# Lambda calculus

Lambda calculus (also written as λ-calculus) is a formal system in mathematical logic for expressing computation based on function abstraction and application using variable binding and substitution. Untyped lambda calculus, the topic of this article, is a universal machine, a model of computation that can be used to simulate any Turing machine (and vice versa). It was introduced by the mathematician Alonzo Church in the 1930s as part of his research into the foundations of mathematics. In 1936, Church found a formulation which was logically consistent, and documented it in 1940.
Lambda calculus consists of constructing lambda terms and performing reduction operations on them. A term is defined as any valid lambda calculus expression. In the simplest form of lambda calculus, terms are built using only the following rules:
* x{\textstyle x}: A variable is a character or string representing a parameter.
* (λx.M){\textstyle (\lambda x.M)}: A lambda abstraction is a function definition, taking as input the bound variable $x$ (between the λ and the punctum/dot .) and returning the body M{\textstyle M}.
* (M N){\textstyle (M\ N)}: An application, applying a function M{\textstyle M} to an argument N{\textstyle N}. Both M{\textstyle M} and N{\textstyle N} are lambda terms.
The reduction operations include:
* (λx.M[x])→(λy.M[y]){\textstyle (\lambda x.M[x])\rightarrow (\lambda y.M[y])} : α-conversion, renaming the bound variables in the expression. Used to avoid name collisions.
* ((λx.M) N)→(M[x:=N]){\textstyle ((\lambda x.M)\ N)\rightarrow (M[x:=N])} : β-reduction, replacing the bound variables with the argument expression in the body of the abstraction.
If De Bruijn indexing is used, then α-conversion is no longer required as there will be no name collisions. If repeated application of the reduction steps eventually terminates, then by the Church–Rosser theorem it will produce a β-normal form.
Variable names are not needed if using a universal lambda function, such as Iota and Jot, which can create any function behavior by calling it on itself in various combinations.

## Explanation and applications

Lambda calculus is Turing complete, that is, it is a universal model of computation that can be used to simulate any Turing machine. Its namesake, the Greek letter lambda (λ), is used in lambda expressions and lambda terms to denote binding a variable in a function.
Lambda calculus may be untyped or typed. In typed lambda calculus, functions can be applied only if they are capable of accepting the given input's "type" of data. Typed lambda calculi are strictly weaker than the untyped lambda calculus, which is the primary subject of this article, in the sense that typed lambda calculi can express less than the untyped calculus can. On the other hand, typed lambda calculi allow more things to be proven. For example, in simply typed lambda calculus, it is a theorem that every evaluation strategy terminates for every simply typed lambda-term, whereas evaluation of untyped lambda-terms need not terminate (see below). One reason there are many different typed lambda calculi has been the desire to do more (of what the untyped calculus can do) without giving up on being able to prove strong theorems about the calculus.
Lambda calculus has applications in many different areas in mathematics, philosophy, linguistics, and computer science. Lambda calculus has played an important role in the development of the theory of programming languages. Functional programming languages implement lambda calculus. Lambda calculus is also a current research topic in category theory.

## History

Lambda calculus was introduced by mathematician Alonzo Church in the 1930s as part of an investigation into the foundations of mathematics. The original system was shown to be logically inconsistent in 1935 when Stephen Kleene and J. B. Rosser developed the Kleene–Rosser paradox.
Subsequently, in 1936 Church isolated and published just the portion relevant to computation, what is now called the untyped lambda calculus. In 1940, he also introduced a computationally weaker, but logically consistent system, known as the simply typed lambda calculus.
Until the 1960s when its relation to programming languages was clarified, the lambda calculus was only a formalism. Thanks to Richard Montague and other linguists' applications in the semantics of natural language, the lambda calculus has begun to enjoy a respectable place in both linguistics and computer science.

### Origin of the λ symbol

There is some uncertainty over the reason for Church's use of the Greek letter lambda (λ) as the notation for function-abstraction in the lambda calculus, perhaps in part due to conflicting explanations by Church himself. According to Cardone and Hindley (2006):
   By the way, why did Church choose the notation "λ"? In [an unpublished 1964 letter to Harald Dickson] he stated clearly that it came from the notation "${\hat {x}}$" used for class-abstraction by Whitehead and Russell, by first modifying "${\hat {x}}$" to "$\land x$" to distinguish function-abstraction from class-abstraction, and then changing "$\land$" to "λ" for ease of printing.
   This origin was also reported in [Rosser, 1984, p.338]. On the other hand, in his later years Church told two enquirers that the choice was more accidental: a symbol was needed and λ just happened to be chosen.
Dana Scott has also addressed this question in various public lectures. Scott recounts that he once posed a question about the origin of the lambda symbol to Church's former student and son-in-law John W. Addison Jr., who then wrote his father-in-law a postcard:
   Dear Professor Church,
   Russell had the iota operator, Hilbert had the epsilon operator. Why did you choose lambda for your operator?
According to Scott, Church's entire response consisted of returning the postcard with the following annotation: "eeny, meeny, miny, moe".

