Download
average case complexity n.
Skip this Video
Loading SlideShow in 5 Seconds..
Average-case Complexity PowerPoint Presentation
Download Presentation
Average-case Complexity

Average-case Complexity

82 Vues Download Presentation
Télécharger la présentation

Average-case Complexity

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Average-case Complexity Luca Trevisan UC Berkeley

  2. Distributional Problem <P,D> P computational problem • e.g. SAT D distribution over inputs • e.g. n vars 10n clauses

  3. Positive Results: • Algorithm that solves P efficiently on most inputs • Interesting when P useful problem, D distribution arising “in practice” Negative Results: • If <assumption>, then no such algorithm • P useful, D natural • guide algorithm design • Manufactured P,D, • still interesting for crypto, derandomization

  4. Positive Results: • Algorithm that solves P efficiently on most inputs • Interesting when P useful problem, D distribution arising “in practice” Negative Results: • If <assumption>, then no such algorithm • P useful, D natural • guide algorithm design • Manufactured P,D, • still interesting for crypto, derandomization

  5. Holy Grail If there is algorithm A that solves P efficiently on most inputs from D Then there is an efficient worst-case algorithm for [the complexity class] P [belongs to]

  6. Part (1) In which the Holy Grail proves elusive

  7. The Permanent Perm (M) := SsPi M(i,s(i)) Perm() is #P-complete Lipton (1990): If there is algorithm that solves Perm() efficiently on most random matrices, Then there is an algorithm that solves it efficiently on all matrices (and BPP=#P)

  8. Lipton’s Reduction Suppose operations are over finite field of size >n A is good-on-average algorithm (wrong on < 1/(10(n+1)) fraction of matrices) Given M, pick random X, compute A(M+X), A(M+2X),…,A(M+(n+1)X) Whp the same as Perm(M+X),Perm(M+2X),…,Perm(M+(n+1)X)

  9. Lipton’s Reduction Given Perm(M+X),Perm(M+2X),…,Perm(M+(n+1)X) Find univariate degree-n polynomialp such thatp(t) = Perm(M+tX) for all t Output p(0)

  10. Improvements / Generalizations • Can handle constant fraction of errors[Gemmel-Sudan] • Works for PSPACE-complete, EXP-complete,…[Feigenbaum-Fortnow, Babai-Fortnow-Nisan-Wigderson]Encode the problem as a polynomial

  11. Strong Average-Case Hardness • [Impagliazzo, Impagliazzo-Wigderson] Manufacture problems in E, EXP, such that • Size-t circuit correct on ½ + 1/t inputs implies • Size poly(t) circuit correct on all inputs Motivation: [Nisan-Wigderson] P=BPP if there is problem in E of exponential average-case complexity

  12. Strong Average-Case Hardness • [Impagliazzo, Impagliazzo-Wigderson] Manufacture problems in E, EXP, such that • Size-t circuit correct on ½ + 1/t inputs implies • Size poly(t) circuit correct on all inputs Motivation: [Impagliazzo-Wigderson] P=BPP if there is problem in E of exponential average worst-case complexity

  13. Open Question 1 • Suppose there are worst-case intractable problems in NP • Are there average-case intractable problems?

  14. Strong Average-Case Hardness • [Impagliazzo, Impagliazzo-Wigderson] Manufacture problems in E, EXP, such that • Size-t circuit correct on ½ + 1/t inputs implies • Size poly(t) circuit correct on all inputs • [Sudan-T-Vadhan] • IW result can be seen as coding-theoretic • Simpler proof by explicitly coding-theoretic ideas

  15. Encoding Approach • Viola proves that an error-correcting code cannot be computed in AC0 • The exponential-size error-correcting code computation not possible in PH

  16. Problem-specific Approaches? [Ajtai] • Proves that there is a lattice problem such that: • If there is efficient average-case algorithm • There is efficient worst-case approximation algorithm

  17. Ajtai’s Reduction • Lattice Problem • If there is efficient average-case algorithm • There is efficient worst-case approximation algorithm The approximation problem is in NPIcoNP Not NP-hard

  18. Holy Grail • Distributional Problem: • If there is efficient average-case algorithm • P=NP(or NP in BPP, or NP has poly-size circuits,…) Already seen: no “encoding” approach works Can extensions of Ajtai’s approach work?

