Models of Computation

Lecture 7: Undecidability [Fa’16]

I said in my haste, All men are liars.
— Psalms 116:11 (King James Version)
Some problems are so complex that you have to be highly intelligent
and well informed just to be undecided about them.
— Laurence Johnston Peter, Peter’s Almanac (September 24, 1982)“Proving or disproving a formula—once you’ve encrypted the formula into numbers,that is—is just a calculation on that number. So it means that the answer to the questionis, no! Some formulas cannot be proved or disproved by any mechanical process! So I
guess there’s some point in being human after all!”Alan looked pleased until Lawrence said this last thing, and then his face collapsed.
“Now there you go making unwarranted assumptions.”— Neal Stephenson, Cryptonomicon (1999)
No matter how P might perform, Q will scoop it:
Q uses P ’s output to make P look stupid.
Whatever P says, it cannot predict Q:
P is right when it’s wrong, and is false when it’s true!
— Geoffrey S. Pullum, “Scooping the Loop Sniffer” (2000)

ÆÆÆ

Rewrite in the language of algorithms instead of the language of Turing machines, using“source code” instead of “encoding” everywhere. Formulation in terms of TMs makes almost
everything much more complicated than it needs to be. (The dovetail/product construction in
the proof of Lemma 4 may be an exception.)7

Undecidability

Perhaps the single most important result in Turing’s remarkable 1936 paper that introduces Turingmachines is his observation that there are problems that cannot be solved by any algorithm.
Turing’s canonical example of an undecidable problem was the halting problem, which asks
whether a given Turing machine halts when given a particular input string. Among otherconsequences, Turing’s undecidability result provided an elegant negative solution to Hilbert’s
Entscheidungsproblem, which asked for an algorithm to decide whether a given statement of
first-order logic is true—no such algorithm exists.

7.1

Acceptable versus Decidable

Recall that there are three possible outcomes for a Turing machine M running on any particular
input string w: acceptance, rejection, and divergence. Every Turing machine M immediately
defines four different languages (over the input alphabet Σ of M ):• The accepting language Accept(M ) := {w ∈ Σ∗ | M accepts w}• The rejecting language Reject(M ) := {w ∈ Σ∗ | M rejects w}• The halting language Halt(M ) := Accept(M ) ∪ Reject(M )• The diverging language Diverge(M ) := Σ∗ \ Halt(M )For any language L, the sentence “M accepts L” means Accept(M ) = L, and the sentence “Mdecides L” means Accept(M ) = L and Diverge(M ) = ∅.
© 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 7: Undecidability [Fa’16]

Now let L be an arbitrary language. We say that L is acceptable (or semi-computable, orsemi-decidable, or recognizable, or listable, or recursively enumerable) if some Turing machine
accepts L, and unacceptable otherwise. Similarly, L is decidable (or computable, or recursive) if
some Turing machine decides L, and undecidable otherwise.

7.2

Lo, I Have Become Death, Stealer of Pie

There is a subtlety in the definitions of “acceptable” and “decidable” that many beginners miss:A language can be decidable even if we can’t exhibit a specific Turing machine decides it. As acanonical example, consider the language Π = {w | 1|w| appears in the binary expansion of π}.
Despite appearances, this language is decidable! There are only two cases to consider:• Suppose there is an integer N such that the binary expansion of π contains the substring
1N but does not contain the substring 1N +1 . Let MN be the Turing machine with N + 3
states {0, 1, . . . , N , accept, reject}, start state 0, and the following transition function:
if a = acceptδ(q, a) = rejectif a 6=  and q = n(q + 1, a, +1) otherwise
This machine correctly decides Π.
• Suppose the binary expansion of π contains arbitrarily long substrings of 1s. Then any
Turing machine that accepts all inputs correctly decides Π.
We have no idea which of these machines correctly decides Π, but one of them does, and that’s
enough!

7.3

Useful Properties

This subsection lists several simple but useful properties of (un)decidable and (un)acceptablelanguages. Almost all of these properties follow from routine definition-chasing; readers arestrongly encouraged to try to prove each lemma themselves before reading ahead.
One might reasonably ask why we don’t also define “rejectable” and “haltable” languages.
The following lemma, whose proof is an easy exercise (hint, hint), implies that these sets areboth identical to the acceptable languages.
Lemma 1. Let M be an arbitrary Turing machine.
(a) There is a Turing machine M R such that Accept(M R ) = Reject(M ) and Reject(M R ) =Accept(M ).
(b) There is a Turing machine M A such that Accept(M A) = Accept(M ) and Reject(M A) = ∅.
(c) There is a Turing machine M H such that Accept(M H ) = Halt(M ) and Reject(M H ) = ∅.
The decidable languages have several fairly obvious useful closure properties.
Lemma 2. If L and L 0 are decidable, then L ∪ L 0 , L ∩ L 0 , L \ L 0 , and L 0 \ L are also decidable.
Proof: Let M and M 0 be Turing machines that decide L and L 0 , respectively. We can build a
Turing machine M∪ that decides L ∪ L 0 as follows. First, M∪ copies its input string w onto asecond tape. Then M∪ runs M on input w (on the first tape), and then runs M 0 on input w (onthe second tape). If either M or M 0 accepts, then M∪ accepts; if both M and M 0 reject, then M∪
rejects.
The other three languages are similar.
2Models of Computation

Lecture 7: Undecidability [Fa’16]

Corollary 3. The following hold for all languages L and L 0 .
(a) If L ∩ L 0 is undecidable and L 0 is decidable, then L is undecidable.
(b) If L ∪ L 0 is undecidable and L 0 is decidable, then L is undecidable.
(c) If L \ L 0 is undecidable and L 0 is decidable, then L is undecidable.
(d) If L 0 \ L is undecidable and L 0 is decidable, then L is undecidable.
Unfortunately, acceptable languages are not quite as well-behaved as decidable languages,
thanks to the subtle distinction between rejecting a string and not accepting a string.
Lemma 4. For all acceptable languages L and L 0 , the languages L ∪ L 0 and L ∩ L 0 are also
acceptable.
Proof: Let M and M 0 be Turing machines that decide L and L 0 , respectively. We can build a
Turing machine M∩ that decides L ∩ L 0 as follows. First, M∩ copies its input string w onto asecond tape. Then M∩ runs M on input w using the first tape, and then runs M 0 on input w using
the second tape. If both M and M 0 accept, then M∩ accepts; if either M or M 0 reject, then M∩
rejects; if either M or M 0 diverge, then M∩ diverges (automatically).
The construction for L ∪ L 0 is more subtle; instead of running M and M 0 in series, we mustrun them in parallel. Like M∩ , the new machine M∪ starts by copying its input string w onto
a second tape. But then M∪ runs M and M 0 simultaneously; with each step of M∪ simulating
both one step of M on the first tape and one step of M 0 on the second. Ignoring the states andtransitions needed for initialization, the state set of M∪ is the product of the state sets of M
and M 0 , and the transition function is
if q = accept or q0 = accept0
accept∪δ∪ (q, a, q0 , a0 ) = reject∪
if q = reject and q0 = reject0(δ(q, a), δ0 (q0 , a0 )) otherwise
Thus, M∪ accepts as soon as either M or M 0 accepts, and rejects only after both M or M 0
reject.