## Informal description
### Motivation

Computable functions are a fundamental concept within computer science and mathematics. The lambda calculus provides simple semantics for computation which are useful for formally studying properties of computation. The lambda calculus incorporates two simplifications that make its semantics simple. The first simplification is that the lambda calculus treats functions "anonymously"; it does not give them explicit names. For example, the function
    $\operatorname {square\_sum} (x,y)=x^{2}+y^{2}$
can be rewritten in anonymous form as
    $(x,y)\mapsto x^{2}+y^{2}$
(which is read as "a tuple of x and y is mapped to x$^2$+y$^2${\textstyle x^{2}+y^{2}}"). Similarly, the function
    $\operatorname {id} (x)=x$
can be rewritten in anonymous form as
    $x\mapsto x$
where the input is simply mapped to itself.
The second simplification is that the lambda calculus only uses functions of a single input. An ordinary function that requires two inputs, for instance the square_sum{\textstyle \operatorname {square\_sum} } function, can be reworked into an equivalent function that accepts a single input, and as output returns another function, that in turn accepts a single input. For example,
    $(x,y)\mapsto x^{2}+y^{2}$
can be reworked into
    $x\mapsto (y\mapsto x^{2}+y^{2})$
This method, known as currying, transforms a function that takes multiple arguments into a chain of functions each with a single argument.
Function application of the square_sum{\textstyle \operatorname {square\_sum} } function to the arguments (5, 2), yields at once
    ((x, y)↦x$^2$+y$^2$)(5, 2){\textstyle ((x, y)\mapsto x^{2}+y^{2})(5, 2)}
    =5$^2$+2$^2${\textstyle =5^{2}+2^{2}}
    =29{\textstyle =29},
whereas evaluation of the curried version requires one more step
    ((x↦(y↦x$^2$+y$^2$))(5))(2){\textstyle {\Bigl{\biglx\mapsto (y\mapsto x^{2}+y^{2}){\bigr )}(5){\Bigr )}(2)}
    =(y↦5$^2$+y$^2$)(2){\textstyle =(y\mapsto 5^{2}+y^{2})(2)} // the definition of $x$ has been used with $5$ in the inner expression. This is like β-reduction.
    =5$^2$+2$^2${\textstyle =5^{2}+2^{2}} // the definition of $y$ has been used with $2$. Again, similar to β-reduction.
    =29{\textstyle =29}
to arrive at the same result.

### The lambda calculus

The lambda calculus consists of a language of lambda terms, that are defined by a certain formal syntax, and a set of transformation rules for manipulating the lambda terms. These transformation rules can be viewed as an equational theory or as an operational definition.
As described above, having no names, all functions in the lambda calculus are anonymous functions. They only accept one input variable, so currying is used to implement functions of several variables.

#### Lambda terms

The syntax of the lambda calculus defines some expressions as valid lambda calculus expressions and some as invalid, just as some strings of characters are valid computer programs and some are not. A valid lambda calculus expression is called a "lambda term".
The following three rules give an inductive definition that can be applied to build all syntactically valid lambda terms:
* variable x is itself a valid lambda term.
* if t is a lambda term, and x is a variable, then $(\lambda x.t)$ is a lambda term (called an abstraction);
* if t and s are lambda terms, then $(ts)$ is a lambda term (called an application).
Nothing else is a lambda term. That is, a lambda term is valid if and only if it can be obtained by repeated application of these three rules. For convenience, some parentheses can be omitted when writing a lambda term. For example, the outermost parentheses are usually not written. See § Notation, below, for an explicit description of which parentheses are optional. It is also common to extend the syntax presented here with additional operations, which allows making sense of terms such as $\lambda x.x^{2}.$ The focus of this article is the pure lambda calculus without extensions, but lambda terms extended with arithmetic operations are used for explanatory purposes.
An abstraction $\lambda x.t$ denotes an § anonymous function that takes a single input x and returns t. For example, $\lambda x.(x^{2}+2)$ is an abstraction representing the function $f$ defined by $f(x)=x^{2}+2,$ using the term $x^{2}+2$ for t. The name $f$ is superfluous when using abstraction. The syntax Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "http://localhost:6011/en.wikipedia.org/v1/":): {\displaystyle (\lambda x.t)} binds the variable x in the term t. The definition of a function with an abstraction merely "sets up" the function but does not invoke it.
An application $ts$ represents the application of a function t to an input s, that is, it represents the act of calling function t on input s to produce $t(s)$.
A lambda term may refer to a variable that has not been bound, such as the term $\lambda x.(x+y)$ (which represents the function definition $f(x)=x+y$). In this term, the variable y has not been defined and is considered an unknown. The abstraction $\lambda x.(x+y)$ is a syntactically valid term and represents a function that adds its input to the yet-unknown y.
Parentheses may be used and might be needed to disambiguate terms. For example,
* $\lambda x.((\lambda x.x)x)$ is of form $\lambda x.B$ and is therefore an abstraction, while
* $(\lambda x.(\lambda x.x))x$ is of form $MN$ and is therefore an application.
The examples 1 and 2 denote different terms, differing only in where the parentheses are placed. They have different meanings: example 1 is a function definition, while example 2 is a function application. The lambda variable x is a placeholder in both examples.
Here, example 1 defines a function $\lambda x.B$, where $B$ is $(\lambda x.x)x$, an anonymous function $(\lambda x.x)$, with input $x$; while example 2, $M$ $N$, is M applied to N, where $M$ is the lambda term $(\lambda x.(\lambda x.x))$ being applied to the input $N$ which is $x$. Both examples 1 and 2 would evaluate to the identity function $\lambda x.x$.

#### Functions that operate on functions

In lambda calculus, functions are taken to be 'first class values', so functions may be used as the inputs, or be returned as outputs from other functions.
For example, the lambda term $\lambda x.x$ represents the identity function, $x\mapsto x$. Further, $\lambda x.y$ represents the constant function $x\mapsto y$, the function that always returns $y$, no matter the input. As an example of a function operating on functions, the function composition can be defined as $\lambda f.\lambda g.\lambda x.(f(gx))$.
There are several notions of "equivalence" and "reduction" that allow lambda terms to be "reduced" to "equivalent" lambda terms.

