remaining topics
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Remaining Topics. Decidability Concept 4.1 The Halting Problem 4.2 P vs. NP 7.2 and 7.3 NP-completeness & Cook-Levin Theorem 7.4. Review: Turing Machines in a nutshell. Church-Turing Thesis Turing Machine equal Notion of an Algorithm Turing Machine - PowerPoint PPT PresentationTRANSCRIPT
Remaining Topics Decidability Concept 4.1 The Halting Problem 4.2 P vs. NP 7.2 and 7.3 NP-completeness &
Cook-Levin Theorem 7.4
Review: Turing Machines in a nutshell
Church-Turing Thesis Turing Machine equal Notion of an Algorithm
Turing Machine Most simple machine possible Computational power of
modern computer and high-level language
Not particularly efficient in a practical sense
Review: Turing Machines in a nutshell
Most simply model possible… Adding another tape can improve
efficiency (time or computational “speed”) but not computational “power”
(ability to solve a problem). Every multi-tape TM has an equivalent single-tape
TM (Theorem 3.13, p.149) Similar to an Automata
Non-determinism does NOT add any“computational power” (Theorem 3.15, p.150)
Review: Turing Machines in a nutshell
Computational power of modern computer and high-level language
Every operation and statement in a high level language can be implemented with a Turing Machine (TM)
Just as statements can be combinedSo can TMs (HW5 illustrates this)
Decidability 4.1 Is there an algorithm that can decide if
An item is in a set. A string is in a language A formula is a member of a theory
These are all variations of the same concept, i.e., the concept of decidability
Decidability in Languages We will concentrate on this:
Algorithms for deciding if a string is in a language
But, the strings and languages are going to represent deeper problems
ADFA = {<B,w> | B is a DFA that accepts input string w}
ADFA
ADFA = {<B,w> | B is a DFA that accepts input string w}
B is the encoding of a DFA Remember that you can encode a DFA as
follows: B = (Q,Σ, δ, qstart, F)
We are literally encoding the machine and the input (w) as a string “<({1,2,3},{a,b},{(1,2,a),(1,3,b)},1,{3}),abc>”
ADFA
ADFA = {<B,w> | B is a DFA that accepts input string w}
Testing whether a DFA accepts an input w is the same as the problem of testing whether the string <B,w> is a member of the language ADFA Just as A = {w | w = (11)*} would accept the set {ε,
11, 1111, 111111, …} ADFA would enumerate all the <B,w>’s such that w is
accepted by the encoded B.
ADFA is decidableADFA =
{<B,w> | B is a DFA that accepts input string w}
What does this mean in plain English?
How can we prove it?
ADFA is decidableADFA =
{<B,w> | B is a DFA that accepts input string w}
What does this mean in plain English?
“An algorithm exists that can accept strings that adhere to the definition of ADFA and reject string that don’t”
ADFA is decidableADFA =
{<B,w> | B is a DFA that accepts input string w}
How can we prove it? Proof is on p.167
ANFA is decidableANFA =
{<B,w> | B is a NFA that accepts input string w}
How do we know this to be true? Hint: How are NFAs and DFAs
different?
AREX is decidableAREX =
{<R,w> | R is a Regular Expression that generates the string w}
How do we know this to be true? Hint: How are Regular Expressions
and DFAs related?
EDFA is decidableEDFA =
{<A> | A is a DFA and L(A) is empty} Prove it Hint: Just as Turing Machine can
“simulate” a DFA it can also determine if a state is unreachable.
EQDFA is decidableEQDFA =
{<A,B> | A and B are DFAs and L(A) = L(B)}
How do we know this to be true? Hint: Symmetric Difference formula
Decidability of Regular Languages Deciding if
a language is Regular or not If given DFA, NFA or REX
a Regular language is empty two Regular languages are equal
Decidability of Context Free Deciding if
a language is Context Free or not (Theorem 4.7) If given CFG
a Context Free language is empty (Theorem 4.8)
two Context Free languages are equal
Classes of languages
Turing-recognized
Decidable
Context-Free (ACFG)
Regular (ADFA)
The Halting Problem Will an algorithm halt on a given input. Intuition:
Can you ever be sure that a loop is infinite? It might just terminate in a few minutes,
hours, years, millenniums, etc. Sometimes you can make such a
determination: while (x > 0) {x=1;} But is it always possible to make such a
determination?
