84
Prof. Busch - LSU 1 Reductions

Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Embed Size (px)

Citation preview

Page 1: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 1

Reductions

Page 2: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 2

Problem is reduced to problemX Y

If we can solve problem then we can solve problemX

Y

Page 3: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 3

Language is reduced tolanguage

There is a computable function (reduction) such that:f

BwfAw )(

A

B

Definition: A B

w )(wf

Page 4: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 4

Computable function : f

which for any string computes )(wfwThere is a deterministic Turing machineM

Recall:

Page 5: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 5

If: a: Language is reduced to b: Language is decidableThen: is decidable

Theorem:

Proof:

A BB

A

Basic idea:Build the decider for using the decider for

A

B

Page 6: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 6

Decider for B

Decider for A

compute

)(wf

)(wfw

accept

reject

accept

reject

(halt)

(halt)(halt)

(halt)

Inputstring

BwfAw )(

END OF PROOF

Reduction YES YES

NO NO

Page 7: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 7

Example:

}languages same the accept that

DFAs are and :,{ 2121 MMMMEQUALDFA

} language empty the

accepts that DFA a is :{

MMEMPTYDFA

is reduced to:

Page 8: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 8

Turing Machinefor reduction

DFADFA EMPTYMEQUALMM 21,

f21,MM M

MMf

21,

DFA

We only need to construct:

Page 9: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 9

21,MM M

MMf

21,

Let be the language of DFA Let be the language of DFA

1L

2L1M

2M

)()()( 2121 LLLLML

construct DFA by combining and so that:

M

DFA

1M 2M

Turing Machinefor reduction f

Page 10: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 10

)(21 MLLL

)()()( 2121 LLLLML

DFADFA EMPTYMEQUALMM 21,

Page 11: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 11

Decider

Decider for

compute M

Inputstring

DFAEQUAL

21,MM 21,MMf DFAEMPTY

YESYES

NONO

Reduction

Page 12: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 12

If: a: Language is reduced to b: Language is undecidableThen: is undecidable

Theorem (version 1):

A B

BA

(this is the negation of the previous theorem)

Proof:

Using the decider for build the decider forA

BSuppose is decidable B

Contradiction!

Page 13: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 13

Decider for B

Decider for A

compute

)(wf

)(wfw

accept

reject

accept

reject

(halt)

(halt)(halt)

(halt)

Inputstring

BwfAw )(

Reduction

END OF PROOF

If is decidable then we can build:B

CONTRADICTION!

YES YES

NO NO

Page 14: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 14

Observation:

In order to prove that some language is undecidablewe only need to reduce a known undecidable language to

B

BA

Page 15: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 15

State-entry problem

Input: M•Turing Machine•State q

Question: Does M

•Stringw

enter state qwhile processing input string ?w

Corresponding language:

} string input on state enters

that machine Turing a is :,,{

wq

MqwMSTATE TM

Page 16: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 16

Theorem:

(state-entry problem is unsolvable)

Proof: Reduce (halting problem) to (state-entry problem)

TMSTATE is undecidable

TMHALT

TMSTATE

Page 17: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 17

Decider for

YES

NO

wM,

state-entry problem decider

TMSTATEDeciderCompute

Reduction

wMf ,

wqM ,,ˆ

TMHALT

YES

NO

Given the reduction,if is decidable,then is decidable

TMSTATE

TMHALT

A contradiction!sinceis undecidable

Halting Problem Decider

TMHALT

Page 18: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 18

wM,Compute

Reduction

wMf ,wqM ,,ˆ

We only need to build the reduction:

TMHALTwM , TMSTATEqwM ,,ˆ

wMf ,

So that:

Page 19: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 19

Mqhalting

states

specialhalt state

Rxx ,

Construct from :M̂ M

A transition for every unused tape symbol of x

iq

iq

Page 20: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 20

M̂ halts on state qM halts

Mqhalting

states

specialhalt state

iq

Page 21: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 21

M̂ halts on state on inputq

M halts on input w

w

Therefore:

Equivalently:

END OF PROOF

TMHALTwM , TMSTATEqwM ,,ˆ

Page 22: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 22

Blank-tape halting problem

Input: MTuring Machine

Question: Does M halt when started with

a blank tape?