  19. A Class of Approaches • L problem in NP, D distribution of inputs • R reduction of SAT to <L,D>: • Given instance f of SAT, • R produces instances x1,…,xk of L, each distributed according to D • Given L(x1),…,L(x1), R is able to decide f If there is good-on-average algorithn for <L,D>, we solve SAT in polynomial time [cf. Lipton’s work on Permanent]

  20. A Class of Approaches • L,W problems in NP, D (samplable) distribution of inputs • R reduction of W to <L,D> • Given instance w of W, • R produces instances x1,…,xk of L, each distributed according to D • Given L(x1),…,L(x1), R is able to decide w If there is good-on-average algorithm for <L,D>, we solve W in polynomial time; Can W be NP-complete?

  21. A Class of Approaches • Given instance w of W, • R produces instances x1,…,xk of L, each distributed according to D • Given L(x1),…,L(x1), R is able to decide w Given good-on-average algorithm for <L,D>, we solve W in polynomial time; If we have such reduction, and W is NP-complete, we have Holy Grail! Feigenbaum-Fortnow: W is in “coNP”

  22. Feigenbaum-Fortnow • Given instance w of W, • R produces instances x1,…,xk of L, each distributed according to D • Given L(x1),…,L(x1), R is able to decide w • Using R, Feigenbaum-Fortnow design a 2-round interactive proof with advice for coW • Given w, Prover convinces Verifier that R rejects w after seeing L(x1),…,L(x1)

  23. Feigenbaum-Fortnow • Given instance w of W, • R produces instances x of L distributed as in D • w in L iff x in L Suppose we know PrD[ x in L]= ½ P V R(w) = x1 R(w) = x2 . . . R(w) = xm x1, x2,. . . , xm w (Yes,w1),No,. . . , (Yes, wm) Accept iff all simulations of R rejectand m/2 +/- sqrt(m) answers are certified Yes

  24. Feigenbaum-Fortnow • Given instance w of W, p:= Pr[ xi in L] • R produces instances x1,…,xk of L, each distrib. according to D • Given L(x1),…,L(xk), R is able to decide w P V R(w) -> x11,…,xk1 w x11,…,xkm . . . R(w) -> x1m,…,xkm (Yes,w11),…,NO Accept iff-pkm +/- sqrt(pkm) YES with certificates -R rejects in each case

  25. Generalizations • Bogdanov-Trevisan: arbitrary non-adaptive reductions • Main Open Question:What happens with adaptive reductions?

  26. Open Question 1 Prove the following: Suppose: W,L are in NP, D is samplable distribution, R is poly-time reduction such that • If A solves <L,D> on 1-1/poly(n) frac of inputs • Then R with oracle A solves W on all inputs Then W is in “coNP”

  27. By the Way • Probably impossible by current techniques: If NP not contained in BPP There is a samplable distribution D and an NP problem L Such that <L,D> is hard on average

  28. By the Way • Probably impossible by current techniques: If NP not contained in BPP There is a samplable distribution D and an NP problem L Such that for every efficient A A makes many mistakes solving L on D

  29. By the Way • Probably impossible by current techniques: If NP not contained in BPP There is a samplable distribution D and an NP problem L Such that for every efficient A A makes many mistakes solving L on D • [Guttfreund-Shaltiel-TaShma] Prove: If NP not contained in BPP For every efficient A There is a samplable distribution D Such that A makes many mistakes solving SAT on D

  30. Part (2) In which we amplify average-case complexity and we discuss a short paper

  31. Revised Goal • Proving“If NP contains worst-case intractable problems, then NP contains average-case intractable problems”Might be impossible • Average-case intractability comes in different quantitative degrees • Equivalence?

  32. Average-Case Hardness What does it mean for <L,D> to be hard-on-average? Suppose A is efficient algorithm Sample x ~ D Then A(x) is noticeably likely to be wrong How noticeably?