Infinite Looping DFA:
by definition, upon consuming the input, the machine rejects unless it is in an accept state.
Looping is simply not an option by definition. PDA:
very, very hard to make deterministic PDA’, but it can be done. Once the input is consumed, empty transitions can move to a
reject/accept state. Every CF language has a PDA that will halt (not loop).
TM: Just like a high-level language TMs can loop forever. Intuition: you don’t consume the input, you can move on the
tape infinitely, and the states can have a loop with no accept or reject.
ATM
ATM= {<M,w> | M is a TM and M accepts w} U = “on input <M,w> simulate M on w”
If M accepts, U accepts If M rejects, U rejects
Simple intuition: M could be a Turing Machine that loops
forever on certain input. If M loops forever, U cannot be a decider for
ATM
Is ATM decidable? ATM= {<M,w> | M is a TM and M accepts
w} U = “on input <M,w> simulate M on w”
If M accepts, U accepts If M rejects, U rejects
BUT! Perhaps there is a way to implement M such that we can detect the infinite loop?
Upon infinite loop detection, U rejects. U could still be a decider for ATM
The Halting Problem
H(<M,w>) = if M accepts w acceptif M rejects w reject
The Halting Problem is Undecidable Proof: First, consider the machine/algorithm D:
D = “on input <M>, where M is a TM: Run H on input <M,<M>> Output the opposite of what H outputs; that is; if
H accepts, reject and if H rejects, accept.”
Recall H: H(<M,w>) = if M accepts w accept
if M rejects w reject
D is a crazy Decider Algorithm D is implemented with a Turing Machine D(<M>) = if D does not accept <M>, accept
if D accepts <M>, reject
What happens if we run D with its own Turing Machine description?
D(<D>) = if D does not accept <D>, acceptif D accepts <D>, reject
A paradox emerges D(<D>) = if D does not accept <D>, accept
if D accepts <D>, reject If D accepts, how can D(<D>) reject?
We assumed that H could decide ATM because it could ‘somehow detect an infinite loop” Think of H as a deterministic decider if a Turing Machine
loops Then, we use H to build D (the crazy decider)
Here we assume H can stop D from looping infinitely Then, we run D on its own encoding, which creates a
paradox.
Paradox resolved Either H or D cannot exist. Which one? D is a TM machine that can simulate another
Turing Machine, which has been elegantly proven.
Intuition: Consider a program that can take another program and simulate its execution. Program, Algorithm, and Turing Machine are all
synonymous (Church-Turing Thesis) Compilers Virtual Machines
Significance of Turing Machines Turing Machines are the “tool” we
used to prove that the Halting Problem is un-decidable. In other words, no algorithm exists to
determine if a general algorithm will halt or not.
Note: There are some algorithms where its easy to show/prove that it will halt, but we are interested in the general case (any/all algorithms).
Un-decidable Languages… …there are many, but this is the interesting one:
ATM= {<M,w> | M is a TM and M accepts w}
Obviously, this language can’t be generated by a REX or CFG. So, a NFA, DFA, and PDA can’t be used as a decider to
accept/reject strings But, even a Turing machine cannot act as a decider.
It may be able to decide some input on some machines, but not all.
There are strings in ATM that will cause the decider to loop infinitely. Specifically <D,<D>> and likely other strings.
Significance of ATM
A formal language that cannot be decided by Turing Machine. We can define this language’s concept But we cannot create an algorithm (TM) to
determine if a string is in this language or not. ATM
Turing-DecidableContext-Free (ACFG)
Regular (ADFA)
Decidable vs. Recognizable
Turing Decidable Languages
Language such that some TM will accept all of its strings
And, reject strings in the language’s compliment
Halts on all input
Turing Recognizable Languages
Language such that some TM will accept all of its strings
But, might not halt on strings in the language’s compliment
Its it looping infinitely or will it accept? We don’t know.
