TCS+ Talk

Wednesday March 25, 2020 10:00 AM

Nearly Optimal Pseudorandomness From Hardness

Speaker: Dana Moshkovitz, University of Texas at Austin
Location: Online Event

Abstract: Existing proofs that deduce BPP=P from circuit lower bounds convert randomized algorithms into deterministic algorithms with a large polynomial slowdown. We convert randomized algorithms into deterministic ones with little slowdown. Specifically, assuming exponential lower bounds against randomized single-valued nondeterministic (SVN) circuits, we convert any randomized algorithm over inputs of length n running in time t>=n to a deterministic one running in time t^{2+alpha} for an arbitrarily small constant alpha > 0. Such a slowdown is nearly optimal, as, under complexity-theoretic assumptions, there are problems with an inherent quadratic derandomization slowdown. We also convert any randomized algorithm that errs rarely into a deterministic algorithm having a similar running time (with pre-processing). The latter derandomization result holds under weaker assumptions, of exponential lower bounds against deterministic SVN circuits. Our results follow from a new, nearly optimal, explicit pseudorandom generator. The construction uses, among other ideas, a new connection between pseudoentropy generators and locally list recoverable codes.

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