Lemma 5. An acceptable language L is decidable if and only if Σ∗ \ L is also acceptable.
Proof: Let M and M be Turing machines that accept L and Σ∗ \ L, respectively. Following theprevious proof, we construct a new Turing machine M ∗ that copies its input onto a second tape,and then simulates M and M 0 in parallel on the two tapes. If M accepts, then M ∗ accepts; if M
accepts, then M ∗ rejects. Since every string is accepted by either M or M , we conclude that M ∗decides L.
The other direction follows immediately from Lemma 1.


7.4

Code is Data; Data is Code

Perhaps the single most important observation in developing these undecidability results—andone of the most important observations in computer science more broadly—is that Turingmachines can be encoded as strings. At one level, this observation is completely trivial: Any
written description of a Turing machine is a string, and modern code is just a sequence of bytes,
stored in a file like any other data. But this apparently trivial observation is actually incredibly
powerful.
Most natural encodings of Turing machines have three important properties.
3Models of Computation

Lecture 7: Undecidability [Fa’16]• Unique: Different Turing machines are encoded as different strings.
• Modifiable: We can algorithmically modify any Turing machine M , given the encodingof M as input. For example, there are algorithms to swap the accept and reject states of any
Turing machine, or to add new states and transitions representing pre- and post-processing
phases, or to build a new machine that calls M as a subroutine, or to build a new machinethat runs several copies of M in parallel.
• Executable: There is a fixed universal Turing machine U that can simulate the behaviorof an arbitrary Turing machine M , given the encodings of M and w as input. For example,
if we decided to encode Turing machines as Python programs, then U would be a Pythoninterpreter.
The precise details of the encoding are unimportant, but for the sake of concreteness, letme describe a natural encoding of Turing machines as strings over the six-character alphabet{0, 1, {, •, }}. Let M = (Γ , , Σ, Q, start, accept, reject, δ) be an arbitrary Turing machine, witha single half-infinite tape and a single read-write head. (I will consistently indicate the states
and tape symbols of M in slanted green to distinguish them from the upright red symbols in theencoding alphabet.)• We encode each symbol a ∈ Γ as a unique string |a| of dlg(|Γ |)e bits. For example, ifΓ = {0, 1, $, x, }, we might use the following encoding:〈0〉 = 001,〈1〉 = 010,〈$〉 = 011,〈x〉 = 100,〈〉 = 000.

• Similarly, we encode each state q ∈ Q as a distinct string 〈q〉 of dlg|Q|e bits. Without
loss of generality, we encode the start state with all 1s and the reject state with all 0s.
For example, if Q = {start, seek1, seek0, reset, verify, accept, reject}, we might use thefollowing encoding:〈start〉 = 111〈seek1〉 = 010〈seek0〉 = 011〈verify〉 = 101〈accept〉 = 110〈reject〉 = 000〈reset〉 = 100• Finally, we encode the machine M itself as the string 〈M 〉 = {〈reject〉•〈〉}〈δ〉, where 〈δ〉is the concatenation of substrings {〈p〉•〈a〉•〈q〉•〈b〉•〈∆〉} encoding each transition
δ(p, a) = (q, b, ∆) such that q 6= reject. We encode the actions ∆ = ±1 by defining〈−1〉 := 0 and 〈+1〉 := 1. Conveniently, every transition string has exactly the samelength. For example, with the symbol and state encodings described above, the transition
δ(reset, $) = (start, $, +1) would be encoded as the string{100•011•001•011•1}.
Our first example Turing machine for recognizing {0n 1n 0n | n ≥ 0} would be represented
by the following string (broken into multiple lines for readability):{000•000}{{001•001•010•011•1}{001•100•101•011•1}{010•001•010•001•1}{010•100•010•100•1}{010•010•011•100•1}{011•010•011•010•1}{011•100•011•100•1}{011•001•100•100•1}{100•001•100•001•0}{100•010•100•010•0}{100•100•100•100•0}{100•011•001•011•1}{101•100•101•011•1}{101•000•110•000•0}}4Models of Computation

Lecture 7: Undecidability [Fa’16]

Building a universal Turing machine U that uses this encoding is more a matter of careful
bookkeeping than real insight. We can encode any configuration of M on U’s work tape byencoding each cell of M ’s tape as a string {〈q〉•〈a〉} indicating that (1) the cell contains symbol a;(2) if q 6= reject, then M ’s head is located at this cell, and M is in state q; and (3) if q = reject,then M ’s head is located somewhere else. We also surround the entire tape encoding withbrackets { and }. For example, the initial configuration (start, 00110, 0) for our example TuringÎmachine would be encoded as follows.
〈start, 00110, 0〉 = {{111•001}{000•001}{000•010}{000•010}{000•001}}
Î
{z
}|
{z
}|
{z
}|
{z
}|
{z
}
|
start 0reject 0

reject 1

reject 1

reject 0

Similarly, the intermediate configuration (reset, $0x1x, 3) would be encoded as follows:Î〈reset, $$x1x, 3〉 = {{000•011}{000•011}{000•100}{010•010}{000•100}}
Î
|
{z
}|
{z
}|
{z
}|
{z
}|
{z
}
reject $reject 0

reject x

reset 1

reject x

To simulate one step of M ’s execution, we (1) find the location of the head (or reject if the headhas vanished), (2) look up the transition for the state-symbol pair at the head, and (3) update
the current cell and one of its neighbors to reflect the transition. The remaining grungy details
are left as an exercise.