#### Alpha equivalence

A basic form of equivalence, definable on lambda terms, is alpha equivalence. It captures the intuition that the particular choice of a bound variable, in an abstraction, does not (usually) matter. For instance, $\lambda x.x$ and $\lambda y.y$ are alpha-equivalent lambda terms, and they both represent the same function (the identity function). The terms $x$ and $y$ are not alpha-equivalent, because they are not bound in an abstraction. In many presentations, it is usual to identify alpha-equivalent lambda terms.
The following definitions are necessary in order to be able to define β-reduction:

#### Free variables

The free variables of a term are those variables not bound by an abstraction. The set of free variables of an expression is defined inductively:
* The free variables of $x$ are just $x$
* The set of free variables of $\lambda x.t$ is the set of free variables of $t$, but with $x$ removed
* The set of free variables of $ts$ is the union of the set of free variables of $t$ and the set of free variables of $s$.
For example, the lambda term representing the identity $\lambda x.x$ has no free variables, but the function $\lambda x.yx$ has a single free variable, $y$.

#### Capture-avoiding substitutions

Suppose $t$, $s$ and $r$ are lambda terms, and $x$ and $y$ are variables. The notation $t[x:=r]$ indicates substitution of $r$ for $x$ in $t$ in a capture-avoiding manner. This is defined so that:
* $x[x:=r]=r$ ; with $r$ substituted for $x$, $x$ becomes $r$
* $y[x:=r]=y$ if $x\neq y$ ; with $r$ substituted for $x$, $y$ (which is not $x$) remains $y$
* $(ts)[x:=r]=(t[x:=r])(s[x:=r])$ ; substitution distributes to both sides of an application
* $(\lambda x.t)[x:=r]=\lambda x.t$ ; a variable bound by an abstraction is not subject to substitution; substituting such variable leaves the abstraction unchanged
* $(\lambda y.t)[x:=r]=\lambda y.(t[x:=r])$ if $x\neq y$ and $y$ does not appear among the free variables of $r$ ($y$ is said to be "fresh" for $r$) ; substituting a variable which is not bound by an abstraction proceeds in the abstraction's body, provided that the abstracted variable $y$ is "fresh" for the substitution term $r$.
For example, $(\lambda x.x)[y:=y]=\lambda x.(x[y:=y])=\lambda x.x$, and $((\lambda x.y)x)[x:=y]=((\lambda x.y)[x:=y])(x[x:=y])=(\lambda x.y)y$.
The freshness condition (requiring that $y$ is not in the free variables of $r$) is crucial in order to ensure that substitution does not change the meaning of functions.
For example, a substitution that ignores the freshness condition could lead to errors: $(\lambda x.y)[y:=x]=\lambda x.(y[y:=x])=\lambda x.x$. This erroneous substitution would turn the constant function $\lambda x.y$ into the identity $\lambda x.x$.
In general, failure to meet the freshness condition can be remedied by alpha-renaming first, with a suitable fresh variable. For example, switching back to our correct notion of substitution, in $(\lambda x.y)[y:=x]$ the abstraction can be renamed with a fresh variable $z$, to obtain $(\lambda z.y)[y:=x]=\lambda z.(y[y:=x])=\lambda z.x$, and the meaning of the function is preserved by substitution.

#### β-reduction

The β-reduction rule states that an application of the form $(\lambda x.t)s$ reduces to the term $t[x:=s]$. The notation $(\lambda x.t)s\to t[x:=s]$ is used to indicate that $(\lambda x.t)s$ β-reduces to $t[x:=s]$. For example, for every $s$, $(\lambda x.x)s\to x[x:=s]=s$. This demonstrates that $\lambda x.x$ really is the identity. Similarly, $(\lambda x.y)s\to y[x:=s]=y$, which demonstrates that $\lambda x.y$ is a constant function.
The lambda calculus may be seen as an idealized version of a functional programming language, like Haskell or Standard ML. Under this view, β-reduction corresponds to a computational step. This step can be repeated by additional β-reductions until there are no more applications left to reduce. In the untyped lambda calculus, as presented here, this reduction process may not terminate. For instance, consider the term $\Omega =(\lambda x.xx)(\lambda x.xx)$. Here $(\lambda x.xx)(\lambda x.xx)\to (xx)[x:=\lambda x.xx]=(x[x:=\lambda x.xx])(x[x:=\lambda x.xx])=(\lambda x.xx)(\lambda x.xx)$. That is, the term reduces to itself in a single β-reduction, and therefore the reduction process will never terminate.
Another aspect of the untyped lambda calculus is that it does not distinguish between different kinds of data. For instance, it may be desirable to write a function that only operates on numbers. However, in the untyped lambda calculus, there is no way to prevent a function from being applied to truth values, strings, or other non-number objects.

## Formal definition
### Definition

Lambda expressions are composed of:
* variables v$_1$, v$_2$, ...;
* the abstraction symbols λ (lambda) and . (dot);
* parentheses.
The set of lambda expressions, Λ, can be defined inductively:
* If x is a variable, then x ∈ Λ.
* If x is a variable and M ∈ Λ, then (λx.M) ∈ Λ.
* If M, N ∈ Λ, then (M N) ∈ Λ.
Instances of rule 2 are known as abstractions and instances of rule 3 are known as applications. See § reducible expression
This set of rules may be written in Backus–Naur form as:

### Notation

To keep the notation of lambda expressions uncluttered, the following conventions are usually applied:
* Outermost parentheses are dropped: M N instead of (M N).
* Applications are assumed to be left associative: M N P may be written instead of ((M N) P).
* When all variables are single-letter, the space in applications may be omitted: MNP instead of M N P.
* The body of an abstraction extends as far right as possible: λx.M N means λx.(M N) and not (λx.M) N.
* A sequence of abstractions is contracted: λx.λy.λz.N is abbreviated as λxyz.N.

