Models of Computation

Lecture 2: Regular Languages [Sp’18]

But the Lord came down to see the city and the tower the people were building. The Lordsaid, “If as one people speaking the same language they have begun to do this, then nothing
they plan to do will be impossible for them. Come, let us go down and confuse their language
so they will not understand each other.”— Genesis 11:6–7 (New International Version)
Soyez réglé dans votre vie et ordinaire comme un bourgeois,
afin d’être violent et original dans vos œuvres.
[Be regular and orderly in your life like a bourgeois,
so that you may be violent and original in your work.]— Gustave Flaubert, in a letter to Gertrude Tennant (December 25, 1876)
Some people, when confronted with a problem, think "I know, I’ll use regular expressions."
Now they have two problems.
— Jamie Zawinski, alt.religion.emacs (August 12, 1997)
As far as I am aware this pronunciation is incorrect in all known languages.
— Kenneth Kleene, describing his father Stephen’s pronunciation of his last name2

Regular Languages

2.1

Languages

A formal language (or just a language) is a set of strings over some finite alphabet Σ, orequivalently, an arbitrary subset of Σ∗ . For example, each of the following sets is a language:• The empty set ∅.1• The set {"}.
• The set {0, 1}∗ .
• The set {THE, OXFORD, ENGLISH, DICTIONARY}.
• The set of all subsequences of THE  OXFORD  ENGLISH  DICTIONARY.
• The set of all words in The Oxford English Dictionary.
• The set of all strings in {0, 1}∗ with an odd number of 1s.
• The set of all strings in {0, 1}∗ that represent a prime number in base 13.
• The set of all sequences of turns that solve the Rubik’s cube (starting in some fixed
configuration)• The set of all python programs that print “Hello World!”
As a notational convention, I will always use italic upper-case letters (usually L, but also A, B, C,
and so on) to represent languages.
Formal languages are not “languages” in the same sense that English, Klingon, and Python
are “languages”. Strings in a formal language do not necessarily carry any “meaning”, nor
are they necessarily assembled into larger units (“sentences” or “paragraphs” or “packages”)
according to some “grammar”.
1The empty set symbol ∅ was introduced in 1939 by André Weil, as a member of the pseudonymous mathematicalcollective Nicholai Bourbaki. The symbol derives from the Norwegian letter Ø, which pronounced like a German ö ora sound of disgust, and not from the Greek letter φ. Calling the empty set “fie” or “fee” makes the baby Jesus cry.
© Copyright 2018 Jeff Erickson.
This work is licensed under a Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/4.0/).
Free distribution is strongly encouraged; commercial distribution is expressly forbidden.
See http://jeffe.cs.illinois.edu/teaching/algorithms/ for the most recent revision.

1Models of Computation

Lecture 2: Regular Languages [Sp’18]

It is very important to distinguish between three “empty” objects. Many beginning students
have trouble keeping these straight.
• ∅ is the empty language, which is a set containing zero strings. ∅ is not a string.
• {"} is a language containing exactly one string, which has length zero. {"} is not empty,
and it is not a string.
• " is the empty string, which is a sequence of length zero. " is not a language.

2.2

Building Languages

Languages can be combined and manipulated just like any other sets. Thus, if A and B are
languages over Σ, then their union A ∪ B, intersection A ∩ B, difference A \ B, and symmetricdifference A ⊕ B are also languages over Σ, as is the complement A := Σ∗ \ A. However, there are
two more useful operators that are specific to sets of strings.
The concatenation of two languages A and B, again denoted A • B or just AB, is the set of
all strings obtained by concatenating an arbitrary string in A with an arbitrary string in B:
A • B := {x y | x ∈ A and y ∈ B}.
For example, if A = {HOCUS, ABRACA} and B = {POCUS, DABRA}, thenA • B = {HOCUSPOCUS, ABRACAPOCUS, HOCUSDABRA, ABRACADABRA}.
In particular, for every language A, we have∅ • A = A• ∅ = ∅and{"} • A = A • {"} = A.

