Nearly optimal pseudorandomness from hardness

Dean Doron, Dana Moshkovitz, Justin Oh, David Zuckerman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations


Existing proofs that deduce text{BPP} = mathrm{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 geq 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 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
Title of host publicationProceedings - 2020 IEEE 61st Annual Symposium on Foundations of Computer Science, FOCS 2020
PublisherInstitute of Electrical and Electronics Engineers
Number of pages12
ISBN (Electronic)9781728196213
StatePublished - 19 Jan 2021
Externally publishedYes
Event61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020 - Virtual, Durham, United States
Duration: 16 Nov 202019 Nov 2020


Conference61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020
Country/TerritoryUnited States
CityVirtual, Durham


  • derandomization
  • list-recoverable codes
  • pseudo-entropy
  • pseudorandom generators

ASJC Scopus subject areas

  • Computer Science (all)


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