### Free and bound variables

The abstraction operator, λ, is said to bind its variable wherever it occurs in the body of the abstraction. Variables that fall within the scope of an abstraction are said to be bound. In an expression λx.M, the part λx is often called binder, as a hint that the variable x is getting bound by prepending λx to M. All other variables are called free. For example, in the expression λy.x x y, y is a bound variable and x is a free variable. Also a variable is bound by its nearest abstraction. In the following example the single occurrence of x in the expression is bound by the second lambda: λx.y (λx.z x).
The set of free variables of a lambda expression, M, is denoted as FV(M) and is defined by recursion on the structure of the terms, as follows:
* FV(x) = {x}, where x is a variable.
* FV(λx.M) = FV(M) \ {x}.
* FV(M N) = FV(M) ∪ FV(N).
An expression that contains no free variables is said to be closed. Closed lambda expressions are also known as combinators and are equivalent to terms in combinatory logic.

## Reduction

The meaning of lambda expressions is defined by how expressions can be reduced.
There are three kinds of reduction:
* α-conversion: changing bound variables;
* β-reduction: applying functions to their arguments;
* η-conversion: expressing extensionality.
We also speak of the resulting equivalences: two expressions are α-equivalent, if they can be α-converted into the same expression. β-equivalence and η-equivalence are defined similarly.
The term redex, short for reducible expression, refers to subterms that can be reduced by one of the reduction rules. For example, (λx.M) N is a β-redex in expressing the substitution of N for x in M. The expression to which a redex reduces is called its reduct; the reduct of (λx.M) N is M[x := N].
If x is not free in M, λx.M x is also an η-redex, with a reduct of M.

### α-conversion

α-conversion (alpha-conversion), sometimes known as α-renaming, allows bound variable names to be changed. For example, α-conversion of λx.x might yield λy.y. Terms that differ only by α-conversion are called α-equivalent. Frequently, in uses of lambda calculus, α-equivalent terms are considered to be equivalent.
The precise rules for α-conversion are not completely trivial. First, when α-converting an abstraction, the only variable occurrences that are renamed are those that are bound to the same abstraction. For example, an α-conversion of λx.λx.x could result in λy.λx.x, but it could not result in λy.λx.y. The latter has a different meaning from the original. This is analogous to the programming notion of variable shadowing.
Second, α-conversion is not possible if it would result in a variable getting captured by a different abstraction. For example, if we replace x with y in λx.λy.x, we get λy.λy.y, which is not at all the same.
In programming languages with static scope, α-conversion can be used to make name resolution simpler by ensuring that no variable name masks a name in a containing scope (see α-renaming to make name resolution trivial).
In the De Bruijn index notation, any two α-equivalent terms are syntactically identical.

#### Substitution

Substitution, written M[x := N], is the process of replacing all free occurrences of the variable x in the expression M with expression N. Substitution on terms of the lambda calculus is defined by recursion on the structure of terms, as follows (note: x and y are only variables while M and N are any lambda expression):
    x[x := N] = N
    y[x := N] = y, if x ≠ y
    (M$_1$ M$_2$)[x := N] = M$_1$[x := N] M$_2$[x := N]
    (λx.M)[x := N] = λx.M
    (λy.M)[x := N] = λy.(M[x := N]), if x ≠ y and y ∉ FV(N) See above for the FV
To substitute into an abstraction, it is sometimes necessary to α-convert the expression. For example, it is not correct for (λx.y)[y := x] to result in λx.x, because the substituted x was supposed to be free but ended up being bound. The correct substitution in this case is λz.x, up to α-equivalence. Substitution is defined uniquely up to α-equivalence. See Capture-avoiding substitutions above

### β-reduction

β-reduction (beta reduction) captures the idea of function application. β-reduction is defined in terms of substitution: the β-reduction of (λx.M) N is M[x := N].
For example, assuming some encoding of 2, 7, ×, we have the following β-reduction: (λn.n × 2) 7 → 7 × 2.
β-reduction can be seen to be the same as the concept of local reducibility in natural deduction, via the Curry–Howard isomorphism.

### η-conversion

η-conversion (eta conversion) expresses the idea of extensionality, which in this context is that two functions are the same if and only if they give the same result for all arguments. η-conversion converts between λx.f x and f whenever x does not appear free in f.
η-reduction changes λx.f x to f, and η-expansion changes f to λx.f x, under the same requirement that x does not appear free in f.
η-conversion can be seen to be the same as the concept of local completeness in natural deduction, via the Curry–Howard isomorphism.

## Normal forms and confluence

For the untyped lambda calculus, β-reduction as a rewriting rule is neither strongly normalising nor weakly normalising.
However, it can be shown that β-reduction is confluent when working up to α-conversion (i.e. we consider two normal forms to be equal if it is possible to α-convert one into the other).
Therefore, both strongly normalising terms and weakly normalising terms have a unique normal form. For strongly normalising terms, any reduction strategy is guaranteed to yield the normal form, whereas for weakly normalising terms, some reduction strategies may fail to find it.

## Encoding datatypes

The basic lambda calculus may be used to model arithmetic, Booleans, data structures, and recursion, as illustrated in the following sub-sections i, ii, iii, and § iv.