The Kleene closure or Kleene star2 of a language L, denoted L∗ , is the set of all strings obtainedby concatenating a sequence of zero or more strings from L. For example, {0, 11}∗ = {", 0, 00, 11,000, 011, 110, 0000, 0011, 0110, 1100, 1111, 00000, 00011, 011110011011, . . .}. Moreformally, L ∗ is defined recursively as the set of all strings w such that either• w = ", or• w = x y, for some strings x ∈ L and y ∈ L ∗ .
This definition immediately implies that∅∗ = {"}∗ = {"}.
For any other language L, the Kleene closure L ∗ is infinite and contains arbitrarily long (butfinite!) strings. Equivalently, L ∗ can also be defined as the smallest superset of L that contains theempty string " and is closed under concatenation (hence “closure”). The set of all strings Σ∗ is,just as the notation suggests, the Kleene closure of the alphabet Σ (where each symbol is viewed
as a string of length 1).
A useful variant of the Kleene closure operator is the Kleene plus, defined as L + := L • L ∗ .
Thus, L + is the set of all strings obtained by concatenating a sequence of one or more strings
from L.
The following identities, which we state here without (easy) proofs, are useful for designing,
simplifying, and understanding languages.
2named after logician Stephen Cole Kleene, who actually pronounced his last name “clay-knee”, not “clean” or“cleanie” or “claynuh” or “dimaggio”.

2Models of Computation

Lecture 2: Regular Languages [Sp’18]

Lemma 2.1. The following identities hold for all languages A, B, and C:(a) A ∪ B = B ∪ A.
(b) (A ∪ B) ∪ C = A ∪ (B ∪ C).
(c) ∅ • A = A • ∅ = ∅.
(d) {"} • A = A • {"} = A.
(e) (A • B) • C = A • (B • C).
(f) A • (B ∪ C) = (A • B) ∪ (A • C).
(g) (A ∪ B) • C = (A • C) ∪ (B • C).
Lemma 2.2. The following identities hold for every language L:(a) L ∗ = {"} ∪ L + = L ∗ • L ∗ = (L ∪ {"})∗ = (L \ {"})∗ = {"} ∪ L ∪ (L + • L + ).
(b) L + = L • L ∗ = L ∗ • L = L + • L ∗ = L ∗ • L + = L ∪ (L + • L + ).
(c) L + = L ∗ if and only if " ∈ L.
Lemma 2.3 (Arden’s Rule). For any languages A, B, and L such that L = A • L ∪ B, we haveA∗ • B ⊆ L. Moreover, if A does not contain the empty string, then L = A • L ∪ B if and only if
L = A∗ • B.

2.3

Regular Languages and Regular Expressions

Intuitively, a language is regular if it can be constructed from individual strings using any
combination of union, concatenation, and unbounded repetition. More formally, a language L isregular if and only if it satisfies one of the following (recursive) conditions:• L is empty;• L contains exactly one string (which could be the empty string ");• L is the union of two regular languages;• L is the concatenation of two regular languages; or• L is the Kleene closure of a regular language.
Regular languages are normally described using a compact notation called regular expressions, which omit braces around one-string sets, use + to represent union instead of ∪, andjuxtapose subexpressions to represent concatenation instead of using an explicit operator •. By
convention, in the absence of parentheses, the ∗ operator has highest precedence, followed bythe (implicit) concatenation operator, followed by +.
For example, the regular expression 10∗ is shorthand for the language {1} • {0}∗ (containing
all strings consisting of a 1 followed by zero or more 0s), and not the language {10}∗ (containing
all strings of even length that start with 1 and alternate between 1 and 0). As a larger example,
the regular expression0∗ 0 + 0∗ 1(10∗ 1 + 01∗ 0)∗ 10∗
represents the language({0}∗ • {0}) ∪ {0}∗ • {1} • ({1} • {0}∗ • {1}) ∪ ({0} • {1}∗ • {0})3∗
• {1} • {0}∗ .