Corresponding language:

}tape blank on started when halts

that machin aTuring is :{ eMMBLANKTM

Page 23: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 23

Theorem:

(blank-tape halting problem is unsolvable)

Proof: Reduce (halting problem) to (blank-tape problem)

TMBLANK is undecidable

TMHALT

TMBLANK

Page 24: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 24

Decider for

YES

NO

wM,

blank-tape problem decider

DeciderCompute

Reduction

wMf ,

TMHALT

YES

NO

Given the reduction,If is decidable,then is decidableTMHALT

A contradiction!sinceis undecidable

Halting Problem Decider

TMHALT

TMBLANK

TMBLANK

Page 25: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 25

wM,Compute

Reduction

wMf ,M̂

We only need to build the reduction:

TMHALTwM , TMBLANKM ˆ

wMf ,

So that:

Page 26: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 26

no

yes

Write on tape w

Tape is blank?

Run

with input

Construct from :M̂ wM,

If halts then halt

M

w

M

Accept and halt

Page 27: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 27

M̂ halts when started on blank tape

M halts on input

no

yesM

Write on tape w

Tape is blank?

Run

with inputw

w

Accept and halt

Page 28: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 28

END OF PROOF

M̂ halts when started on blank tape

M halts on inputw

TMHALTwM , TMBLANKM ˆ

Equivalently:

Page 29: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 29

If: a: Language is reduced to b: Language is undecidableThen: is undecidable

Theorem (version 2):

A B

BA

Proof:

Using the decider for build the decider for A

B

Suppose is decidable B

Contradiction!

Then is decidableB

Page 30: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 30

Suppose is decidableB

Decider for B

s

accept

reject

(halt)

(halt)

Page 31: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 31

Suppose is decidableB

Decider for B

s

accept

reject

(halt)

(halt)

Then is decidableB(we have proven this in previous class)

reject

accept(halt)

(halt)

Decider for BNO YES

YES NO

Page 32: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 32

Decider for B

Decider for A

compute

)(wf

)(wfw

accept

reject

accept

reject

(halt)

(halt)(halt)

(halt)

Inputstring

BwfAw )(

Reduction

If is decidable then we can build:B

CONTRADICTION!

YES YES

NO NO

Page 33: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 33

Decider for B

Decider for A

compute

)(wf

)(wfw

accept

reject accept

reject

(halt)

(halt)(halt)

(halt)

Inputstring

BwfAw )(

Reduction

END OF PROOFCONTRADICTION!

Alternatively:

NO YES

YES NO

Page 34: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 34

Observation:

In order to prove that some language is undecidablewe only need to reduce some known undecidable languagetoor to B

(theorem version 1)

(theorem version 2)

B

AB

Page 35: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 35

Undecidable Problems for Turing Recognizable languages

• is empty?L

L• is regular?

L• has size 2?

Let be a Turing-acceptable language L

All these are undecidable problems

Page 36: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 36

• is empty?L

L• is regular?

L• has size 2?

Let be a Turing-acceptable language L

Page 37: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 37

Empty language problem

Input: MTuring Machine

Question: Is )(ML empty?

Corresponding language:

} language empty the accepts

that machine aTuring is :{

MMEMPTYTM

?)( ML

Page 38: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 38

Theorem:

(empty-language problem is unsolvable)

is undecidable

Proof: Reduce (membership problem) to

(empty language problem)

TMA

TMEMPTY

TMEMPTY

Page 39: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 39

Decider for

YES

NO

wM,

empty problem decider

DeciderCompute

Reduction

wMf ,

M̂YES

NO

Given the reduction,if is decidable,then is decidable

membership problem decider

TMA

TMATMEMPTY

TMEMPTY

A contradiction!sinceis undecidable

TMA

Page 40: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 40

wM,Compute

Reduction

wMf ,M̂

We only need to build the reduction:

TMATwM , TMEMPTYM ˆ

wMf ,

So that:

Page 41: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 41

•Write on tape, and•Simulate on input

wM w M w

sTape of M̂

input string

accepts ?

Louisiana?s

Construct from :M̂ wM,

yes

Turing Machine

Accepts

yes

Page 42: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 42

The only possible accepted string

Louisiana

Prof. Busch - LSU 42

s

Prof. Busch - LSU 42

•Write on tape, and•Simulate on input

wM w M waccepts

?

Louisiana?s

yes

Turing MachineM̂

Accepts

yes

Page 43: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 43

accepts }Louisiana{)ˆ(MLM w

does notaccept

M w )ˆ(ML

Prof. Busch - LSU 43Prof. Busch - LSU 43Prof. Busch - LSU 43

•Write on tape, and•Simulate on input

wM w M waccepts

?

Louisiana?s

yes

Turing MachineM̂

Accepts

yes

Page 44: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 44

Therefore:

acceptsM w )ˆ(ML

Equivalently:

TMATwM , TMEMPTYM ˆ

END OF PROOF

Page 45: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 45

• is empty?L

L• is regular?

L• has size 2?

Let be a Turing-acceptable language L

Page 46: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 46

Regular language problem

Input: MTuring Machine

Question: Is )(ML a regular language?