ATM is Turing Recognizable ATM= {<M,w> | M is a TM and M accepts w}
U = “on input <M,w> simulate M on w” If M accepts, U accepts If M rejects, U rejects
U will always halt if M halts. If M doesn’t halt on w than M doesn’t accept w, so <M,w> isn’t in the language. By its very definition U will always halt on strings in ATM. The un-decidability is when U has been looping for 10 million
years, we really don’t know
Is it eventually going to be an accepted w or an infinite loop caused by a rejected w. This is why infinity is
trouble.
Time Complexity TM’s are a formal way to describe
algorithms Some problems don’t have algorithms
that will always halt, i.e., determining if a string is in ATM.
Algorithms that do halt can still take a long time.
How long is long?
General Time Unit With Turing Machines we can define a
unit of time to be the execution time of one TM transition.
With more practical machines, a time unit could be a CPU clock cycle, which might execute one machine-level instruction.
Some machines can execute 1 billion instructions per second, so the time unit would be 1/100000000 seconds.
Time as a function of input size N is the size of the input f(N) is the number of time unit to solve
the problem. The running time of algorithms can be
expressed as functions: f(N) = 2N + 5;
Two loops of size N and 5 setup instructions Or, on loop of size N with two instruction
inside and 5 instructions outside the loop.
Constants don’t matter For really big problems, constants don’t
matter f(N) = 2N is the same as g(N) = 100N
While 100 days seems like forever compared to 2 days, parallel computation and faster computers can eventually make up the difference (we hope).
For big problems, f(N) = N4 is much different than g(N) = N2
A faster computer may not help, why?
1.15 Days vs. 11 billion years
N N^2 days N^4 years1 1 1.15741E-14 1 3.17E-172 4 4.62963E-14 16 5.07E-163 9 1.04167E-13 81 2.57E-154 16 1.85185E-13 256 8.12E-15
10 100 1.15741E-12 10000 3.17E-1320 400 4.62963E-12 160000 5.07E-1250 2500 2.89352E-11 6250000 1.98E-10
1000 1000000 1.15741E-08 1E+12 3.17E-0510000 100000000 1.15741E-06 1E+16 3.17E-01
100000 10000000000 0.000115741 1E+20 3.17E+031000000 1E+12 0.011574074 1E+24 3.17E+07
10000000 1E+14 1.157407407 1E+28 3.17E+11
Big-O Review Constants don’t matter Only the leading exponent matters Why?
1.157407 days vs. 1.157419 days
N N^2 days N^2 + 100N days1 1 1.15741E-14 101 1.16898E-122 4 4.62963E-14 204 2.36111E-123 9 1.04167E-13 309 3.57639E-124 16 1.85185E-13 416 4.81481E-12
10 100 1.15741E-12 1100 1.27315E-1120 400 4.62963E-12 2400 2.77778E-1150 2500 2.89352E-11 7500 8.68056E-11
1000 1000000 1.15741E-08 1100000 1.27315E-0810000 100000000 1.15741E-06 101000000 1.16898E-06
100000 10000000000 0.000115741 10010000000 0.0001158561000000 1E+12 0.011574074 1.0001E+12 0.011575231
10000000 1E+14 1.157407407 1.00001E+14 1.157418981
Why do we only care about big N’s
Same reason I would worry about a $10,000 bill in my wallet but not a penny.
Same reason I would worry about a trip to Mars but not a trip to Menands.
Real Algorithm TM Decider
Prepare for “hand-waving magic:” Any algorithm that can be programmed
can be reduced into a language problem. A = {<p,i,o> | p is the
encoding/description of a problem, i is the input, and o is the correct output.}
Deciding if a string is in L is the same thing as solving the problem.
The TM that decides A solves problem p.
The class P The class of languages that can be
decided in polynomial time. Corresponds, the set of problems that
can be solved in polynomial time. Polynomial is O(nk) What are some problem in P that you
have studied?
Did you know? Every context free language is in P
The class NP Non-deterministically Polynomial. One way to think of this is NOT
Polynomial. Or, exponential Or N! But that is not the whole story.