### Arithmetic in lambda calculus

There are several possible ways to define the natural numbers in lambda calculus, but by far the most common are the Church numerals, which can be defined as follows:
    0 := λf.λx.x
    1 := λf.λx.f x
    2 := λf.λx.f (f x)
    3 := λf.λx.f (f (f x))
and so on. Or using the alternative syntax presented above in Notation:
    0 := λfx.x
    1 := λfx.f x
    2 := λfx.f (f x)
    3 := λfx.f (f (f x))
A Church numeral is a higher-order function—it takes a single-argument function f, and returns another single-argument function. The Church numeral n is a function that takes a function f as argument and returns the n-th composition of f, i.e. the function f composed with itself n times. This is denoted f$^{(n)}$ and is in fact the n-th power of f (considered as an operator); f$^{(0)}$ is defined to be the identity function. Such repeated compositions (of a single function f) obey the laws of exponents, which is why these numerals can be used for arithmetic. (In Church's original lambda calculus, the formal parameter of a lambda expression was required to occur at least once in the function body, which made the above definition of 0 impossible.)
One way of thinking about the Church numeral n, which is often useful when analysing programs, is as an instruction 'repeat n times'. For example, using the PAIR and NIL functions defined below, one can define a function that constructs a (linked) list of n elements all equal to x by repeating 'prepend another x element' n times, starting from an empty list. The lambda term is
    λn.λx.n (PAIR x) NIL
By varying what is being repeated, and varying what argument that function being repeated is applied to, a great many different effects can be achieved.
We can define a successor function, which takes a Church numeral n and returns n + 1 by adding another application of f, where '(mf)x' means the function 'f' is applied 'm' times on 'x':
    SUCC := λn.λf.λx.f (n f x)
Because the m-th composition of f composed with the n-th composition of f gives the m+n-th composition of f, addition can be defined as follows:
    PLUS := λm.λn.λf.λx.m f (n f x)
PLUS can be thought of as a function taking two natural numbers as arguments and returning a natural number; it can be verified that
    PLUS 2 3
and
    5
are β-equivalent lambda expressions. Since adding m to a number n can be accomplished by adding 1 m times, an alternative definition is:
    PLUS := λm.λn.m SUCC n
Similarly, multiplication can be defined as
    MULT := λm.λn.λf.m (n f)
Alternatively
    MULT := λm.λn.m (PLUS n) 0
since multiplying m and n is the same as repeating the add n function m times and then applying it to zero. Exponentiation has a rather simple rendering in Church numerals, namely
    POW := λb.λe.e b
The predecessor function defined by PRED n = n − 1 for a positive integer n and PRED 0 = 0 is considerably more difficult. The formula
    PRED := λn.λf.λx.n (λg.λh.h (g f)) (λu.x) (λu.u)
can be validated by showing inductively that if T denotes (λg.λh.h (g f)), then T$^{(n)}$(λu.x) = (λh.h(f$^{(n−1)}$(x))) for n > 0. Two other definitions of PRED are given below, one using conditionals and the other using pairs. With the predecessor function, subtraction is straightforward. Defining
    SUB := λm.λn.n PRED m,
SUB m n yields m − n when m > n and 0 otherwise.

### Logic and predicates

By convention, the following two definitions (known as Church Booleans) are used for the Boolean values TRUE and FALSE:
    TRUE := λx.λy.x
    FALSE := λx.λy.y
Then, with these two lambda terms, we can define some logic operators (these are just possible formulations; other expressions could be equally correct):
    AND := λp.λq.p q p
    OR := λp.λq.p p q
    NOT := λp.p FALSE TRUE
    IFTHENELSE := λp.λa.λb.p a b
We are now able to compute some logic functions, for example:
    AND TRUE FALSE
       ≡ (λp.λq.p q p) TRUE FALSE →$_β$ TRUE FALSE TRUE
       ≡ (λx.λy.x) FALSE TRUE →$_β$ FALSE
and we see that AND TRUE FALSE is equivalent to FALSE.
A predicate is a function that returns a Boolean value. The most fundamental predicate is ISZERO, which returns TRUE if its argument is the Church numeral 0, but FALSE if its argument were any other Church numeral:
    ISZERO := λn.n (λx.FALSE) TRUE
The following predicate tests whether the first argument is less-than-or-equal-to the second:
    LEQ := λm.λn.ISZERO (SUB m n),
and since m = n, if LEQ m n and LEQ n m, it is straightforward to build a predicate for numerical equality.
The availability of predicates and the above definition of TRUE and FALSE make it convenient to write "if-then-else" expressions in lambda calculus. For example, the predecessor function can be defined as:
    PRED := λn.n (λg.λk.ISZERO (g 1) k (PLUS (g k) 1)) (λv.0) 0
which can be verified by showing inductively that n (λg.λk.ISZERO (g 1) k (PLUS (g k) 1)) (λv.0) is the add n − 1 function for n > 0.

### Pairs

A pair (2-tuple) can be defined in terms of TRUE and FALSE, by using the Church encoding for pairs. For example, PAIR encapsulates the pair (x,y), FIRST returns the first element of the pair, and SECOND returns the second.
    PAIR := λx.λy.λf.f x y
    FIRST := λp.p TRUE
    SECOND := λp.p FALSE
    NIL := λx.TRUE
    NULL := λp.p (λx.λy.FALSE)
A linked list can be defined as either NIL for the empty list, or the PAIR of an element and a smaller list. The predicate NULL tests for the value NIL. (Alternatively, with NIL := FALSE, the construct l (λh.λt.λz.deal_with_head_h_and_tail_t) (deal_with_nil) obviates the need for an explicit NULL test).
As an example of the use of pairs, the shift-and-increment function that maps (m, n) to (n, n + 1) can be defined as
    Φ := λx.PAIR (SECOND x) (SUCC (SECOND x))
which allows us to give perhaps the most transparent version of the predecessor function:
    PRED := λn.FIRST (n Φ (PAIR 0 0)).