Models of Computation

Lecture 2: Regular Languages [Sp’18]

Most of the time we do not distinguish between regular expressions and the languages they
represent, for the same reason that we do not normally distinguish between the arithmetic
expression “2+2” and the integer 4, or the symbol π and the area of the unit circle. However, wesometimes need to refer to regular expressions themselves as strings. In those circumstances, wewrite L(R) to denote the language represented by the regular expression R. String w matches
regular expression R if and only if w ∈ L(R).
Here are several more examples of regular expressions and the languages they represent.
• 0∗ — the set of all strings of 0s, including the empty string.
• 00000∗ — the set of all strings consisting of at least four 0s.
• (00000)∗ — the set of all strings of 0s whose length is a multiple of 5.
• (0 + 1)∗ — the set of all binary strings.
• (" + 1)(01)∗ (" + 0) — the set of all strings of alternating 0s and 1s, or equivalently, theset of all binary strings that do not contain the substrings 00 or 11.
• (0 + 1)∗ 0000(0 + 1)∗ — the set of all binary strings that contain the substring 0000.
• ((" + 0 + 00 + 000)1)∗ (" + 0 + 00 + 000) — the set of all binary strings that do not containthe substring 0000.
• ((0 + 1)(0 + 1))∗ — the set of all binary strings whose length is even.
• 1∗ (01∗ 01∗ )∗ — the set of all binary strings with an even number of 0s.
• 0 + 1(0 + 1)∗ 00 — the set of all non-negative binary numerals divisible by 4 and with noredundant leading 0s.
• 0∗ 0 + 0∗ 1(10∗ 1 + 01∗ 0)∗ 10∗ — the set of all non-negative binary numerals divisible by 3,
possibly with redundant leading 0s.
The last example should not be obvious. It is straightforward, but really tedious, to proveby induction that every string in 0∗ 0 + 0∗ 1(10∗ 1 + 01∗ 0)∗ 10∗ is the binary representation of a
non-negative multiple of 3. It is similarly straightforward, but even more tedious, to prove thatthe binary representation of every non-negative multiple of 3 matches this regular expression. In
a later note, we will see a systematic method for deriving regular expressions for some languages
that avoids (or more accurately, automates) this tedium.
Two regular expressions R and R0 are equivalent if they describe the same language. For
example, the regular expressions (0 + 1)∗ and (1 + 0)∗ are equivalent, because the union
operator is commutative. More subtly, the regular expressions (0 + 1)∗ and (0∗ 1∗ )∗ and(00 + 01 + 10 + 11)∗ (0 + 1 + ") are all equivalent; intuitively, these three expressions represent
different ways of thinking about the language {0, 1}∗ . In fact, almost every regular language canbe represented by infinitely many distinct but equivalent regular expressions, even if we ignore
ultimately trivial equivalences like L = (L∅)∗ L" + ∅.

ÆÆÆGive some examples of designing regular expressions via Arden’s rule. For example: Allstrings that don’t contain the substring 0011.

4Models of Computation

2.4

Lecture 2: Regular Languages [Sp’18]

Things What Ain’t Regular Expressions

Many computing environments and programming languages support patterns called regexen(singular regex, pluralized like ox) that are considerably more general and powerful than regularexpressions. Regexen include special symbols representing negation, character classes (forexample, upper-case letters, or digits), contiguous ranges of characters, line and word boundaries,
limited repetition (as opposed to the unlimited repetition allowed by ∗ ), back-references to earlier
subexpressions, and even local variables. Despite its obvious etymology, a regex is not necessarilya regular expression, and it does not necessarily describe a regular language!3
Another type of pattern that is often confused with regular expression are globs, which
are patterns used in most Unix shells and some scripting languages to represent sets filenames. Globs include symbols for arbitrary single characters (?), single characters from a
specified range ([a-z]), arbitrary substrings (*), and substrings from a specified finite set({foo,ba{r,z}}). Globs are significantly less powerful than regular expressions.

