Download presentation

Presentation is loading. Please wait.

Published byNickolas Cunningham Modified about 1 year ago

1
A survey on derandomizing BPP and AM Danny Gutfreund, Hebrew U. Ronen Shaltiel, Weizmann Inst. Amnon Ta-Shma, Tel-Aviv U.

2
Arthur-Merlin Games [BM] Interactive games in which the all- powerful prover Merlin attempts to prove some statement to a probabilistic poly-time verifier. Merlin Arthur “xL”“xL” toss coins message I accept

3
Arthur-Merlin Games [BM] Completeness: If the statement is true then Arthur accepts. Soundness: If the statement is false then Pr[Arthur accepts]< ½. Merlin Arthur “xL”“xL” toss coins message I accept

4
Arthur-Merlin Games [BM] Completeness: If the statement is true then Arthur accepts. Soundness: If the statement is false then Pr[Arthur accepts]< ½. The class AM: All languages L which have an Arthur-Merlin protocol. Contains many interesting problems not known to be in NP.

5
Example: Co-isomorphism of Graphs. L={G 1,G 2 : the labeled graphs G 1,G 2 are not isomorphic}. L in coNP and is not known to be in NP. Merlin Arthur (G 1,G 2 ) L Randonly chooses: b {1,2} random permutation of G b “ The graph G c was permuted ” Decides which of the two graphs was permuted. Verifies that c=b.

6
The big question: Does AM=NP? In other words: Can every Arthur-Merlin protocol be replaced with one in which Arthur is deterministic? Note that such a protocol is an NP proof.

7
Derandomization: a brief overview A paradigm that attempts to transform: Probabilistic algorithms => deterministic algorithms. (P BPP EXP NEXP). Probabilistic protocols => deterministic protocols. (NP AM EXP NEXP). We don ’ t know how to separate BPP and NEXP. Can derandomize BPP and AM under natural complexity theoretic assumptions.

8
Hardness versus Randomness Initiated by [BM,Yao,Shamir]. Assumption: hard functions exist. Conclusion: Derandomization. A lot of works: [BM82,Y82,HILL,NW88,BFNW93, I95,IW97,IW98,KvM99,STV99,ISW99,MV99, ISW00,SU01,U02,TV02]

9
A quick survey Assumption: There exists a function in DTIME(2 O(n) ) which is hard for “ small ” circuits. AMBPPClass Nondeterministic circuits Deterministic circuits A hard function for: AM=NPBPP=PHigh-end AM NSUBEXPBPP SUBEXP Low-end

10
Hardness versus Randomness Assumption: hard functions exist. Conclusion: Derandomization.

11
Hardness versus Randomness Assumption: hard functions exist. Exists pseudo-random generator Conclusion: Derandomization.

12
Pseudo-random generators A pseudo-random generator (PRG) is an algorithm that stretches a short string of truly random bits into a long string of pseudo-random bits. pseudo-random bits PRG seed Pseudo-random bits are indistinguishable from truly random bits for feasible algorithms. For derandomizing AM: Feasible algorithms = nondeterministic circuits. ??????????????

13
Pseudo-random generators for nondeterministic circuits Nondeterministic circuits can identify pseudo-random strings. Given a long string, guess a short seed and check that PRG(seed)=long string. Can distinguish between random strings and pseudo- random strings. Assuming the circuit can run the PRG!! The Nisan-Wigderson setup: The circuit cannot run the PRG!! For example: The PRG runs in time n 5 and fools (nondeterministic) circuits of size n 3. Sufficient for derandomization!!

14
Hardness versus Randomness Assumption: hard functions exist. Exists pseudo-random generator Conclusion: Derandomization.

15
PRG ’ s for nondeterministic circuits derandomize AM We can model the AM protocol as a nondeterministic circuit which gets the random coins as input. Merlin Arthur “xL”“xL” random message message I accept Hardwire input

16
PRG ’ s for nondeterministic circuits derandomize AM We can model the AM protocol as a nondeterministic circuit which gets the random coins as input. Merlin Arthur “xL”“xL” random input Nondeterministic guess I accept input Nondeterministic guess Hardwire input

17
PRG ’ s for nondeterministic circuits derandomize AM We can model the AM protocol as a nondeterministic circuit which gets the random coins as input. We can use pseudo-random bits instead of truly random bits. Merlin Arthur “xL”“xL” pseudo-random input Nondeterministic guess I accept Nondeterministic guess input Hardwire input

18
PRG ’ s for nondeterministic circuits derandomize AM We have an AM protocol in which Arthur acts deterministically. (Arthur sends all pseudo-random strings and Merlin replies on each one.) Deterministic protocol => NP proof. Merlin Arthur “xL”“xL” pseudo-random input Nondeterministic guess I accept

19
A quick survey Assumption: There exists a function in DTIME(2 O(n) ) which is hard for “ small ” circuits. AMBPPClass Nondeterministic circuits Deterministic circuits A hard function for: AM=NPBPP=PHigh-end AM NSUBEXPBPP SUBEXP Low-end

20
The Nisan-Wigderson setting We ’ re given a function f which is: Hard for small circuits. Computable by uniform machines with “ slightly ” larger time. Basic idea: G(x)=x,f(x) “ f(x) looks random to a small circuit that sees x ”. Warning: no composition theorems. Correctness proof of PRG can ’ t use it ’ s efficiency. The PRG runs in time “ slightly ” larger than the size of the circuit.

21
The rest At this point I moved to the blackboard and covered: The Nisan-Wigderson generator. (You can find a presentation (as well as an introduction to derandomization)on my homepage under the title “ derandomizing BPP ”. This was written in 1998 so the part about hardness amplification is slightly outdated. However, the first chapter is still relevant and contains both the BMY and NW generators. I also explained that PRGs for nondeterministic circuits (which derandomize AM) can be constructed using methods for constructing PRGs for deterministic circuits. This was pointed out by Klivans and van-Melekbeek and you can get the paper at

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google