## Additional programming techniques

There is a considerable body of programming idioms for lambda calculus. Many of these were originally developed in the context of using lambda calculus as a foundation for programming language semantics, effectively using lambda calculus as a low-level programming language. Because several programming languages include the lambda calculus (or something very similar) as a fragment, these techniques also see use in practical programming, but may then be perceived as obscure or foreign.

### Named constants

In lambda calculus, a library would take the form of a collection of previously defined functions, which as lambda-terms are merely particular constants. The pure lambda calculus does not have a concept of named constants since all atomic lambda-terms are variables, but one can emulate having named constants by setting aside a variable as the name of the constant, using abstraction to bind that variable in the main body, and apply that abstraction to the intended definition. Thus to use f to mean N (some explicit lambda-term) in M (another lambda-term, the "main program"), one can say
    (λf.M) N
Authors often introduce syntactic sugar, such as let, to permit writing the above in the more intuitive order
    let f = N in M
By chaining such definitions, one can write a lambda calculus "program" as zero or more function definitions, followed by one lambda-term using those functions that constitutes the main body of the program.
A notable restriction of this let is that the name f may not be referenced in N, for N is outside the scope of the abstraction binding f, which is M; this means a recursive function definition cannot be written with let. The letrec construction would allow writing recursive function definitions, where the scope of the abstraction binding f includes N as well as M. Or self-application a-la that which leads to Y combinator could be used.

### Recursion and fixed points

Recursion is the definition of a function invoking itself. A definition containing itself inside itself, by value, leads to the whole value being of infinite size. Other notations which support recursion natively overcome this by referring to the function definition by name. Lambda calculus cannot express this: all functions are anonymous in lambda calculus, so we can't refer by name to a value which is yet to be defined, inside the lambda term defining that same value. However, a lambda expression can receive itself as its own argument, for example in (λx.x x) E. Here E should be an abstraction, applying its parameter to a value to express recursion.
Consider the factorial function F(n) recursively defined by
    F(n) = 1, if n = 0; else n × F(n − 1).
In the lambda expression which is to represent this function, a parameter (typically the first one) will be assumed to receive the lambda expression itself as its value, so that calling it – applying it to an argument – will amount to recursion. Thus to achieve recursion, the intended-as-self-referencing argument (called r here) must always be passed to itself within the function body, at a call point:
    G := λr. λn.(1, if n = 0; else n × (r r (n−1)))
          with r r x = F x = G r x to hold, so r = G and
    F := G G = (λx.x x) G
The self-application achieves replication here, passing the function's lambda expression on to the next invocation as an argument value, making it available to be referenced and called there.
This solves it but requires re-writing each recursive call as self-application. We would like to have a generic solution, without a need for any re-writes:
    G := λr. λn.(1, if n = 0; else n × (r (n−1)))
          with r x = F x = G r x to hold, so r = G r =: FIX G and
    F := FIX G where FIX g := (r where r = g r) = g (FIX g)
          so that FIX G = G (FIX G) = (λn.(1, if n = 0; else n × ((FIX G) (n−1))))
Given a lambda term with first argument representing recursive call (e.g. G here), the fixed-point combinator FIX will return a self-replicating lambda expression representing the recursive function (here, F). The function does not need to be explicitly passed to itself at any point, for the self-replication is arranged in advance, when it is created, to be done each time it is called. Thus the original lambda expression (FIX G) is re-created inside itself, at call-point, achieving self-reference.
In fact, there are many possible definitions for this FIX operator, the simplest of them being:
    Y := λg.(λx.g (x x)) (λx.g (x x))
In the lambda calculus, Y g is a fixed-point of g, as it expands to:
    Y g
    ~> (λh.(λx.h (x x)) (λx.h (x x))) g
    ~> (λx.g (x x)) (λx.g (x x))
    ~> g ((λx.g (x x)) (λx.g (x x)))
    <~ g (Y g)
Now, to perform the recursive call to the factorial function for an argument n, we would simply call (Y G) n. Given n = 4, for example, this gives:
    (Y G) 4
    ~> G (Y G) 4
    ~> (λr.λn.(1, if n = 0; else n × (r (n−1)))) (Y G) 4
    ~> (λn.(1, if n = 0; else n × ((Y G) (n−1)))) 4
    ~> 1, if 4 = 0; else 4 × ((Y G) (4−1))
    ~> 4 × (G (Y G) (4−1))
    ~> 4 × ((λn.(1, if n = 0; else n × ((Y G) (n−1)))) (4−1))
    ~> 4 × (1, if 3 = 0; else 3 × ((Y G) (3−1)))
    ~> 4 × (3 × (G (Y G) (3−1)))
    ~> 4 × (3 × ((λn.(1, if n = 0; else n × ((Y G) (n−1)))) (3−1)))
    ~> 4 × (3 × (1, if 2 = 0; else 2 × ((Y G) (2−1))))
    ~> 4 × (3 × (2 × (G (Y G) (2−1))))
    ~> 4 × (3 × (2 × ((λn.(1, if n = 0; else n × ((Y G) (n−1)))) (2−1))))
    ~> 4 × (3 × (2 × (1, if 1 = 0; else 1 × ((Y G) (1−1)))))
    ~> 4 × (3 × (2 × (1 × (G (Y G) (1−1)))))
    ~> 4 × (3 × (2 × (1 × ((λn.(1, if n = 0; else n × ((Y G) (n−1)))) (1−1)))))
    ~> 4 × (3 × (2 × (1 × (1, if 0 = 0; else 0 × ((Y G) (0−1))))))
    ~> 4 × (3 × (2 × (1 × (1))))
    ~> 24
Every recursively defined function can be seen as a fixed point of some suitably defined higher order function (also known as functional) closing over the recursive call with an extra argument, and therefore, using Y, every recursively defined function can be expressed as a lambda expression. In particular, we can now cleanly define the subtraction, multiplication and comparison predicate of natural numbers recursively.
When Y combinator is coded directly in a strict programming language, the applicative order of evaluation used in such languages will cause the attempt to fully expand the internal self-application $(xx)$ right away, causing stack overflow or, in case of tail call optimization, indefinite looping. A delayed variant of Y, the Z combinator, can be used in such languages. It has the internal self-application hidden behind an extra abstraction through eta-expansion, as $(\lambda v.xxv)$, thus preventing its premature expansion:
    $Z=\lambda f.(\lambda x.f(\lambda v.xxv))\ (\lambda x.f(\lambda v.xxv))\ .$