2.5

Regular Expression Trees

Regular expressions are convenient notational shorthand for a more explicit representation ofregular languages called regular expression trees. A regular expression tree is formally definedas one of the following:• A leaf node labeled ∅.
• A leaf node labeled with a string in Σ∗ .
• A node labeled + with two children, each of which is the root of a regular expression tree.
• A node labeled • with two children, each of which is the root of a regular expression tree.
• A node labeled ∗ with one child, which is the root of a regular expression tree.
These cases mirror the definition of regular language exactly. A leaf labeled ∅ represents theempty language; a leaf labeled with a string represents the language containing only that string;a node labeled + represents the union of the languages represented by its two children; a nodelabeled • represents the concatenation of the languages represented by its two children; and anode labeled ∗ represents the Kleene closure of the languages represented by its child.
+
•
*
0

•
0

*

•

0

1

•
*

•

+

1

•
1

•
•

*

0
1

0

*
0

•
*

0

1

A regular expression tree for 0∗ 0 + 0∗ 1(10∗ 1 + 01∗ 0)∗ 10∗

3However, regexen are not all-powerful, either; see http://stackoverflow.com/a/1732454/775369.

5Models of Computation

Lecture 2: Regular Languages [Sp’18]

The size of a regular expression is the number of nodes in its regular expression tree. The sizeof a regular expression could be either larger or smaller than its length as a raw string. On the
one hand, concatenation nodes in the tree are not represented by symbols in the string; on theother hand, parentheses in the string are not represented by nodes in the tree. For example, theregular expression 0∗ 0 + 0∗ 1(10∗ 1 + 01∗ 0)∗ 10∗ has size 29, but the corresponding raw string
0*0+0*1(10*1+01*0)*10* has length 22.
A subexpression of a regular expression R is another regular expression S whose regularexpression tree is a subtree of some regular expression tree for R. A proper subexpression of R
is any subexpression except R itself. Every subexpression of R is also a substring of R, but not
every substring is a subexpression. For example, the substring 10∗ 1 is a proper subexpressionof 0∗ 0 + 0∗ 1(10∗ 1 + 01∗ 0)∗ 10∗ . However, the substrings 0∗ 0 + 0∗ 1 and 0∗ 1 + 01∗ are notsubexpressions of 0∗ 0 + 0∗ 1(10∗ 1 + 01∗ 0)∗ 10∗ , even though they are well-formed regularexpressions.

2.6

Proofs about Regular Expressions

The standard strategy for proving properties of regular expressions, just as for any other recursively
defined structure, to argue inductively on the recursive structure of the expression, rather thanconsidering the regular expression as a raw string.
Induction proofs about regular expressions follow a standard boilerplate that mirrors therecursive definition of regular languages. Suppose we want to prove that every regular expression
is perfectly cromulent, whatever that means. The white boxes hide additional proof details that,among other things, depend on the precise definition of “perfectly cromulent”. The boilerplate
structure is longer than the boilerplate for string induction proofs, but don’t be fooled into thinking
it’s harder. The five cases in the proof mirror the five cases in the recursive definition of regular
language.
Proof: Let R be an arbitrary regular expression.
Assume that every proper subexpression of R is perfectly cromulent.
There are five cases to consider.

• Suppose R = ∅.

Therefore, R is perfectly cromulent.

• Suppose R is a single string.

Therefore, R is perfectly cromulent.

• Suppose R = S + T for some regular expressions S and T .
The induction hypothesis implies that S and T are perfectly cromulent.

Therefore, R is perfectly cromulent.

• Suppose R = S • T for some regular expressions S and T .
The induction hypothesis implies that S and T are perfectly cromulent.

6Models of Computation

Lecture 2: Regular Languages [Sp’18]

Therefore, R is perfectly cromulent.

• Suppose R = S ∗ for some regular expression. S .
The induction hypothesis implies that S is perfectly cromulent.

Therefore, R is perfectly cromulent.
In all cases, we conclude that w is perfectly cromulent.



Students uncomfortable with structural induction can instead induct on the size of the regularexpression (defined as the number of nodes in the corresponding regular expression tree). Thisvariant changes only the statement of inductive hypothesis, not the structure of the proof itself;
the rest of the boilerplate is utterly identical.
Proof: Let R be an arbitrary regular expression.
Assume that every regular expression smaller than R is perfectly cromulent.
There are five cases to consider.
..
.
In all cases, we conclude that w is perfectly cromulent.