Corresponding language:

language} regular a accepts

that machine aTuring is :{ MMREGULARTM

Page 47: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 47

Theorem:

(regular language problem is unsolvable)

is undecidable

Proof: Reduce (membership problem) to (regular language problem)

TMA

TMREGULAR

TMREGULAR

Page 48: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 48

Decider for

YES

NO

wM,

regular problem decider

DeciderCompute

Reduction

wMf ,

M̂YES

NO

Given the reduction,If is decidable,then is decidable

membership problem decider

TMA

TMA

TMREGULAR

A contradiction!sinceis undecidable

TMATMREGULAR

Page 49: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 49

wM,Compute

Reduction

wMf ,M̂

We only need to build the reduction:

TMATwM , TMREGULARM ˆ

wMf ,

So that:

Page 50: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 50

sTape of M̂

input string

Construct from :M̂ wM,

Prof. Busch - LSU 50Prof. Busch - LSU 50Prof. Busch - LSU 50Prof. Busch - LSU 50

•Write on tape, and•Simulate on input

wM w M w

Accepts

accepts ?

?kkbas

yes yes

Turing MachineM̂)0 some (for k

Page 51: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 51

accepts }0:{)ˆ( nbaML nnM w

does notaccept

M w )ˆ(ML

not regular

regular

Prof. Busch - LSU 51Prof. Busch - LSU 51Prof. Busch - LSU 51Prof. Busch - LSU 51Prof. Busch - LSU 51

•Write on tape, and•Simulate on input

wM w M w

Accepts

accepts ?

?kkbas

yes yes

Turing Machine)0 some (for k

Page 52: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 52

Therefore:

acceptsM w )ˆ(ML

Equivalently:

TMATwM , TMREGULARM ˆ

END OF PROOF

is not regular

Page 53: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 53

• is empty?L

L• is regular?

L• has size 2?

Let be a Turing-acceptable language L

Page 54: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 54

Does have size 2 (two strings)?

Size2 language problem

Input: MTuring Machine

Question: )(ML

Corresponding language:

strings} two exactly accepts

that machine aTuring is :{2 MMSIZE TM

?2|)(| ML

Page 55: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 55

Theorem:

(size2 language problem is unsolvable)

is undecidable

Proof: Reduce (membership problem) to (size 2 language problem)

TMA

TMSIZE 2

TMSIZE 2

Page 56: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 56

Decider for

YES

NO

wM,

size2 problem decider

DeciderCompute

Reduction

wMf ,

M̂YES

NO

Given the reduction,If is decidable,then is decidable

membership problem decider

TMA

TMA

A contradiction!sinceis undecidable

TMA

TMSIZE 2

TMSIZE 2

Page 57: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 57

wM,Compute

Reduction

wMf ,M̂

We only need to build the reduction:

TMATwM , TMSIZEM 2ˆ

wMf ,

So that:

Page 58: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 58

sTape of M̂

input string

Construct from :M̂ wM,

Prof. Busch - LSU 58Prof. Busch - LSU 58Prof. Busch - LSU 58Prof. Busch - LSU 58Prof. Busch - LSU 58

•Write on tape, and•Simulate on input

wM w M w

Accepts

accepts ?

?}Rouge,Baton{s

yes yes

Turing MachineM̂

Page 59: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 59

accepts }Rouge,Baton{)ˆ( MLM w

does notaccept

M w )ˆ(ML

2 strings

0 strings

Prof. Busch - LSU 59Prof. Busch - LSU 59Prof. Busch - LSU 59Prof. Busch - LSU 59Prof. Busch - LSU 59Prof. Busch - LSU 59

•Write on tape, and•Simulate on input

wM w M w

Accepts

accepts ?

?}Rouge,Baton{s

yes yes

Turing Machine

Page 60: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 60

Therefore:

acceptsM w )ˆ(ML

Equivalently:

TMATwM , TMSIZEM 2ˆ

END OF PROOF

has size 2

Page 61: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 61

RICE’s Theorem

• is empty?L

L• is regular?

L• has size 2?

Undecidable problems:

This can be generalized to all non-trivialproperties of Turing-acceptable languages

Page 62: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 62

Non-trivial property:

A property possessed by some Turing-acceptable languages but not all

: is empty?LExample:

L

}Louisiana{L

YES

NO

}Rouge,Baton{LNO

P

1P

Page 63: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 63

: is regular?

More examples of non-trivial properties:

L

}0:{ nbaL nn

YES

NO

L2P

}0:{ naL nYES

: has size 2?L3PLNO

YES

NO }Louisiana{L}Rouge,Baton{L

Page 64: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 64

Trivial property:

A property possessed by ALL Turing-acceptable languages

P

: has size at least 0?LExamples: 4PTrue for all languages

: is accepted by some Turing machine?

