Download presentation

Presentation is loading. Please wait.

Published byCristopher Herd Modified over 3 years ago

1
COMPLEXITY THEORY CSci 5403 LECTURE VII: DIAGONALIZATION

2
Theorem (Cantor). Let ƒ : S ! P(S) map elements of the set S to subsets of S. Then ƒ is not onto. In particular, let D ƒ = { y | y ƒ(y) }. There is no s 2 S such that ƒ(s) = D ƒ : Suppose ƒ(s) = D ƒ. Then s 2 D ƒ, s ƒ(s), s D ƒ. Example: Let S = {0,1,2} and let ƒ(0) = Ø, ƒ(1) = {1,2}, ƒ(2) = {0,1}. y02ƒ(y)?12ƒ(y)?22ƒ(y)? 0 N NN 1N Y Y 2YY N DƒDƒ YNY

3
Theorem (Turing). The diagonal language D TM = { hMi | M does not accept on input hMi } is undecidable. Proof. Let ƒ TM : {0,1}* ! P({0,1}*) map a TMs code to the language it decides. If D TM is decidable, there must be a TM H such that ƒ TM (hHi) = D TM. But D TM is the diagonal set for ƒ TM. Cantor, Gödel, and Turing used it… must be good! (Insert cheesy 1970s theme music)

4
TIME HEIRARCHY THEOREM Theorem. Let f and g be time constructible, and let g(n) = ω(f(n) 2 ). Then DTIME(f(n)) ( DTIME(g(n)). Corollary. P EXP. Proof. Let D f = { hMi | M does not accept hMi in f(|hMi|) steps. }. D f DTIME(f(n)), since it is the diagonal set for L f mapping a TM to the set it decides in time f(n). But recall that we can simulate a TM that runs in time t in time O(t 2 ). Thus D f 2 DTIME(f(n) 2 ).

5
SPACE HEIRARCHY THEOREM Theorem. Let f and g be space constructible, and let g(n) = ω(f(n)). Then SPACE(f(n)) ( SPACE(g(n)). Corollary. L PSPACE. Proof. Analogous. We can simulate a machine using space s(n) in space O(s(n)).

6
NTIME HEIRARCHY THEOREM Theorem. Let f and g be time constructible, and let g(n) = ω(f(n) 2 ). Then NTIME(f(n)) ( NTIME(g(n)). Proof. Let D f = { hMi | M does not accept hMi in f(|hMi|) steps. }. D f NTIME(f(n)), since it is the diagonal set for L f mapping a TM to the set it accepts in time f(n). But recall that we can simulate a NTM that runs in time t in time O(t 2 ). Thus D f 2 NTIME(f(n) 2 ).

7
NTIME HEIRARCHY THEOREM We dont know how to simulate a coNTIME(f(n)) machine in NTIME(g(n)), where g(n) = o(2 f(n) )! Instead of differing from M i at string i, well build D that is different from M i in (s(i),s(i+1)]. s(1)s(2)s(3)…s(i)s(i)+1…s(i+1) M1M1 YNY…NY…Y M2M2 NYY…YN…N MiMi YYY…NN…N DNNY…NY…Y

8
NTIME HEIRARCHY THEOREM Key idea: make s(i+1) big enough that we can simulate a coNTIME machine on input s(i). s(i)+1s(i)+2…s(i+1)-1s(i+1) MiMi NY…NN DY…NNY Let s(i) = 1, if i = 0 2 f(s(i-1)) f(s(i-1)) 2, otherwise Let NTM D(1 n ) = find i such that s(i) < n · s(i+1) if n = s(i+1) return not(M i (1 s(i) )) else return NSIM f (M i,1 n+1 ) simulate all branches f(s(i)) steps

9
NTIME HEIRARCHY THEOREM Theorem. Let f and g be time constructible, and g(n) = ω(f(n+1) 2 ). Then NTIME(f(n)) ( NTIME(g(n)). Proof. The language accepted by machine D is not in NTIME(f(n)) by construction. But the running time of D on input size n is: Case 1: n s(N): Time to simulate an NTM for f(n+1) steps = O(f(n+1) 2 ). Case 2: n = s(i+1): Time to simulate all branches of length f(s(i)) = 2 f(s(i)) f(s(i)) 2 = s(i+1) = O(n).

