actually more efficient than garbage collection in the
    first place, but the details seem to hinge on fine points of
    computer architecture. (See [Appel 1987](#Appel 1987) and [Miller
    and Rozas 1994](#Miller and Rozas 1994) for opposing views on this
    issue.)

[^326]: The variable `all/regs` is bound to the list of names of all the
    registers:

    ::: smallscheme
    (define all-regs '(env proc val argl continue))
    :::

[^327]: Note that `preserving` calls `append` with three arguments.
    Though the definition of `append` shown in this book accepts only
    two arguments, Scheme standardly provides an `append` procedure that
    takes an arbitrary number of arguments.

[^328]: We have used the same symbol `+` here to denote both the
    source-language procedure and the machine operation. In general
    there will not be a one-to-one correspondence between primitives of
    the source language and primitives of the machine.

[^329]: Making the primitives into reserved words is in general a bad
    idea, since a user cannot then rebind these names to different
    procedures. Moreover, if we add reserved words to a compiler that is
    in use, existing programs that define procedures with these names
    will stop working. See [Exercise 5.44](#Exercise 5.44) for ideas on
    how to avoid this problem.

[^330]: This is not true if we allow internal definitions, unless we
    scan them out. See [Exercise 5.43](#Exercise 5.43).

[^331]: This is the modification to variable lookup required if we
    implement the scanning method to eliminate internal definitions
    ([Exercise 5.43](#Exercise 5.43)). We will need to eliminate these
    definitions in order for lexical addressing to work.

[^332]: Lexical addresses cannot be used to access variables in the
    global environment, because these names can be defined and redefined
    interactively at any time. With internal definitions scanned out, as
    in [Exercise 5.43](#Exercise 5.43), the only definitions the
    compiler sees are those at top level, which act on the global
    environment. Compilation of a definition does not cause the defined
    name to be entered in the compile-time environment.

[^333]: Of course, compiled procedures as well as interpreted procedures
    are compound (nonprimitive). For compatibility with the terminology
    used in the explicit-control evaluator, in this section we will use
    "compound" to mean interpreted (as opposed to compiled).

[^334]: Now that the evaluator machine starts with a `branch`, we must
    always initialize the `flag` register before starting the evaluator
    machine. To start the machine at its ordinary read-eval-print loop,
    we could use

    ::: smallscheme
    (define (start-eceval) (set! the-global-environment
    (setup-environment)) (set-register-contents! eceval 'flag false)
    (start eceval))
    :::

[^335]: Since a compiled procedure is an object that the system may try
    to print, we also modify the system print operation `user/print`
    (from [Section 4.1.4](#Section 4.1.4)) so that it will not attempt
    to print the components of a compiled procedure:

    ::: smallscheme
    (define (user-print object) (cond ((compound-procedure? object)
    (display (list 'compound-procedure (procedure-parameters object)
    (procedure-body object) '\<procedure-env\>))) ((compiled-procedure?
    object) (display '\<compiled-procedure\>)) (else (display object))))
    :::

[^336]: We can do even better by extending the compiler to allow
    compiled code to call interpreted procedures. See [Exercise
    5.47](#Exercise 5.47).

[^337]: Independent of the strategy of execution, we incur significant
    overhead if we insist that errors encountered in execution of a user
    program be detected and signaled, rather than being allowed to kill
    the system or produce wrong answers. For example, an out-of-bounds
    array reference can be detected by checking the validity of the
    reference before performing it. The overhead of checking, however,
    can be many times the cost of the array reference itself, and a
    programmer should weigh speed against safety in determining whether
    such a check is desirable. A good compiler should be able to produce
    code with such checks, should avoid redundant checks, and should
    allow programmers to control the extent and type of error checking
    in the compiled code.

    Compilers for popular languages, such as C and C++, put hardly any
    error-checking operations into running code, so as to make things
    run as fast as possible. As a result, it falls to programmers to
    explicitly provide error checking. Unfortunately, people often
    neglect to do this, even in critical applications where speed is not
    a constraint. Their programs lead fast and dangerous lives. For
    example, the notorious "Worm" that paralyzed the Internet in 1988
    exploited the unix(tm) operating system's failure to
    check whether the input buffer has overflowed in the finger daemon.
    (See [Spafford 1989](#Spafford 1989).)

[^338]: Of course, with either the interpretation or the compilation
    strategy we must also implement for the new machine storage
    allocation, input and output, and all the various operations that we
    took as "primitive" in our discussion of the evaluator and compiler.
    One strategy for minimizing work here is to write as many of these
    operations as possible in Lisp and then compile them for the new
    machine. Ultimately, everything reduces to a small kernel (such as
    garbage collection and the mechanism for applying actual machine
    primitives) that is hand-coded for the new machine.

[^339]: This strategy leads to amusing tests of correctness of the
    compiler, such as checking whether the compilation of a program on
    the new machine, using the compiled compiler, is identical with the
    compilation of the program on the original Lisp system. Tracking
    down the source of differences is fun but often frustrating, because
    the results are extremely sensitive to minuscule details.