Here is an example of the structural induction boilerplate in action. Again, this proof is longer
than a typical induction proof about strings or integers, but each individual case is still just a
short exercise in definition-chasing.
Lemma 2.4. Every regular expression that does not use the symbol ∅ represents a non-empty
language.
Proof: Let R be an arbitrary regular expression that does not use the symbol ∅. Assume thatevery proper subexpression of R that does not use the symbol ∅ represents a non-empty language.
There are five cases to consider, mirroring the definition of R.
• If R = ∅, we have a contradiction; we can ignore this case.
• If R is a single string w, then L(R) contains the string w. (In fact, L(R) = {w}.)• Suppose R = S + T for some regular expressions S and T .
S does not use the symbol ∅, because otherwise R would.
Thus, the inductive hypothesis implies that L(S) is non-empty.
Choose an arbitrary string x ∈ L(S).
Then L(R) = L(S + T ) = L(S) ∪ L(T ) also contains the string x.
• Suppose R = S • T for some regular expressions S and T .
Neither S nor T uses the symbol ∅, because otherwise R would.
Thus, the inductive hypothesis implies that both L(S) and L(T ) are non-empty.
Choose arbitrary strings x ∈ L(S) and y ∈ L(T ).
Then L(R) = L(S • T ) = L(S) • L(T ) contains the string x y.
• Suppose R = S ∗ for some regular expression S.
Then L(R) contains the empty string ".
In every case, the language L(R) is non-empty.

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Lecture 2: Regular Languages [Sp’18]

Similarly, most algorithms that accept regular expressions as input actually require regularexpression trees, rather than regular expressions as raw strings. Fortunately, it is possible to parse
any regular expression of length n into an equivalent regular expression tree in O(n) time. (Thedetails of the parsing algorithm are beyond the scope of this chapter.) Thus, when we see analgorithmic problem that starts “Given a regular expression. . . ”, we can assume without loss ofgenerality that we are actually given a regular expression tree.

2.7

Proofs about Regular Languages

The same boilerplate also applies to inductive arguments about properties of regular languages.
Languages themselves are just unadorned sets; they don’t have any recursive structure that webuild an inductive proof around. In particular, proper subsets of an infinite language L are notnecessarily “smaller” than L! Rather than trying to argue directly about an arbitrary regular
language L, we choose an arbitrary regular expression that represents L, and then build our
inductive argument around the recursive structure of that regular expression.
Lemma 2.5. Every non-empty regular language is represented by a regular expression that does
not use the symbol ∅.
Proof: Let R be an arbitrary regular expression; we need to prove that either L(R) = ∅ or
L(R) = L(R0 ) for some ∅-free regular expression R0 . For every proper subexpression S of R,
assume that either L(S) = ∅ or L(S) = L(S 0 ) for some ∅-free regular expression S 0 . There are
five cases to consider, mirroring the definition of R.
• If R = ∅, then L(R) = ∅.
• If R is a single string w, then R is already ∅-free.
• Suppose R = S + T for some regular expressions S and T . There are four subcases toconsider:– If L(S) = L(T ) = ∅, then L(R) = L(S) ∪ L(T ) = ∅.
– Suppose L(S) 6= ∅ and L(T ) = ∅. The inductive hypothesis implies that there is a∅-free regular expression S 0 such that L(S 0 ) = L(S) = L(S) ∪ L(T ) = L(R).
– Suppose L(S) = ∅ and L(T ) 6= ∅. The inductive hypothesis implies that there is a∅-free regular expression T 0 such that L(T 0 ) = L(T ) = L(S) ∪ L(T ) = L(R).
– Finally, suppose L(S) 6= ∅ and L(T ) 6= ∅. The inductive hypothesis implies that thereare ∅-free regular expressions S 0 and T 0 such that L(S 0 ) = L(S) and L(T 0 ) = L(T ).
The regular expression S 0 +T 0 is ∅-free and L(S 0 +T 0 ) = L(S 0 )∪ L(T 0 ) = L(S)∪ L(T ) =L(S + T ) = L(R).
• Suppose R = S • T for some regular expressions S and T . There are two subcases toconsider.
– If either L(S) = ∅ or L(T ) = ∅ then L(R) = L(S) • L(T ) = ∅.
– Otherwise, the inductive hypothesis implies that there are ∅-free regular expressions S 0
and T 0 such that L(S 0 ) = L(S) and L(T 0 ) = L(T ). The regular expression S 0 • T 0 is∅-free and equivalent to R.
• Suppose R = S ∗ for some regular expression S. There are two subcases to consider.
8Models of Computation

