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CS151 Complexity Theory

CS151 Complexity Theory. Lecture 11 May 7, 2013. Min-entropy. General model of physical source w/ k < n bits of hidden randomness. 2 k strings. string sampled uniformly from this set. {0,1} n. Definition : random variable X on {0,1} n has min-entropy min x –log(Pr[X = x])

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CS151 Complexity Theory

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  1. CS151Complexity Theory Lecture 11 May 7, 2013

  2. Min-entropy • General model of physical source w/ k < n bits of hidden randomness 2kstrings string sampled uniformly from this set {0,1}n • Definition: random variable X on {0,1}n has min-entropyminx –log(Pr[X = x]) • min-entropy k implies no string has weight more than 2-k

  3. Extractor • Extractor: universal procedure for “purifying” imperfect source: • E is efficiently computable • truly random seed as “catalyst” source string 2kstrings E near-uniform seed m bits {0,1}n t bits

  4. Extractor “(k, ε)-extractor”  for all X with min-entropy k: • output fools all circuits C: |Prz[C(z) = 1] - Pry, xX[C(E(x, y)) = 1]| ≤ ε • distributions E(X, Ut), Um “ε-close” (L1 dist ≤ 2ε) • Notice similarity to PRGs • output of PRG fools all efficient tests • output of extractor fools all tests

  5. Extractors • Goals: good: best: short seed O(log n) log n+O(1) long output m = kΩ(1) m = k+t–O(1) many k’s k = nΩ(1) any k = k(n) source string 2kstrings E near-uniform seed m bits {0,1}n t bits

  6. Extractors • random function for E achieves best ! • but we need explicit constructions • many known; often complex + technical • optimal extractors still open • Trevisan Extractor: • insight: use NW generator with source string in place of hard function • this works (!!) • proof slightly different than NW, easier

  7. Trevisan Extractor • Ingredients: ( > 0, m are parameters) • error-correcting code C:{0,1}n  {0,1}n’ distance (½ - ¼m-4)n’ blocklength n’ = poly(n) • (log n’, a = δlog n/3) design: S1,S2,…,Sm  {1…t = O(log n’)} E(x, y)=C(x)[y|S1]◦C(x)[y|S2]◦…◦C(x)[y|Sm]

  8. Trevisan Extractor E(x, y)=C(x)[y|S1]◦C(x)[y|S2]◦…◦C(x)[y|Sm] Theorem (T): E is an extractor for min-entropy k = nδ, with • output length m = k1/3 • seed length t = O(log n) • error ε ≤ 1/m C(x): 010100101111101010111001010 seed y

  9. Trevisan Extractor • Proof: • given X  {0,1}n of size 2k • assume E fails to ε-pass statistical test C |Prz[C(z) = 1] - PrxX, y[C(E(x, y)) = 1]| > ε • distinguisher C predictor P: PrxX, y[P(E(x, y)1…i-1)=E(x, y)i] > ½ + ε/m

  10. Trevisan Extractor • Proof (continued): • for at least ε/2 of x  X we have: Pry[P(E(x, y)1…i-1)=E(x, y)i] > ½ + ε/(2m) • fix bits , outside of Si to preserve advantage Pry’[P(E(x;y’)1…i-1)=C(x)[y’] ] >½ + ε/(2m) • as vary y’, for j ≠ i, j-th bit of E(x; y’) varies over only 2avalues • (m-1) tables of 2a values supply E(x;y’)1…i-1

  11. Trevisan Extractor output C(x)[y’] w.p. ½ + ε/(2m) Y’  {0,1}log n’ P y’

  12. Trevisan Extractor • Proof (continued): • (m-1) tables of size 2a constitute a description of a string that has ½ + ε/(2m) agreement with C(x) • # of strings x with such a description? • exp((m-1)2a) = exp(nδ2/3) = exp(k2/3) strings • Johnson Bound: each string accounts for at most O(m4) x’s • total #: O(m4)exp(k2/3) << 2k(ε/2) • contradiction

  13. Extractors Trevisan: k = n± t = O(log n) m = k1/3 ² = 1/m • (k, )- extractor: • E is efficiently computable • 8 X with minentropy k, E fools all circuits C: |Prz[C(z) = 1] - Pry, xX[C(E(x, y)) = 1]| ≤ ε source string 2kstrings E near-uniform seed m bits {0,1}n t bits

