Nearly Optimal Pseudorandomness from Hardness

Dean Doron, Dana Moshkovitz, Justin Oh, David Zuckerman

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 NP ∩ coNP circuits, formally known as randomized SVN circuits, we convert any randomized algorithm over inputs of length n running in time t ≥ n into a deterministic one running in time t 2+α for an arbitrarily small constant α > 0. Such a slowdown is nearly optimal for t close to n, since under standard 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 fooling circuits of size s with seed length (1+α) log s, under the assumption that there exists a function f ∈ E that requires randomized SVN circuits of size at least 2(1−α')n, where α = O(α'). The construction uses, among other ideas, a new connection between pseudoentropy generators and locally list recoverable codes

Original languageEnglish
Article number43
Pages (from-to)1-55
JournalJournal of the ACM
Volume69
Issue number6
DOIs
StatePublished - 17 Nov 2022

Keywords

  • Pseudorandom generators
  • list recovery
  • local list decoding
  • pseudoentropy
  • quantified derandomization

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Hardware and Architecture
  • Artificial Intelligence

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