10
LADNERS THEOREM Theorem. If P NP, then there is a language L 2 NP n P that is not NP-complete. Proof. Suppose P NP. Let H: N ! N and consider the set SAT H = { (ψ,1 k ) | ψ 2 SAT and k = n H(n) }. If H(n) = c (= O(1)), then SAT H P. (why?) If H(n) = ω(1) then SAT H cannot be NP-complete: The reduction ƒ from SAT to SAT H must reduce the size of the formula.

11
We will build an H(n)=ω(1) so that SAT H P. H(n) = min { j < n : M j decides SAT H [n] in time jn j } For a set S, define S[n] = { s 2 S : |s| < n }. if M i decides SAT H in poly(n) time, then H(n) · i. Suppose H(n) = O(1), i.e. for n > N, H(n) · k. Then there is a k 2 n k -time algorithm for SAT H. (why?) Combined we have SAT H P and SAT H is not NP Complete. Whats wrong with this proof?

12
H(n) = min { j < lg n / lg lg n : M j decides SAT H [lg n] in time jn j } Fix: make it easy to compute H(n): To test M i on only poly(n) inputs: replace SAT H [n] with SAT H [O(lg n)] To test each input for only poly(n) steps: replace j < n with j < lg n / lg lg n

13
LIMITS OF DIAGONALIZATION All of the previous theorems are true because TMs can be enumerated and simulated. Thus, any proof that uses only these facts must also hold for any computational model that also has these properties. We will show an enumerable, simulatable model where P=NP, and one where P NP. So a proof using only diagonalization cannot resolve P vs NP.

14
ORACLE TM An oracle Turing Machine is a TM with a query Tape and three states q ?, q yes, q no. FINITE STATE CONTROL INPUT WORK QUERY S With an oracle for set S, it goes in one step from state q ? with x on the query tape to q x2S.

15
We denote M running with oracle S by M S. M S is pronounced M relative to S Claim. Relative to a fixed oracle O, the set of OTMs is enumerable and simulatable. Theorem (Baker, Gill, Solovay). There exist oracles A and B such that P A = NP A and P B NP B. For fixed O, we can define complexity classes NP O, P O, PSPACE O, etc. e.g. S 2 P O iff 9 poly(n)-time M. L(M O ) = S S 2 NP O iff 9 poly(n)-time V, poly q. S = { x | 9y2{0,1} q(|x|). V O (x,y) accepts }

16
P A = NP A Let A be any PSPACE-complete problem. We know that P A µ NP A. (why?) Also NP A µ NPSPACE, since it only takes poly(n) space to answer queries to A. But notice that NPSPACE = PSPACE µ P A.

17
P B NP B We construct a set B so that the language U B = { 1 n | 9 x 2 B. |x| = n } is not in P B. Note that for any set B, we have U B 2 NP B. We build B as a union [ i 2 N B i. B 0 = Ø. Also, Q 0 = Ø To build B i : let n = 1 + max { |x| : x 2 B j [ Q j, j · i }. Run M i B[n] (1 n ) for 2 n /10 steps, adding every oracle query to Q i. If M i accepts, B i = Ø. Else B i = {0,1} n n Q i.

18
P B NP B 1…nini …f(n i )n i+1 … M1M1 Y... MiMi N M i+1 Y UBUB N…Y…NN BØ…{0,1} n ……Ø

19
RELATIVIZATION If an argument about two complexity classes is true relative to any oracle, we say it relativizes. The oracles A and B show that any argument that relativizes cannot resolve P vs NP. Diagonalization relativizes because it doesnt care about how a machine computes. Results that care: NP · P 3SAT, IP = PSPACE, NP = PCP(1,lg n)…

20
CS5403.info

Similar presentations

OK

1 Undecidability Andreas Klappenecker [based on slides by Prof. Welch]

1 Undecidability Andreas Klappenecker [based on slides by Prof. Welch]

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on our environment of class 10 free download Ppt on high level languages vs low level Ppt on sets theory Ppt on sets for class 11 cbse Ppt on object-oriented programming in java Ppt on bmc remedy service Ppt on school education in india Moral stories for kids ppt on batteries Ppt on second law of thermodynamics entropy Ppt on formation of company for class 11