7.5

Self-Haters Gonna Self-Hate

A Turing machine encoding 〈M 〉 is just a string, and any string (over the correct alphabet) canbe used as the input to a Turing machine. Thus, a suitable encoding of any Turing machine canbe used as the input to any Turing machine. In particular:
The encoding 〈M〉 of Turing machine M
can be used as input to the same Turing machine M.
Turing used this observation about self-reference to derive his first undecidable language asfollows. Let’s say that a Turing machine M is self-rejecting if it rejects its own encoding 〈M 〉.
Let SelfReject be the set of all encodings of self-rejecting Turing machines:

SelfReject := 〈M 〉 M rejects 〈M 〉Theorem 6. SelfReject is undecidable.
Proof: Suppose to the contrary that there is a Turing machine SR that decides SelfReject.
Then by definition, Accept(SR) = SelfReject and Diverge(SR) = ∅. More explicitly, for any
Turing machine M ,• SR accepts 〈M 〉 ⇐⇒ M rejects 〈M 〉, and• SR rejects 〈M 〉 ⇐⇒ M does not reject 〈M 〉.
In particular, these equivalences must hold when M is the machine SR. Thus,• SR accepts 〈SR〉 ⇐⇒ SR rejects 〈SR〉, and• SR rejects 〈SR〉 ⇐⇒ SR does not reject 〈SR〉.
In short, SR accepts 〈SR〉 if and only if SR rejects 〈SR〉, which is impossible! The only logicalconclusion is that the Turing machine SR does not exist.
5Models of Computation

7.6

Lecture 7: Undecidability [Fa’16]

Aside: Uncountable Barbers

Turing’s proof by contradiction is an avatar of the famous diagonalization argument that
uncountable sets exist, published by Georg Cantor in 1891. Indeed, SelfReject is sometimes
called “the diagonal language”. Recall that a function f : A → B is a surjection1 if f (A) = { f (a) |a ∈ A} = B.
Cantor’s Theorem. Let f : X → 2X be an arbitrary function from an arbitrary set X to its
power set. This function f is not a surjection.
Proof: Fix an arbitrary function f : X → 2X . Call an element x ∈ X happy if x ∈ f (x) and sad
if x 6∈ f (x). Let Y be the set of all sad elements of X ; that is, for every element x ∈ X , we havex ∈ Y ⇐⇒ x 6∈ f (x).
For the sake of argument, suppose f is a surjection. Then (by definition of surjection) there must
be an element y ∈ X such that f ( y) = Y . Then for every element x ∈ X , we havex ∈ f ( y) ⇐⇒ x 6∈ f (x).
In particular, the previous equivalence must hold when x = y:y ∈ f ( y) ⇐⇒ y 6∈ f ( y).
We have a contradiction! We conclude that f is not a surjection after all.



Now let X = Σ∗ , and define the function f : X → 2X as follows:¨
Accept(M ) if w = 〈M 〉 for some Turing machine M
f (w) :=∅
if w is not the encoding of a Turing machine
Cantor’s theorem immediately implies that not all languages are acceptable.
Alternatively, let X be the set of all Turing machines that halt on all inputs. For any Turingmachine M ∈ X , let f (M ) be the set of all Turing machines N ∈ X such that M accepts theencoding 〈N 〉. Then a Turing machine M is sad if it rejects its own encoding 〈M 〉; thus, Y isessentially the set SelfReject. Cantor’s argument now immediately implies that no Turingmachine decides the language SelfReject.
The core of Cantor’s diagonalization argument also appears in the “barber paradox” popularized by Bertrand Russell in the 1910s. In a certain small town, every resident has a haircut on
Haircut Day. Some residents cut their own hair; others have their hair cut by another resident of
the same town. To obtain an official barber’s license, a resident must cut the hair of all residents
who don’t cut their own hair, and no one else. Given these assumptions, we can immediately
conclude that there are no licensed barbers. After all, who would cut the barber’s hair?
To map Russell’s barber paradox back to Cantor’s theorem, let X be the set of residents, andlet f (x) be the set of residents who have their hair cut by x; then a resident is sad if they do not
cut their own hair. To prove that SelfReject is undecidable, replace “resident” with “a Turingmachine that halts on all inputs”, and replace “A cuts B’s hair” with “A accepts 〈B〉”.
1more commonly, flouting all reasonable standards of grammatical English, “an onto function”

6Models of Computation

7.7

Lecture 7: Undecidability [Fa’16]

Just Don’t Know What to Do with Myself

Similar diagonal arguments imply that three other languages are also undecidable:

SelfAccept := 〈M 〉 M accepts 〈M 〉
SelfHalt := 〈M 〉 M halts on 〈M 〉
SelfDiverge := 〈M 〉 M diverges on 〈M 〉The proofs for these three languages are not quite as direct as the proof for SelfReject; eachfictional deciding machine requires a small modification to create the contradiction.
Theorem 7. SelfAccept is undecidable.
Proof: For the sake of argument, suppose there is a Turing machine SA such that Accept(SA) =SelfAccept and Diverge(M ) = ∅. Let SAR be the Turing machine obtained from SA by
swapping its accept and reject states (as in the proof of Lemma 1). Then Reject(SAR ) =SelfAccept and Diverge(SAR ) = ∅. It follows that SAR rejects 〈SAR 〉 if and only if SAR accepts〈SAR 〉, which is impossible.

Theorem 8. SelfHalt is undecidable.
Proof: Suppose to the contrary that there is a Turing machine SH such that Accept(SH) =SelfHalt and Diverge(SH) = ∅. Let SH X be the Turing machine obtained from SH byredirecting every transition to accept to a new hanging state hang, and then redirecting everytransition to reject to accept. Then Accept(SH X ) = Σ∗ \ SelfHalt and Reject(SH X ) = ∅.
It follows that SH X accepts 〈SH X 〉 if and only if SH X does not halt on 〈SH X 〉, and we have acontradiction.

Theorem 9. SelfDiverge is unacceptable and therefore undecidable.
Proof: Suppose to the contrary that there is a Turing machine S D such that Accept(M ) =SelfDiverge. Let S DA be the Turing machine obtained from M by redirecting every transition
to reject to a new hanging state hang such that δ(hang, a) = (hang, a, +1) for every symbol a.
Then Accept(S DA) = SelfDiverge and Reject(S DA) = ∅. It follows that S DA accepts 〈S DA〉 if
and only if S DA does not halt on 〈S DA〉, which is impossible.

?