L5P

True for allTuring-acceptable languages

Page 65: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 65

We can describe a property as the setof languages that possess the property

P

: is empty?LExample:

1LYES

NO

NO

P

}{ 1LP

If language has property then PL PL

}Louisiana{2 L

}Rouge,Baton{3 L

Page 66: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 66

: has size 1?LP

}{a

},{ a

NO

NO

YES

Example: Suppose alphabet is }{a

}{aa},{ aa

}{aaa}{},{ aaa

}},{},{},{},{},{{ aaaaaaaaaaP

},,{ aaaNO },,{ aaaaaaaaa

Page 67: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 67

Non-trivial property problem

Does have the non-trivial property ?

Input: MTuring Machine

Question: )(ML

Corresponding language:

})( is, that , property

trivial-non the has )( that such

machine aTuring is :{

PMLP

ML

MMPROPERTYTM

?)( PML P

Page 68: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 68

Rice’s Theorem: TMPROPERTY is undecidable

(the non-trivial property problem is unsolvable)

Proof: Reduce (membership problem)

to

TMA

TMPROPERTY TMPROPERTYor

Page 69: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 69

We examine two cases:

Case 1:

Case 2:

P

P

Examples: : is empty?)(MLP

: is regular?)(MLP

: has size 2?)(MLPExample:

Page 70: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 70

Let be the Turing machine thataccepts

Case 1: P

Since is non-trivial, there is a Turing-acceptable languagesuch that:

PX

PX

XM

X

Page 71: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 71

Reduce (membership problem) to

TMA

TMPROPERTY

Page 72: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 72

Decider for

YES

NO

wM,

Non-trivial property problem decider

DeciderCompute

Reduction

wMf ,

M̂YES

NO

Given the reduction,if is decidable,then is decidable

membership problem decider

TMA

TMA

A contradiction!sinceis undecidable

TMA

TMPROPERTY

TMPROPERTY

Page 73: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 73

wM,Compute

Reduction

wMf ,M̂

We only need to build the reduction:

TMATwM , TMPROPERTYM ˆ

wMf ,

So that:

Page 74: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 74

sTape of M̂

input string

Construct from :M̂ wM,

?Xs

•Write on tape, and•Simulate on input

wM w M w

Accepts

accepts ?

yes yes

Turing MachineM̂

Page 75: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 75

For this we can run machine ,that accepts language , with input string

XMX

s

?Xs

•Write on tape, and•Simulate on input

wM w M w

Accepts

accepts ?

yes yes

Turing MachineM̂

Page 76: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 76

accepts XML )ˆ(M w

does notaccept

M w )ˆ(ML

P

P

Prof. Busch - LSU 76

?Xs

•Write on tape, and•Simulate on input

wM w M w

Accepts

accepts ?

yes yes

Turing MachineM̂

Page 77: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 77

Therefore:

acceptsM w PML )ˆ(

Equivalently:

TMATwM , TMPROPERTYM ˆ

Page 78: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 78

Let be the Turing machine thataccepts

Case 2: P

Since is non-trivial, there is a Turing-acceptable languagesuch that:

PX

PX

XM

X

Page 79: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 79

Reduce (membership problem) to

TMA

TMPROPERTY

Page 80: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 80

Decider for

YES

NO

wM,

Non-trivial property problem decider

DeciderCompute

Reduction

wMf ,

M̂YES

NO

Given the reduction,if is decidable,then is decidable

membership problem decider

TMA

TMA

A contradiction!sinceis undecidable

TMA

TMPROPERTY

TMPROPERTY

Page 81: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 81

wM,Compute

Reduction

wMf ,M̂

We only need to build the reduction:

TMATwM , TMPROPERTYM ˆ

wMf ,

So that:

Page 82: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 82

sTape of M̂

input string

Construct from :M̂ wM,

?Xs

•Write on tape, and•Simulate on input

wM w M w

Accepts

accepts ?

yes yes

Turing MachineM̂

Page 83: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 83

accepts XML )ˆ(M w

does notaccept

M w )ˆ(ML

P

PM̂

?Xs

•Write on tape, and•Simulate on input

wM w M w

Accepts

accepts ?

yes yes

Turing Machine

Page 84: Prof. Busch - LSU1 Reductions. Prof. Busch - LSU2 Problem is reduced to problem If we can solve problem then we can solve problem

Prof. Busch - LSU 84

Therefore:

acceptsM w PML )ˆ(

Equivalently:

TMATwM , TMPROPERTYM ˆ

END OF PROOF