Lecture 2: Regular Languages [Sp’18]– If L(S) = ∅, then L(R) = ∅∗ = {"}, so R is represented by the ∅-free regularexpression ".
– Otherwise, The inductive hypothesis implies that there is a ∅-free regular expression S 0such that L(S 0 ) = L(S). The regular expression (S 0 )∗ is ∅-free and equivalent to R.
In all cases, either L(R) = ∅ or R is equivalent to some ∅-free regular expression R0 .

2.8

Not Every Language is Regular

You may be tempted to conjecture that all languages are regular, but in fact, the following
cardinality argument almost all languages are not regular. To make the argument concrete, let’s
consider languages over the single-symbol alphabet {}.
• Every regular expression over the one-symbol alphabet {} is itself a string over theseven-symbol alphabet {, +, (, ), *, 3, Ø}. By interpreting these symbols as the digits 1
through 7, we can interpret any string over this larger alphabet as the base-8 representationof some unique integer. Thus, the set of all regular expressions over {} is at most as large
as the set of integers, and is therefore countably infinite. It follows that the set of all regular
languages over {} is also countably infinite.
• On the other hand, for any real number 0 ≤ α < 1, we can define a corresponding languageLα =  n α2n mod 1 ≥ 1/2 .
In other words, Lα contains the string  n if and only if the (n + 1)th bit in the binary
representation of α is equal to 1. For any distinct real numbers α 6= β, the binary
representations of α and β must differ in some bit, so Lα 6= Lβ . We conclude that the setof all languages over {} is at least as large as the set of real numbers between 0 and 1,
and is therefore uncountably infinite.
We will see several explicit examples of non-regular languages in later lectures. In particular, theset of all regular expressions over the alphabet {0, 1} is itself a non-regular language over thealphabet {0, 1, +, (, ), *, 3, Ø}!

Exercises
1. (a) Prove that ∅ • L = L • ∅ = ∅, for every language L.
(b) Prove that {"} • L = L • {"} = L, for every language L.
(c) Prove that (A • B) • C = A • (B • C), for all languages A, B, and C.
(d) Prove that |A• B| ≤ |A| · |B|, for all languages A and B. (The second · is multiplication!)i. Describe two languages A and B such that |A • B| < |A| · |B|.
ii. Describe two languages A and B such that |A • B| = |A| · |B|.
(e) Prove that L ∗ is finite if and only if L = ∅ or L = {"}.
(f) Prove that if A • B = B • C, then A∗ • B = B • C ∗ = A∗ • B • C ∗ , for all languages A, B,
and C.
(g) Prove that (A ∪ B)∗ = (A∗ • B ∗ )∗ , for all languages A and B.

9Models of Computation

Lecture 2: Regular Languages [Sp’18]