7.8

Nevertheless, Acceptable

Our undecidability argument for SelfDiverge actually implies the stronger result that SelfDiverge is unacceptable; we never assumed that the hypothetical accepting machine S D halts on
all inputs. However, we can use or modify our universal Turing machine U to accept the otherthree self-referential languages.
Theorem 10. SelfAccept is acceptable.
Proof: We describe a Turing machine SA that accepts the language SelfAccept. Given any
string w as input, SA first verifies that w is the encoding of a Turing machine. If w is not
the encoding of a Turing machine, then SA diverges. Otherwise, w = 〈M 〉 for some Turingmachine M ; in this case, SA writes the string ww = 〈M 〉〈M 〉 onto its tape and passes control tothe universal Turing machine U. U then simulates M (the machine encoded by the first half of7

Models of Computation

Lecture 7: Undecidability [Fa’16]

its input) on the string 〈M 〉 (the second half of its input).2 In particular, U accepts 〈M , M 〉 if andonly if M accepts 〈M 〉. We conclude that SR accepts 〈M 〉 if and only if M accepts 〈M 〉.

Theorem 11. SelfReject is acceptable.
Proof: Let U R be the Turing machine obtained from our universal machine U by swapping theaccept and reject states. We describe a Turing machine SR that accepts the language SelfReject
as follows. SR first verifies that its input string w is the encoding of a Turing machine anddiverges if not. Otherwise, SR writes the string ww = 〈M , M 〉 onto its tape and passes control to
the reversed universal Turing machine U R . Then U R accepts 〈M , M 〉 if and only if M rejects 〈M 〉.
We conclude that SR accepts 〈M 〉 if and only if M rejects 〈M 〉.
Finally, because SelfHalt is the union of two acceptable languages, SelfHalt is also
acceptable.

7.9

The Halting Problem via Reduction

Now consider the following related languages:3
Accept := 〈M , w〉
Reject := 〈M , w〉Halt := 〈M , w〉
Diverge := 〈M , w〉

M accepts w
M rejects w
M halts on w
M diverges on w

Deciding the language Halt is usually called the halting problem: Given a program M and an
input w to that program, does the program halt? This problem may seem trivial; why not just run
the program and see? More formally, why not just pass the input string 〈M , x〉 to our universal
Turing machine U? That strategy works perfectly if we just want to accept Halt, but we actuallywant to decide Halt; if M is not going to halt on w, we still want an answer in a finite amount oftime. Sadly, we can’t always get what we want.
Theorem 12. Halt is undecidable.
Proof: Suppose to the contrary that there is a Turing machine H that decides Halt. Then we
can use H to build another Turing machine SH that decides the language SelfHalt. Given any
string w, the machine SH first verifies that w = 〈M 〉 for some Turing machine M (rejecting ifnot), then writes the string ww = 〈M , M 〉 onto the tape, and finally passes control to H. But
SelfHalt is undecidable, so no such machine SH exists. We conclude that H does not exist
either.

Nearly identical arguments imply that the languages Accept, Reject, and Diverge areundecidable.
2To simplify the presentation, I am implicitly assuming here that 〈M 〉 = 〈〈M 〉〉. Without this assumption, we needa Turing machine that transforms an arbitrary string w ∈ Σ∗M into its encoding 〈w〉 ∈ Σ∗U ; building such a Turingmachine is straightforward.
3Many sources including Sipser and Wikipedia uses the shorter name A T M instead of Accept, but uses HALTT M
instead of Halt. I have no idea why Sipser thought four-letter names are okay, but six-letter names are not. The
subscript TM is just a reminder that these are languages of Turing machine encodings, as opposed to encodings ofDFAs or some other machine model.

8Models of Computation

Lecture 7: Undecidability [Fa’16]

Here we have our first example of an undecidability proof by reduction. Specifically, we
reduced the language SelfHalt to the language Halt. More generally, to reduce one language
X to another language Y , we assume (for the sake of argument) that there is a program PY that
decides Y , and we write another program that decides X , using PY as a black-box subroutine.
If later we discover that Y is decidable, we can immediately conclude that X is decidable.
Equivalently, if we later discover that X is undecidable, we can immediately conclude that Y is
undecidable.
To prove that a language L is undecidable,
reduce a known undecidable language to L.
Perhaps the most confusing aspect of reduction arguments is that the languages we want toprove undecidable nearly (but not quite) always involve encodings of Turing machines, while atthe same time, the programs that we build to prove them undecidable are also Turing machines.
Our proof that Halt is undecidable involved three different machines:• The hypothetical Turing machine H that decides Halt.
• The new Turing machine SH that decides SelfHalt, using H as a subroutine.
• The Turing machine M whose encoding is the input to H.
It is incredibly easy to get confused about which machines are playing each in the proof. Therefore,
it is absolutely vital that we give each machine in a reduction proof a unique and mnemonic
name, and then always refer to each machine by name. Never write, say, or even think “theTuring machine” or “the state” or “the tape” or “the input” or (gods forbid) “it”. You also mayfind it useful to think of the working programs we are trying to construct (H and SH in thisproof) as being written in a different language than the arbitrary source code that we want those
programs to analyze (〈M 〉 in this proof).

7.10

One Million Years Dungeon!

As a more complex set of examples, consider the following languages:
NeverAccept := 〈M 〉 | Accept(M ) = ∅NeverReject := 〈M 〉 | Reject(M ) = ∅NeverHalt := 〈M 〉 | Halt(M ) = ∅NeverDiverge := 〈M 〉 | Diverge(M ) = ∅Theorem 13. NeverAccept is undecidable.
Proof: Suppose to the contrary that there is a Turing machine N A that decides NeverAccept.
Then by swapping the accept and reject states, we obtain a Turing machine N AR that decides
the complementary language Σ∗ \ NeverAccept.
To reach a contradiction, we construct a Turing machine A that decides Accept as follows.
Given the encoding 〈M , w〉 of an arbitrary machine M and an arbitrary string w as input, A writes
the encoding 〈Mw 〉 of a new Turing machine Mw that ignores its input, writes w onto the tape,and then passes control to M . Finally, A passes the new encoding 〈Mw 〉 as input to N AR . The
following cartoon tries to illustrate the overall construction.
Before going any further, it may be helpful to list the various Turing machines that appear inthis construction.
9Models of Computation

Lecture 7: Undecidability [Fa’16]

x

Mw

acceptw

M
reject
hang

A
<M,w>

NAR
Build <Mw>
<Mw>

NA

acceptrejectA reduction from from Accept to NeverAccept, which proves NeverAccept undecidable.