  14. Strong error reduction • L BPP if there is a p.p.t. TM M: x  L  Pry[M(x,y) accepts] ≥ 2/3 x  L  Pry[M(x,y) rejects] ≥ 2/3 • Want: x  L  Pry[M(x,y) accepts] ≥ 1 - 2-k x  L  Pry[M(x,y) rejects] ≥ 1 - 2-k • We saw: repeat O(k) times • n = O(k)·|y| random bits; 2n-k badstrings Want to spend n = poly(|y|) random bits; achieve << 2n/3bad strings

  15. Strong error reduction • Better: • E extractor for minentropy k=|y|3=nδ, ε < 1/6 • pick random w  {0,1}n, run M(x, E(w, z)) for all z  {0,1}t, take majority • call w “bad” if majzM(x, E(w, z)) incorrect |Prz[M(x,E(w,z))=b] - Pry[M(x,y)=b]| ≥ 1/6 • extractor property: at most 2k bad w • n random bits; 2nδbad strings

  16. RL • Recall: probabilistic Turing Machine • deterministic TM with extra tape for “coin flips” • RL(Random Logspace) • L RL if there is a probabilistic logspace TM M: x  L  Pry[M(x,y) accepts] ≥ ½ x  L  Pry[M(x,y) rejects] = 1 • important detail #1: only allow one-way access to coin-flip tape • important detail #2: explicitly require to run in polynomial time

  17. RL • L  RL NL  SPACE(log2 n) • Theorem (SZ) : RL  SPACE(log3/2 n) • Belief: L = RL (open problem)

  18. RL L  RL NL • Natural problem: Undirected STCONN: given an undirected graph G = (V, E), nodes s, t, is there a path from s  t? Theorem: USTCONN  RL. (Recall: STCONN is NL-complete)

  19. Undirected STCONN • Proof sketch: (in Papadimitriou) • add self-loop to each vertex (technical reasons) • start at s, random walk 2|V||E| steps, accept if see t • Lemma: expected return time for any node i is 2|E|/di • suppose s=v1, v2, …, vn=t is a path • expected time from vi to vi+1 is (di/2)(2|E|/di) = |E| • expected time to reach vn ≤ |V||E| • Pr[fail reach t in 2|V||E| steps] ≤ ½ • Reingold 2005: USTCONN  L

  20. A motivating question • Central problem in logic synthesis: • Complexity of this problem? • NP-hard? in NP? in coNP? in PSPACE? • complete for any of these classes?  • given Boolean circuit C, integer k • is there a circuit C’ of size at most k that computes the same function C does?      x1 x2 x3 … xn

  21. Oracle Turing Machines • Oracle Turing Machine (OTM): • multitape TM M with special “query” tape • special states q?, qyes, qno • on input x, with oracle language A • MA runs as usual, except… • when MA enters state q?: • y = contents of query tape • y  A  transition to qyes • y  A  transition to qno

  22. Oracle Turing Machines • Nondeterministic OTM • defined in the same way • (transition relation, rather than function) • oracle is like a subroutine, or function in your favorite programming language • but each call counts as single step e.g.: given φ1, φ2, …, φn are even # satisfiable? • poly-time OTM solves with SAT oracle

  23. Oracle Turing Machines Shorthand #1: • applying oracles to entire complexity classes: • complexity class C • language A CA = {L decided by OTM M with oracle A with M “in” C} • example: PSAT

  24. Oracle Turing Machines Shorthand #2: • using complexity classes as oracles: • OTM M • complexity class C • MCdecides language L if for some language A  C, MA decides L Both together:CD =languages decided by OTM “in” C with oracle language from D exercise: show PSAT = PNP

  25. The Polynomial-Time Hierarchy • can define lots of complexity classes using oracles • the classes on the next slide stand out • they have natural complete problems • they have a natural interpretation in terms of alternating quantifiers • they help us state certain consequences and containments(more later)

  26. The Polynomial-Time Hierarchy Σ0= Π0 =P Δ1=PP Σ1=NPΠ1=coNP Δ2=PNP Σ2=NPNPΠ2=coNPNP Δi+1=PΣiΣi+i=NPΣiΠi+1=coNPΣi Polynomial HierarchyPH = iΣi