### Standard terms

Certain terms have commonly accepted names:
    I := λx.x
    S := λx.λy.λz.x z (y z)
    K := λx.λy.x
    B := λx.λy.λz.x (y z)
    C := λx.λy.λz.x z y
    W := λx.λy.x y y
    ω or Δ or U := λx.x x
    Ω := ω ω
I is the identity function. SK and BCKW form complete combinator calculus systems that can express any lambda term - see the next section. Ω is UU, the smallest term that has no normal form. YI is another such term. Y is standard and defined above, and can also be defined as Y=BU(CBU), so that Yg=g(Yg). TRUE and FALSE defined above are commonly abbreviated as T and F.

### Abstraction elimination

If N is a lambda-term without abstraction, but possibly containing named constants (combinators), then there exists a lambda-term T(x,N) which is equivalent to λx.N but lacks abstraction (except as part of the named constants, if these are considered non-atomic). This can also be viewed as anonymising variables, as T(x,N) removes all occurrences of x from N, while still allowing argument values to be substituted into the positions where N contains an x. The conversion function T can be defined by:
    T(x, x) := I
    T(x, N) := K N if x is not free in N.
    T(x, M N) := S T(x, M) T(x, N)
In either case, a term of the form T(x,N) P can reduce by having the initial combinator I, K, or S grab the argument P, just like β-reduction of (λx.N) P would do. I returns that argument. K throws the argument away, just like (λx.N) would do if x has no free occurrence in N. S passes the argument on to both subterms of the application, and then applies the result of the first to the result of the second.
The combinators B and C are similar to S, but pass the argument on to only one subterm of an application (B to the "argument" subterm and C to the "function" subterm), thus saving a subsequent K if there is no occurrence of x in one subterm. In comparison to B and C, the S combinator actually conflates two functionalities: rearranging arguments, and duplicating an argument so that it may be used in two places. The W combinator does only the latter, yielding the B, C, K, W system as an alternative to SKI combinator calculus.

## Typed lambda calculus

A typed lambda calculus is a typed formalism that uses the lambda-symbol ($\lambda$) to denote anonymous function abstraction. In this context, types are usually objects of a syntactic nature that are assigned to lambda terms; the exact nature of a type depends on the calculus considered (see Kinds of typed lambda calculi). From a certain point of view, typed lambda calculi can be seen as refinements of the untyped lambda calculus but from another point of view, they can also be considered the more fundamental theory and untyped lambda calculus a special case with only one type.
Typed lambda calculi are foundational programming languages and are the base of typed functional programming languages such as ML and Haskell and, more indirectly, typed imperative programming languages. Typed lambda calculi play an important role in the design of type systems for programming languages; here typability usually captures desirable properties of the program, e.g., the program will not cause a memory access violation.
Typed lambda calculi are closely related to mathematical logic and proof theory via the Curry–Howard isomorphism and they can be considered as the internal language of classes of categories, e.g., the simply typed lambda calculus is the language of a Cartesian closed category (CCC).

## Reduction strategies

Whether a term is normalising or not, and how much work needs to be done in normalising it if it is, depends to a large extent on the reduction strategy used. Common lambda calculus reduction strategies include:

### Normal order

    The leftmost outermost redex is reduced first. That is, whenever possible, arguments are substituted into the body of an abstraction before the arguments are reduced. If a term has a beta-normal form, normal order reduction will always reach that normal form.

### Applicative order

    The leftmost innermost redex is reduced first. As a consequence, a function's arguments are always reduced before they are substituted into the function. Unlike normal order reduction, applicative order reduction may fail to find the beta-normal form of an expression, even if such a normal form exists. For example, the term $(\;\lambda x.y\;\;(\lambda z.(zz)\;\lambda z.(zz))\;)$ is reduced to itself by applicative order, while normal order reduces it to its beta-normal form $y$.

### Full β-reductions

    Any redex can be reduced at any time. This means essentially the lack of any particular reduction strategy—with regard to reducibility, "all bets are off".
Weak reduction strategies do not reduce under lambda abstractions:

### Call by value

    Like applicative order, but no reductions are performed inside abstractions. This is similar to the evaluation order of strict languages like C: the arguments to a function are evaluated before calling the function, and function bodies are not even partially evaluated until the arguments are substituted in.

### Call by name

    Like normal order, but no reductions are performed inside abstractions. For example, λx.(λy.y)x is in normal form according to this strategy, although it contains the redex (λy.y)x.
Strategies with sharing reduce computations that are "the same" in parallel:

### Optimal reduction

    As normal order, but computations that have the same label are reduced simultaneously.

### Call by need

    As call by name (hence weak), but function applications that would duplicate terms instead name the argument. The argument may be evaluated "when needed", at which point the name binding is updated with the reduced value. This can save time compared to normal order evaluation.