• The hypothetical Turing machine N A that decides NeverAccept.
• The Turing machine N AR that decides Σ∗ \ NeverAccept, which we constructed bymodifying N A.
• The Turing machine A that we are building, which decides Accept using N AR as a black-box
subroutine.
• The Turing machine M , whose encoding is part of the input to A.
• The Turing machine Mw whose encoding A constructs from 〈M , w〉 and then passes to N AR
as input.
Now let M be an arbitrary Turing machine and w be an arbitrary string, and suppose we run
our new Turing machine A on the encoding 〈M , w〉. To complete the proof, we need to consider
two cases: Either M accepts w or M does not accept w.
• First, suppose M accepts w.
– Then for all strings x, the machine Mw accepts x.
– So Accept(Mw ) = Σ∗ , by the definition of Accept(Mw ).
– So 〈Mw 〉 6∈ NeverAccept, by definition of NeverAccept.
– So N A rejects 〈Mw 〉, because N A decides NeverAccept.
– So N AR accepts 〈Mw 〉, buy construction of N AR .
– We conclude that A accepts 〈M , w〉, by construction of A.
• On the other hand, suppose M does not accept w, either rejecting or diverging instead.
– Then for all strings x, the machine Mw does not accept x.
– So Accept(Mw ) = ∅, by the definition of Accept(Mw ).
– So 〈Mw 〉 ∈ NeverAccept, by definition of NeverAccept.
– So N A accepts 〈Mw 〉, because N A decides NeverAccept.
– So N AR rejects 〈Mw 〉, buy construction of N AR .
– We conclude that A rejects 〈M , w〉, by construction of A.

10

Models of Computation

Lecture 7: Undecidability [Fa’16]

In short, A decides the language Accept, which is impossible. We conclude that N A does notexist.

Again, similar arguments imply that the languages NeverReject, NeverHalt, and NeverDiverge are undecidable. In each case, the core of the argument is describing how to transformthe incoming machine-and-input encoding 〈M , w〉 into the encoding of an appropriate newTuring machine 〈Mw 〉.
Now that we know that NeverAccept and its relatives are undecidable, we can use them as
the basis of further reduction proofs. Here is a typical example:

Theorem 14. The language DivergeSame := 〈M1 〉 〈M2 〉 Diverge(M1 ) = Diverge(M2 ) is
undecidable.
Proof: Suppose for the sake of argument that there is a Turing machine DS that decides
DivergeSame. Then we can build a Turing machine N D that decides NeverDiverge as follows.
Fix a Turing machine Y that accepts Σ∗ (for example, by defining δ(start, a) = (accept, ·, ·) for
all a ∈ Γ ). Given an arbitrary Turing machine encoding 〈M 〉 as input, N D writes the string〈M 〉〈Y 〉 onto the tape and then passes control to DS. There are two cases to consider:• If DS accepts 〈M 〉〈Y 〉, then Diverge(M ) = Diverge(Y ) = ∅, so 〈M 〉 ∈ NeverDiverge.
• If DS rejects 〈M 〉〈Y 〉, then Diverge(M ) 6= Diverge(Y ) = ∅, so 〈M 〉 6∈ NeverDiverge.
In short, N D accepts 〈M 〉 if and only if 〈M 〉 ∈ NeverDiverge, which is impossible. We conclude
that DS does not exist.


7.11

Rice’s Theorem

In 1953, Henry Rice proved the following extremely powerful theorem, which essentially statesthat every interesting question about the language accepted by a Turing machine is undecidable.

ÆÆÆ

The following formulation is closer to the proof and may be (slightly) easier to use:
Rice’s Theorem. For any set L of languages, if ∅ 6∈ L and there is a Turingmachine M such that Accept(M ) ∈ L, then the language AcceptIn(L) :={〈M 〉 | Accept(M ) ∈ L} is undecidable.
The only downside of this formulation is that when ∅ ∈ L, we need to consider either the∗complementary property L = 2Σ \ L or the complementary language {〈M 〉 | Accept(M ) 6∈ L}.

Rice’s Theorem. Let L be any set of languages that satisfies the following conditions:• There is a Turing machine Y such that Accept(Y ) ∈ L.
• There is a Turing machine N such that Accept(N ) 6∈ L.
The language AcceptIn(L) := 〈M 〉 Accept(M ) ∈ L is undecidable.
Proof: Without loss of generality, suppose ∅ 6∈ L. (A symmetric argument establishes the theorem
in the opposite case ∅ ∈ L.) Fix an arbitrary Turing machine Y such that Accept(Y ) ∈ L.
Suppose to the contrary that there is a Turing machine AL that decides AcceptIn(L). To
derive a contradiction, we describe a Turing machine H that decides the halting language Halt,using AL as a black-box subroutine. Given the encoding 〈M , w〉 of an arbitrary Turing machine M
and an arbitrary string w as input, H writes the encoding 〈WTF〉 of a new Turing machine WTF
that executes the following algorithm:
11Models of Computation

Lecture 7: Undecidability [Fa’16]

WTF(x):
run M on input w (and discard the result)run Y on input x

H then passes the new encoding 〈WTF〉 to AL .
Now let M be an arbitrary Turing machine and w be an arbitrary string, and suppose we run
our new Turing machine H on the encoding 〈M , w〉. There are two cases to consider.
• Suppose M halts on input w.
– Then for all strings x, the machine WTF accepts x if and only if Y accepts x.
– So Accept(WTF) = Accept(Y ), by definition of Accept( · ).
– So Accept(WTF) ∈ L, by definition of Y .
– So AL accepts 〈WTF〉, because AL decides AcceptIn(L).
– So H accepts 〈M , w〉, by definition of H.
• Suppose M does not halt on input w.
– Then for all strings x, the machine WTF does not halt on input x, and therefore does
not accept x.
– So Accept(WTF) = ∅, by definition of Accept(WTF).
– So Accept(WTF) 6∈ L, by our assumption that ∅ 6∈ L.
– So AL rejects 〈WTF〉, because AL decides AcceptIn(L).
– So H rejects 〈M , w〉, by definition of H.
In short, H decides the language Halt, which is impossible. We conclude that AL does notexist.
The set L in the statement of Rice’s Theorem is often called a property of languages, rather
than a set, to avoid the inevitable confusion about sets of sets of finite sequences of characters. We
can also think of L as a decision problem about languages, where the languages are represented
by Turing machines that accept or decide them. Rice’s theorem states that the only propertiesof languages that are decidable are the trivial properties “Does this Turing machine accept an
acceptable language?” (Answer: Yes, by definition.) and “Does this Turing machine accept
Discover?” (Answer: No, because Discover is a credit card, not a language.)
Rice’s Theorem makes it incredibly easy to prove that language properties are undecidable;
we only need to exhibit one acceptable language that has the property and another acceptablelanguage that does not. In fact, every proof using Rice’s theorem can use at least one of thefollowing Turing machines:• MAccept accepts every string, by defining δ(start, a) = accept for every tape symbol a.
• MReject rejects every string, by defining δ(start, a) = reject for every tape symbol a.
• MDiverge diverges on every string, by defining δ(start, a) = (start, a, +1) for every tapesymbol a.
Corollary 15. Each of the following languages is undecidable.
(a) {〈M 〉 | M accepts given an empty initial tape}(b) {〈M 〉 | M accepts the string UIUC}(c) {〈M 〉 | M accepts exactly three strings}
12Models of Computation

