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Targeted pseudorandom generators, simulation advice generators, and derandomizing logspace

Published: 19 June 2017 Publication History
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  • Abstract

    Assume that for every derandomization result for logspace algorithms, there is a pseudorandom generator strong enough to nearly recover the derandomization by iterating over all seeds and taking a majority vote. We prove under a precise version of this assumption that BPL ⊆ ∩α > 0 DSPACE(log1 + α n).
    We strengthen the theorem to an equivalence by considering two generalizations of the concept of a pseudorandom generator against logspace. A targeted pseudorandom generator against logspace takes as input a short uniform random seed and a finite automaton; it outputs a long bitstring that looks random to that particular automaton. A simulation advice generator for logspace stretches a small uniform random seed into a long advice string; the requirement is that there is some logspace algorithm that, given a finite automaton and this advice string, simulates the automaton reading a long uniform random input. We prove that ∩α > 0 prBPSPACE(log1 + α n) = ∩α > 0 prDSPACE(log1 + α n) if and only if for every targeted pseudorandom generator against logspace, there is a simulation advice generator for logspace with similar parameters.
    Finally, we observe that in a certain uniform setting (namely, if we only worry about sequences of automata that can be generated in logspace), targeted pseudorandom generators against logspace can be transformed into simulation advice generators with similar parameters.

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    Cited By

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    • (2018)Simple Optimal Hitting Sets for Small-Success RL2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS.2018.00015(59-64)Online publication date: Oct-2018

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    cover image ACM Conferences
    STOC 2017: Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing
    June 2017
    1268 pages
    ISBN:9781450345286
    DOI:10.1145/3055399
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    Published: 19 June 2017

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    Author Tags

    1. derandomization
    2. pseudorandom generators
    3. space complexity

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    STOC '17: Symposium on Theory of Computing
    June 19 - 23, 2017
    Montreal, Canada

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    • (2020)Optimal error pseudodistributions for read-once branching programsProceedings of the 35th Computational Complexity Conference10.4230/LIPIcs.CCC.2020.25(1-27)Online publication date: 28-Jul-2020
    • (2018)Simple Optimal Hitting Sets for Small-Success RL2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS.2018.00015(59-64)Online publication date: Oct-2018

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