## Computability

There is no algorithm that takes as input any two lambda expressions and outputs TRUE or FALSE depending on whether one expression reduces to the other. More precisely, no computable function can decide the question. This was historically the first problem for which undecidability could be proven. As usual for such a proof, computable means computable by any model of computation that is Turing complete. In fact computability can itself be defined via the lambda calculus: a function F: N → N of natural numbers is a computable function if and only if there exists a lambda expression f such that for every pair of x, y in N, F(x)=y if and only if f x =$_β$ y, where x and y are the Church numerals corresponding to x and y, respectively and =$_β$ meaning equivalence with β-reduction. See the Church–Turing thesis for other approaches to defining computability and their equivalence.
Church's proof of uncomputability first reduces the problem to determining whether a given lambda expression has a normal form. Then he assumes that this predicate is computable, and can hence be expressed in lambda calculus. Building on earlier work by Kleene and constructing a Gödel numbering for lambda expressions, he constructs a lambda expression e that closely follows the proof of Gödel's first incompleteness theorem. If e is applied to its own Gödel number, a contradiction results.

## Complexity

The notion of computational complexity for the lambda calculus is a bit tricky, because the cost of a β-reduction may vary depending on how it is implemented. To be precise, one must somehow find the location of all of the occurrences of the bound variable V in the expression E, implying a time cost, or one must keep track of the locations of free variables in some way, implying a space cost. A naïve search for the locations of V in E is O(n) in the length n of E. Director strings were an early approach that traded this time cost for a quadratic space usage. More generally this has led to the study of systems that use explicit substitution.
In 2014, it was shown that the number of β-reduction steps taken by normal order reduction to reduce a term is a reasonable time cost model, that is, the reduction can be simulated on a Turing machine in time polynomially proportional to the number of steps. This was a long-standing open problem, due to size explosion, the existence of lambda terms which grow exponentially in size for each β-reduction. The result gets around this by working with a compact shared representation. The result makes clear that the amount of space needed to evaluate a lambda term is not proportional to the size of the term during reduction. It is not currently known what a good measure of space complexity would be.
An unreasonable model does not necessarily mean inefficient. Optimal reduction reduces all computations with the same label in one step, avoiding duplicated work, but the number of parallel β-reduction steps to reduce a given term to normal form is approximately linear in the size of the term. This is far too small to be a reasonable cost measure, as any Turing machine may be encoded in the lambda calculus in size linearly proportional to the size of the Turing machine. The true cost of reducing lambda terms is not due to β-reduction per se but rather the handling of the duplication of redexes during β-reduction. It is not known if optimal reduction implementations are reasonable when measured with respect to a reasonable cost model such as the number of leftmost-outermost steps to normal form, but it has been shown for fragments of the lambda calculus that the optimal reduction algorithm is efficient and has at most a quadratic overhead compared to leftmost-outermost. In addition the BOHM prototype implementation of optimal reduction outperformed both Caml Light and Haskell on pure lambda terms.

## Lambda calculus and programming languages

As pointed out by Peter Landin's 1965 paper "A Correspondence between ALGOL 60 and Church's Lambda-notation", sequential procedural programming languages can be understood in terms of the lambda calculus, which provides the basic mechanisms for procedural abstraction and procedure (subprogram) application.

### Anonymous functions

For example, in Python the "square" function can be expressed as a lambda expression as follows:
The above example is an expression that evaluates to a first-class function. The symbol lambda creates an anonymous function, given a list of parameter names, x – just a single argument in this case, and an expression that is evaluated as the body of the function, x**2. Anonymous functions are sometimes called lambda expressions.
For example, Pascal and many other imperative languages have long supported passing subprograms as arguments to other subprograms through the mechanism of function pointers. However, function pointers are an insufficient condition for functions to be first class datatypes, because a function is a first class datatype if and only if new instances of the function can be created at runtime. Such runtime creation of functions is supported in Smalltalk, JavaScript, Wolfram Language, and more recently in Scala, Eiffel (as agents), C# (as delegates) and C++11, among others.

### Parallelism and concurrency

The Church–Rosser property of the lambda calculus means that evaluation (β-reduction) can be carried out in any order, even in parallel. This means that various nondeterministic evaluation strategies are relevant. However, the lambda calculus does not offer any explicit constructs for parallelism. One can add constructs such as futures to the lambda calculus. Other process calculi have been developed for describing communication and concurrency.

## Semantics

The fact that lambda calculus terms act as functions on other lambda calculus terms, and even on themselves, led to questions about the semantics of the lambda calculus. Could a sensible meaning be assigned to lambda calculus terms? The natural semantics was to find a set D isomorphic to the function space D → D, of functions on itself. However, no nontrivial such D can exist, by cardinality constraints because the set of all functions from D to D has greater cardinality than D, unless D is a singleton set.
In the 1970s, Dana Scott showed that if only continuous functions were considered, a set or domain D with the required property could be found, thus providing a model for the lambda calculus.
This work also formed the basis for the denotational semantics of programming languages.

## Variations and extensions

These extensions are in the lambda cube:
* Typed lambda calculus – Lambda calculus with typed variables (and functions)
* System F – A typed lambda calculus with type-variables
* Calculus of constructions – A typed lambda calculus with types as first-class values
These formal systems are extensions of lambda calculus that are not in the lambda cube:
* Binary lambda calculus – A version of lambda calculus with binary input/output (I/O), a binary encoding of terms, and a designated universal machine.
* Lambda-mu calculus – An extension of the lambda calculus for treating classical logic
These formal systems are variations of lambda calculus:
* Kappa calculus – A first-order analogue of lambda calculus
These formal systems are related to lambda calculus:
* Combinatory logic – A notation for mathematical logic without variables
* SKI combinator calculus – A computational system based on the S, K and I combinators, equivalent to lambda calculus, but reducible without variable substitutions