Lecture 7: Undecidability [Fa’16](d) {〈M 〉 | M accepts all palindromes}(e) {〈M 〉 | Accept(M ) is regular}(f) {〈M 〉 | Accept(M ) is not regular}(g) {〈M 〉 | Accept(M ) is undecidable}(h) {〈M 〉 | Accept(M ) = Accept(N )}, for some arbitrary fixed Turing machine N .
Proof: In all cases, undecidability follows from Rice’s theorem.
(a) Let L be the set of all languages that contain the empty string. Then AcceptIn(L) = {〈M 〉 |M accepts given an empty initial tape}.
• Given an empty initial tape, MAccept accepts, so Accept(MAccept ) ∈ L.
• Given an empty initial tape, MDiverge does not accept, so Accept(MDiverge ) 6∈ L.
Therefore, Rice’s Theorem implies that AcceptIn(L) is undecidable.
(b) Let L be the set of all languages that contain the string UIUC.
• MAccept accepts UIUC, so Accept(MAccept ) ∈ L.
• MDiverge does not accept UIUC, so Accept(MDiverge ) 6∈ L.
Therefore, AcceptIn(L) = {〈M 〉 | M accepts the string UIUC} is undecidable by Rice’s
Theorem.
(c) There is a Turing machine that accepts the language {larry, curly, moe}. On the other
hand, MReject does not accept exactly three strings.
(d) MAccept accepts all palindromes, and MReject does not accept all palindromes.
(e) MReject accepts the regular language ∅, and there is a Turing machine M0n 1n that accepts
the non-regular language {0n 1n | n ≥ 0}.
(f) MReject accepts the regular language ∅, and there is a Turing machine M0n 1n that accepts
the non-regular language {0n 1n | n ≥ 0}.⁴(g) MReject accepts the decidable language ∅, and there is a Turing machine that accepts theundecidable language SelfReject.
(h) The Turing machine N accepts Accept(N ) by definition. For the negative Turing machine
MAccept accepts Σ∗ and the Turing machine MReject accepts ∅, so at least one of those twomachines does not accept Accept(N ).

We can also use Rice’s theorem as a component in more complex undecidability proofs, where
the target language consists of more than just a single Turing machine encoding.

Theorem 16. The language L := 〈M , w〉 M accepts wk for every integer k ≥ 0 is undecidable.
Proof: Fix an arbitrary string w, and let L be the set of all languages that contain wk for all k.
Then Accept(MAccept ) = Σ∗ ∈ L and Accept(MReject ) = ∅ 6∈ L. Thus, even if the string w is
fixed in advance, no Turing machine can decide L.
Nearly identical reduction arguments imply the following variants of Rice’s theorem. (Thenames of these theorems are not standard.)
⁴Yes, parts (e) and (f) have exactly the same proof.

13

Models of Computation

Lecture 7: Undecidability [Fa’16]

Rice’s Rejection Theorem. Let L be any set of languages that satisfies the following conditions:• There is a Turing machine Y such that Reject(Y ) ∈ L• There is a Turing machine N such that Reject(N ) 6∈ L.
The language RejectIn(L) := 〈M 〉 Reject(M ) ∈ L is undecidable.
Rice’s Halting Theorem. Let L be any set of languages that satisfies the following conditions:• There is a Turing machine Y such that Halt(Y ) ∈ L• There is a Turing machine N such that Halt(N ) 6∈ L.
The language HaltIn(L) := 〈M 〉 Halt(M ) ∈ L is undecidable.
Rice’s Divergence Theorem. Let L be any set of languages that satisfies the following conditions:• There is a Turing machine Y such that Diverge(Y ) ∈ L• There is a Turing machine N such that Diverge(N ) 6∈ L.
The language DivergeIn(L) := 〈M 〉 Diverge(M ) ∈ L is undecidable.
Rice’s Decision Theorem. Let L be any set of languages that satisfies the following conditions:• There is a Turing machine Y that decides an language in L.
• There is a Turing machine N that decides an language not in L.

The language DecideIn(L) := 〈M 〉 M decides a language in L is undecidable.
As easy as it is to use Rice’s theorem and its variants, they cannot be used for all undecidability proofs; these theorems only apply to properties of languages. For example, thelanguage ThisIsSparta := {〈M 〉 | M accepts the string SPARTA after exactly 300 steps} is decidable, even though there are Turing machines that accept the string SPARTA after exactly 300
steps and there are other Turing machines that do not.
More subtly, Rice’s theorem cannot be applied to self-referential languages like RevAccept :={〈M 〉 | M accepts 〈M 〉R }, because membership depends on details of the encoded machine andnot just the language that the encoded machine accepts. To be clear: RevAccept is undecidable;
you just can’t use Rice’s theorem to prove that fact.
?

7.12

The Rice-McNaughton-Myhill-Shapiro Theorem

The following subtle generalization of Rice’s theorem precisely characterizes which propertiesof acceptable languages are acceptable. This result was partially proved by Henry Rice in 1953,
in the same paper that proved Rice’s Theorem; Robert McNaughton, John Myhill, and Norman
Shapiro completed the proof a few years later, each independently from the other two.⁵
The Rice-McNaughton-Myhill-Shapiro Theorem. Let L be an arbitrary set of acceptable languages. The language AcceptIn(L) := {〈M 〉 | Accept(M ) ∈ L} is acceptable if and only if L
satisfies the following conditions:(a) L is monotone: For any language L ∈ L, every superset of L is also in L.
(b) L is compact: Every language in L has a finite subset that is also in L.
⁵McNaughton never published his proof (although he did announce the result); consequently, this theorem issometimes called “The Rice-Myhill-Shapiro Theorem”. Even more confusingly, Myhill published his proof twice, oncein a paper with John Shepherdson and again in a later paper with Jacob Dekker. So maybe it should be called theRice–Dekker-Myhill–(McNaughton–)Myhill-Shepherdson–Shapiro Theorem.