  27. The Polynomial-Time Hierarchy Σ0= Π0 = P Δi+1=PΣiΣi+i=NPΣiΠi+1=coNPΣi • Example: • MIN CIRCUIT: given Boolean circuit C, integer k; is there a circuit C’ of size at most k that computes the same function C does? • MIN CIRCUIT  Σ2

  28. The Polynomial-Time Hierarchy Σ0= Π0 = P Δi+1=PΣiΣi+i=NPΣiΠi+1=coNPΣi • Example: • EXACT TSP: given a weighted graph G, and an integer k; is the k-th bit of the length of the shortest TSP tour in G a 1? • EXACT TSP  Δ2

  29. The PH EXP PSPACE PSPACE: generalized geography, 2-person games… 3rd level: V-C dimension… 2nd level: MIN CIRCUIT, BPP… 1st level: SAT, UNSAT, factoring, etc… PH Σ3 Π3 Δ3 Σ2 Π2 Δ2 NP coNP P

  30. Useful characterization • Recall: L  NP iff expressible as L = { x | 9y, |y| ≤ |x|k, (x, y)  R } where R  P. • Corollary: L  coNP iff expressible as L = { x | 8y, |y| ≤ |x|k, (x, y)  R } where R  P.

  31. Useful characterization Theorem: L  Σi iff expressible as L = { x | 9 y, |y| ≤ |x|k, (x, y)  R } where R  Πi-1. • Corollary: L  Πi iff expressible as L = { x | 8 y, |y| ≤ |x|k, (x, y)  R } where R  Σi-1.

  32. Useful characterization Theorem: L  Σi iff expressible as L = { x | 9 y, |y| ≤ |x|k, (x, y)  R }, where R  Πi-1. • Proof of Theorem: • induction on i • base case (i =1) on previous slide ( ) • we know Σi=NPΣi-1 = NPΠi-1 • guess y, ask oracle if (x, y)  R

  33. Useful characterization Theorem: L  Σi iff expressible as L = { x | 9 y, |y| ≤ |x|k, (x, y)  R }, where R  Πi-1. ( ) • given L  Σi = NPΣi-1decided by ONTM M running in time nk • try: R = { (x, y) : y describes valid path of M’s computation leading to qaccept } • but how to recognize valid computation path when it depends on result of oracle queries?

  34. Useful characterization Theorem: L  Σi iff expressible as L = { x | 9 y, |y| ≤ |x|k, (x, y)  R }, where R  Πi-1. • try: R = { (x, y) : y describes valid path of M’s computation leading to qaccept } • valid path = step-by-step description including correctyes/no answer for each A-oracle query zj (A  Σi-1) • verify “no” queries in Πi-1: e.g: z1 A  z3 A  …  z8 A • for each “yes” query zj:9 wj, |wj| ≤ |zj|kwith (zj, wj)  R’ for some R’  Πi-2by induction. • for each “yes” query zj put wj in description of path y

  35. Useful characterization Theorem: L  Σi iff expressible as L = { x | 9 y, |y| ≤ |x|k, (x, y)  R }, where R  Πi-1. • single language R in Πi-1 : (x, y) R  all “no” zj are not in A and all “yes” zj have (zj, wj)  R’ and y is a path leading to qaccept. • Note: AND of polynomially-many Πi-1 predicates is in Πi-1.

  36. Alternating quantifiers Nicer, more usable version: • LΣi iff expressible as L = { x | 9y1 8y29y3 …Qyi (x, y1,y2,…,yi)R } where Q= 8/9if i even/odd, and RP • LΠi iff expressible as L = { x | 8y19y2 8y3 …Qyi (x, y1,y2,…,yi)R } where Q= 9/8 if i even/odd, and RP

  37. Alternating quantifiers • Proof: • ( )induction on i • base case: true for Σ1=NP and Π1=coNP • consider LΣi: L = {x | 9y1 (x, y1)  R’ }, for R’  Πi-1 L = {x | 9y1 8y2 9y3 …Qyi ((x, y1), y2,…,yi)R} L = {x | 9y1 8y2 9y3 …Qyi (x, y1,y2,…,yi)R} • same argument for L Πi • ( ) exercise.

  38. Alternating quantifiers Pleasing viewpoint: “9898989…” PSPACE const. # of alternations poly(n) alternations PH Δ3 Σ2 Π2 “98” “89” Σi Πi “989…” “898…” Δ2 Σ3 Π3 “898” “989” NP coNP “8” “9” P

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