  33. Average-Case Hardness Amplification Ideally: • If there is <L,Uniform>, L in NP, such that every poly-time algorithm (poly-size circuit) makes > 1/poly(n) mistakes • Then there is <L’,Uniform>, L’ in NP, such that every poly-time algorithm (poly-size circuit) makes > ½ - 1/poly(n) mistakes

  34. Amplification “Classical” approach: Yao’s XOR Lemma Suppose: for every efficient APrD [ A(x) = L(x) ] < 1- d Then: for every efficient A’ PrD [ A’(x1,…,xk) = L(x1) xor … xor L(xk) ] < ½ + (1 - 2d)k + negligible

  35. Yao’s XOR Lemma Suppose: for every efficient APrD [ A(x) = L(x) ] < 1- d Then: for every efficient A’ PrD [ A’(x1,…,xk) = L(x1) xor … xor L(xk) ] < ½ + (1 - 2d)k + negligible Note: computing L(x1) xor … xor L(xk) need not be in NP, even if L is in NP

  36. O’Donnell Approach Suppose: for every efficient APrD [ A(x) = L(x) ] < 1- d Then: for every efficient A’ PrD [ A’(x1,…,xk) = g(L(x1), …, L(xk)) ] < ½ + small(k, d) For carefully chosen monotone function g Now computing g(L(x1),…, L(xk)) is in NP, if L is in NP

  37. Amplification (Circuits) Ideally: • If there is <L,Uniform>, L in NP, such that every poly-time algorithm (poly-size circuit) makes > 1/poly(n) mistakes • Then there is <L’,Uniform>, L’ in NP, such that every poly-time algorithm (poly-size circuit) makes > ½ - 1/poly(n) mistakes Achieved by [O’Donnell, Healy-Vadhan-Viola] for poly-size circuits

  38. Amplification (Algorithms) • If there is <L,Uniform>, L in NP, such that every poly-time algorithm makes > 1/poly(n) mistakes • Then there is <L’,Uniform>, L’ in NP, such that every poly-time algorithm makes > ½ - 1/polylog(n) mistakes [T] [Impagliazzo-Jaiswal-Kabanets-Wigderson] ½ - 1/poly(n) but for PNP||

  39. Open Question 2 Prove: • If there is <L,Uniform>, L in NP, such that every poly-time algorithm makes > 1/poly(n) mistakes • Then there is <L’,Uniform>, L’ in NP, such that every poly-time algorithm makes > ½ - 1/poly(n) mistakes

  40. Completeness • Suppose we believe there is L in NP, D distribution, such that <L,D> is hard • Can we point to a specific problem C such that <C,Uniform> is also hard?

  41. Completeness • Suppose we believe there is L in NP, D distribution, such that <L,D> is hard • Can we point to a specific problem C such that <C,Uniform> is also hard? Must put restriction on D, otherwise assumption is the same as P != NP

  42. Side Note Let K be distribution such that x has probability proportional to 2-K(x) Suppose A solves <L,K> on 1-1/poly(n) fraction of inputs of length n Then A solves L on all but finitely many inputs Exercise: prove it

  43. Completeness • Suppose we believe there is L in NP, Dsamplable distribution, such that <L,D> is hard • Can we point to a specific problem C such that <C,Uniform> is also hard?

  44. Completeness • Suppose we believe there is L in NP, Dsamplable distribution, such that <L,D> is hard • Can we point to a specific problem C such that <C,Uniform> is also hard? Yes we can! [Levin, Impagliazzo-Levin]

  45. Levin’s Completeness Result • There is an NP problem C, such that • If there is L in NP, D computable distribution, such that <L,D> is hard • Then <C,Uniform> is also hard

  46. Reduction Need to define reduction that preserves efficiency on average (Note: we haven’t yet defined efficiency on average) R is a (Karp) average-case reduction from <A,DA> to <B,DB> if • x in A iff R(x) in B • R(DA) is “dominated” by DB: Pr[ R(DA)=y] < poly(n) * Pr [DB = y]

  47. Reduction R is an average-case reduction from <A, DA> to <B, DB> if • x in A iff R(x) in B • R(DA) is “dominated” by DB: Pr[ R(DA)=y] < poly(n) * Pr [DB = y] Suppose we have good algorithm for <B, DB> Then algorithm also good for <B,R(DA)> Solving <A, DA> reduces to solving <B,R(DA)>

  48. Reduction If Pr[ Y=y] < poly(n) * Pr [DB = y] and we have good algorithm for <B, DB > Then algorithm also good for <B,Y> Reduction works for any notion of average-case tractability for which above is true.

  49. Levin’s Completeness Result Follow presentation of [Goldreich] • If <BH,Uniform> is easy on average • Then for every L in NP, every D computable distribution, <L,D> is easy on average BH is non-deterministic Bounded Halting: given <M,x,1t>,does M(x) accept with t steps?