14

Models of Computation

Lecture 7: Undecidability [Fa’16](c) L is finitely acceptable: The language 〈L〉 | L ∈ L and L is finite is acceptable.⁶
I won’t give a complete proof of this theorem (in part because it requires techniques I haven’t
introduced), but the following lemma is arguably the most interesting component:
Lemma 17. Let L be a set of acceptable languages. If L is not monotone, then AcceptIn(L) is
unacceptable.
Proof: Suppose to the contrary that there is a Turing machine AIL that accepts AcceptIn(L).
Using this Turing machine as a black box, we describe a Turing machine S D that accepts theunacceptable language SelfDiverge. Fix two Turing machines Y and N such that
Accept(Y ) ∈ L,
Accept(N ) 6∈ L,
and Accept(Y ) ⊆ Accept(N ).
Let w be the input to S D. After verifying that w = 〈M 〉 for some Turing machine M(and rejecting otherwise), S D writes the encoding 〈WTF〉 or a new Turing machine WTF that
implements the following algorithm:
WTF(x):
write x to second tapewrite 〈M 〉 to third tapein parallel:run Y on the first taperun N on the second taperun M on the third tape
if Y accepts x
acceptif N accepts x and M halts on 〈M 〉acceptFinally, S D passes the new encoding 〈WTF〉 to AIL . There are two cases to consider:• If M halts on 〈M 〉, then Accept(WTF) = Accept(N ) 6∈ L, and therefore AIL does not
accept 〈WTF〉.
• If M does not halt on 〈M 〉, then Accept(WTF) = Accept(Y ) ∈ L, and therefore AIL
accepts 〈WTF〉.
In short, S D accepts SelfDiverge, which is impossible. We conclude that S D does not exist. 
Corollary 18. Each of the following languages is unacceptable.
(a) {〈M 〉 | Accept(M ) is finite}(b) {〈M 〉 | Accept(M ) is infinite}(c) {〈M 〉 | Accept(M ) is regular}(d) {〈M 〉 | Accept(M ) is not regular}(e) {〈M 〉 | Accept(M ) is decidable}
⁶Here the encoding 〈L〉 of a finite language L ⊆ Σ∗ is exactly the string that you would write down to explicitly
describe L. Formally, 〈L〉 is the unique string over the alphabet Σ ∪ {{, •, }, 3} that contains the strings in L in
lexicographic order, separated by commas • and surrounded by braces {}, with 3 representing the empty string. For
example, {", 0, 01, 0110, 01101001} = {3•0•01•0110•01101001}.

15

Models of Computation

Lecture 7: Undecidability [Fa’16](f) {〈M 〉 | Accept(M ) is undecidable}(g) {〈M 〉 | M accepts at least one string in SelfDiverge}(h) {〈M 〉 | Accept(M ) = Accept(N )}, for some arbitrary fixed Turing machine N .
Proof: (a) The set of finite languages is not monotone: ∅ is finite; Σ∗ is not finite; both ∅and Σ∗ are acceptable (in fact decidable); and ∅ ⊂ Σ∗ .
(b) The set of infinite acceptable languages is not compact: No finite subset of the infinite
acceptable language Σ∗ is infinite!
(c) The set of regular languages is not monotone: Consider the languages ∅ and {0n 1n | n ≥ 0}.
(d) The set of non-regular acceptable languages is not monotone: Consider the languages{0n 1n | n ≥ 0} and Σ∗ .
(e) The set of decidable languages is not monotone: Consider the languages ∅ and SelfReject.
(f) The set of undecidable acceptable languages is not monotone: Consider the languages
SelfReject and Σ∗ .
(g) The set L = {L | L ∩ SelfDiverge 6= ∅} is not finitely acceptable. For any string w, deciding
whether {w} ∈ L is equivalent to deciding whether w ∈ SelfDiverge, which is impossible.
(h) If Accept(N ) 6= Σ∗ , then the set {Accept(N )} is not monotone. On the other hand, if
Accept(N ) = Σ∗ , then the set {Accept(N )} is not compact: No finite subset of Σ∗ is equalto Σ∗ !


7.13

Turing Machine Behavior: It’s ComplicatedRice’s theorems imply that every interesting question about the language that a Turing machine
accepts—or more generally, the function that a program computes—is undecidable. A more subtle
question is whether we can recognize Turing machines that exhibit certain internal behavior.
Some behaviors we can recognize; others we can’t.
Theorem 19. The language NeverLeft := {〈M , w〉 | Given w as input, M never moves left} isdecidable.
Proof: Given the encoding 〈M , w〉, we simulate M with input w using our universal Turingmachine U, but with the following termination conditions. If M ever moves its head to the left,then we reject. If M halts without moving its head to the left, then we accept. Finally, if M reads
more than |Q| blanks, where Q is the state set of M , then we accept. If the first two cases do not
apply, M only moves to the right; moreover, after reading the entire input string, M only reads
blanks. Thus, after reading |Q| blanks, it must repeat some state, and therefore loop forever
without moving to the left. The three cases are exhaustive.

Theorem 20. The language LeftThree := {〈M , w〉 | Given w as input, M eventually moves left
three times in a row} is undecidable.
Proof: Given 〈M 〉, we build a new Turing machine M 0 that accepts the same language as M andmoves left three times in a row if and only if it accepts, as follows. For each non-accepting state p

16Models of Computation

Lecture 7: Undecidability [Fa’16]of M , the new machine M 0 has three states p1 , p2 , p3 , with the following transitions:
δ0 (p1 , a) = (q2 , b, ∆),where (q, b, ∆) = δ(p, a) and q 6= accept0δ (p2 , a) = (p3 , a, +1)
δ0 (p3 , a) = (p1 , a, −1)
In other words, after each non-accepting transition, M 0 moves once to the right and then once tothe left. For each transition to accept, M 0 has a sequence of seven transitions: three steps to theright, then three steps to the left, and then finally accept0 , all without modifying the tape. (Thethree steps to the right ensure that M 0 does not fall off the left end of the tape.)
Finally, M 0 moves left three times in a row if and only if M accepts w. Thus, if we could
decide LeftThree, we could also decide Accept, which is impossible.