2. Recall that the reversal wR of a string w is defined recursively as follows:("
if w = "wR :=R
x • a if w = a · x
The reversal L R of any language L is the set of reversals of all strings in L:
L R := wR w ∈ L .
(a) Prove that (A • B)R = B R • AR for all languages A and B.
(b) Prove that (L R )R = L for every language L.
(c) Prove that (L ∗ )R = (L R )∗ for every language L.
3. Prove that each of the following regular expressions is equivalent to (0 + 1)∗ .
(a) " + 0(0 + 1)∗ + 1(1 + 0)∗(b) 0∗ + 0∗ 1(0 + 1)∗(c) ((" + 0)(" + 1))∗(d) 0∗ (10∗ )∗(e) (1∗ 0)∗ (0∗ 1)∗
4. For each of the following languages in {0, 1}∗ , describe an equivalent regular expression.
There are infinitely many correct answers for each language. (This problem will become
significantly simpler after we’ve seen finite-state machines.)(a) Strings that end with the suffix 09 = 000000000.
(b) All strings except 010.
(c) Strings that contain the substring 010.
(d) Strings that contain the subsequence 010.
(e) Strings that do not contain the substring 010.
(f) Strings that do not contain the subsequence 010.
(g) Strings that contain an even number of occurrences of the substring 010.
? (h)Strings that contain an even number of occurrences of the substring 000.

(i) Strings in which every occurrence of the substring 00 appears before every occurrenceof the substring 11.
(j) Strings w such that in every prefix of w, the number of 0s and the number of 1s differ
by at most 1.
? (k)Strings w such that in every prefix of w, the number of 0s and the number of 1s differ
by at most 2.

? (l)Strings in which the number of 0s and the number of 1s differ by a multiple of 3.

? (m)
Æ

Strings that contain an even number of 1s and an odd number of 0s.

(n) Strings that represent a number divisible by 5 in binary.
10Models of Computation

Lecture 2: Regular Languages [Sp’18]

5. For any string w, define stutter(w) as follows:¨"
if w = "stutter(w) :=aa • stutter(x) if w = a x for some symbol a and string x
Let L be an arbitrary regular language.
(a) Prove that the language stutter(L) = {stutter(w) | w ∈ L} is also regular.
? (b)Prove that the language stutter−1 (L) = {w | stutter(w) ∈ L} is also regular.

6. Recall that the reversal w R of a string w is defined recursively as follows:¨"
if w = "wR =x • a if w = a x for some symbol a and some string x
The reversal L R of a language L is defined as the set of reversals of all strings in L:
L R := wR w ∈ L(a) Prove that (L ∗ )R = (L R )∗ for every language L.
(b) Prove that the reversal of any regular language is also a regular language. (You may
assume part (a) even if you haven’t proved it yet.)
You may assume the following facts without proof:• L ∗ • L ∗ = L ∗ for every language L.
• (wR )R = w for every string w.
• (x • y)R = y R • x R for all strings x and y.
[Hint: Yes, all three proofs use induction, but induction on what? And yes, all three
proofs.]7. This problem considers two special classes of regular expressions.
• A regular expression R is plus-free if and only if it never uses the + operator.
• A regular expression R is top-plus if and only if either– R is plus-free, or– R = S + T , where S and T are top-plus.
For example, 1((0∗ 10)∗ 1)∗ 0 is plus-free and (therefore) top-plus; 01∗ 0 + 10∗ 1 + " istop-plus but not plus-free, and 0(0 + 1)∗ (1 + ") is neither top-plus nor plus-free.
Recall that two regular expressions R and S are equivalent if they describe exactly thesame language: L(R) = L(S).
(a) Prove that for any top-plus regular expressions R and S, there is a top-plus regularexpression that is equivalent to RS.
(b) Prove that for any top-plus regular expression R, there is a plus-free regular expression S such that R∗ and S ∗ are equivalent.
11Models of Computation

Lecture 2: Regular Languages [Sp’18](c) Prove that for any regular expression, there is an equivalent top-plus regular expression.
You may assume the following facts without proof, for all regular expressions A, B, and C:• A(B + C) is equivalent to AB + AC.
• (A + B)C is equivalent to AC + BC.
• (A + B)∗ is equivalent to (A∗ B ∗ )∗ .
8. (a) Describe and analyze an efficient algorithm to determine, given a regular expression R,
whether L(R) = ∅.
(b) Describe and analyze an efficient algorithm to determine, given a regular expression R,
whether L(R) = {"}. [Hint: Use part (a).](c) Describe and analyze an efficient algorithm to determine, given a regular expression R,
whether L(R) is finite. [Hint: Use parts (a) and (b).]In each problem, assume you are given R as a regular expression tree, not just a raw string.

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