There is no hard and fast rule like Rice’s theorem to distinguish decidable behaviors fromundecidable behaviors, but I can offer two rules of thumb.
• If it is possible to simulate an arbitrary Turing machine while avoiding the target behavior,
then the behavior is not decidable. For example, there is no algorithm to determine whether
a given Turing machine reenters its start state, or revisits the left end of the tape, or writes
a blank.
• If a Turing machine with the target behavior is limited to a finite number of configurations,or is guaranteed to force an infinite loop after a finite number of transitions, then the
behavior is likely to be decidable. For example, there are algorithms to determine whether
a given Turing machine ever leaves its start state, or reads its entire input string, or writes
a non-blank symbol over a blank.

Exercises
1. Let M be an arbitrary Turing machine.
(a) Describe a Turing machine M R such that
Accept(M R ) = Reject(M ) and Reject(M R ) = Accept(M ).
(b) Describe a Turing machine M A such that
Accept(M A) = Accept(M ) and Reject(M A) = ∅.
(c) Describe a Turing machine M H such that
Accept(M H ) = Halt(M ) and
2. (a) Prove that Accept is undecidable.
(b) Prove that Reject is undecidable.
(c) Prove that Diverge is undecidable.
3. (a) Prove that NeverReject is undecidable.
17

Reject(M H ) = ∅.

Models of Computation

Lecture 7: Undecidability [Fa’16](b) Prove that NeverHalt is undecidable.
(c) Prove that NeverDiverge is undecidable.
4. Prove that each of the following languages is undecidable.
(a) AlwaysAccept := {〈M 〉 | Accept(M ) = Σ∗ }(b) AlwaysReject := {〈M 〉 | Reject(M ) = Σ∗ }(c) AlwaysHalt := {〈M 〉 | Halt(M ) = Σ∗ }(d) AlwaysDiverge := {〈M 〉 | Diverge(M ) = Σ∗ }5. Let L be a non-empty proper subset of the set of acceptable languages. Prove that thefollowing languages are undecidable:(a) RejectIn(L) := 〈M 〉 Reject(M ) ∈ L(b) HaltIn(L) := 〈M 〉 Halt(M ) ∈ L(c) DivergeIn(L) := 〈M 〉 Diverge(M ) ∈ L
6. For each of the following decision problems, either sketch an algorithm or prove that the
problem is undecidable. Recall that wR denotes the reversal of string w. For each problem,
the input is the encoding 〈M 〉 of a Turing machine M .
(a) Does M reject the empty string?
(b) Does M accept 〈M 〉R ?
(c) Does M accept 〈M 〉〈M 〉?
(d) Does M accept 〈M 〉k for any integer k?
(e) Does M accept the encoding of any Turing machine?
(f) Is there a Turing machine that accepts 〈M 〉?
(g) Is 〈M 〉 a palindrome?
(h) Does M reject any palindrome?
(i) Does M accept all palindromes?
(j) Does M diverge only on palindromes?
(k) Is there an input string that forces M to move left?
(l) Is there an input string that forces M to move left three times in a row?
(m) Does M accept the encoding of any Turing machine N such that Accept(N ) =SelfDiverge?
7. For each of the following decision problems, either sketch an algorithm or prove that the
problem is undecidable. Recall that wR denotes the reversal of string w. For each problem,
the input is an encoding 〈M , w〉 of a Turing machine M and its input string w.
(a) Does M accept the string wwR ?

18

Models of Computation

Lecture 7: Undecidability [Fa’16](b) Does M accept either w or wR ?
(c) Does M either accept w or reject wR ?
(d) Does M accept the string wk for some integer k?
(e) Does M accept w in at most 2|w| steps?
(f) If we run M on input w, does M ever change a symbol on its tape?
(g) If we run M on input w, does M ever move to the right?
(h) If we run M on input w, does M ever move to the right twice in a row?
(i) If we run M on input w, does M move its head to the right more than 2|w| times (notnecessarily consecutively)?
(j) If we run M with input w, does M ever change a  on the tape to any other symbol?
(k) If we run M with input w, does M ever change a  on the tape to 1?
(l) If we run M with input w, does M ever write a ?
(m) If we run M with input w, does M ever leave its start state?
(n) If we run M with input w, does M ever reenter its start state?
(o) If we run M with input w, does M ever reenter a state that it previously left? That is,
are there states p 6= q such that M moves from state p to state q and then later moves
back to state p?
8. Let M be a Turing machine, let w be an arbitrary input string, and let s and t be positive
integers integer. We say that M accepts w in space s if M accepts w after accessing at
most the first s cells on the tape, and M accepts w in time t if M accepts w after at most ttransitions.
(a) Prove that the following languages are decidable:
i. 〈M , w〉 M accepts w in time |w|2
ii. 〈M , w〉 M accepts w in space |w|2(b) Prove that the following languages are undecidable:
i. 〈M 〉 M accepts at least one string w in time |w|2
ii. 〈M 〉 M accepts at least one string w in space |w|2
9. Let L0 be an arbitrary language. For any integer i > 0, define the languageL i := 〈M 〉 M decides L i−1 .
For which integers i > 0 is L i decidable? Obviously the answer depends on the initial
language L0 ; give a complete characterization of all possible cases. Prove your answer iscorrect. [Hint: This question is a lot easier than it looks!]
10. Argue that each of the following decision problems about programs in your favorite
programming language are undecidable.
(a) Does this program correctly compute Fibonacci numbers?

19Models of Computation

Lecture 7: Undecidability [Fa’16](b) Can this program fall into an infinite loop?
(c) Will the value of this variable ever change?
(d) Will this program every attempt to deference a null pointer?
(e) Does this program free every block of memory that it dynamically allocates?
(f) Is any statement in this program unreachable?
(g) Do these two programs compute the same function?
? 11.

Call a Turing machine conservative if it never writes over its input string. More formally, aTuring machine is conservative if for every transition δ(p, a) = (q, b, ∆) where a ∈ Σ, wehave b = a; and for every transition δ(p, a) = (q, b, ∆) where a 6∈ Σ, we have b 6= Σ.
(a) Prove that if M is a conservative Turing machine, then Accept(M ) is a regular
language.
(b) Prove that the language {〈M 〉 | M is conservative and M accepts "} is undecidable.
Together, these two results imply that every conservative Turing machine accepts the samelanguage as some DFA, but it is impossible to determine which DFA.

Æ

12. (a) Prove that it is undecidable whether a given C++ program is syntactically correct.
[Hint: Use templates!](b) Prove that it is undecidable whether a given ANSI C program is syntactically correct.
[Hint: Use the preprocessor!](c) Prove that it is undecidable whether a given Perl program is syntactically correct.
[Hint: Does that slash character / delimit a regular expression or represent division?]© 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.

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