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[Computational Complexity] Full Derandomization

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  • Lance
    What does the complexity world look like if we have hard functions that let us efficiently derandomize? All of the results in this post follow from the
    Message 1 of 1 , Jul 31, 2006
      What does the complexity world look like if we have hard functions that let us efficiently derandomize? All of the results in this post follow from the following open but reasonable hardness assumption.
      There is some language L computable in time 2O(n) such that for some ε>0, every algorithm A computing L uses 2εn space for sufficiently large n.
      First are the complexity class collapses. As always see the Zoo if some class does not look familiar.
      • ZPP = RP = BPP = P
      • MA = AM = NP. In particular Graph Isomorphism and all of statistical zero-knowledge lie in NP∩co-NP.
      • S2P = ZPPNP = BPPNP = PNP
      • The polynomial-time hierarchy lies in SPP. As a corollary PH ⊆ ⊕P ∩ ModkP ∩ C=P ∩ PP ∩ AWPP
      One can also derandomize Valiant-Vazirani. There is a polynomial time procedure that maps a CNF formula φ to a polynomial list of formula ψi such that
      1. If φ is not satisfiable then all of the ψi are not satisfiable.
      2. If φ is satisfiable then some ψi has exactly one satisfying assignment.
      Beyond complexity classes we also get some randomized constructions such as creating Ramsey graphs. In polynomial in n time, we can generate a polynomial list of graphs on n vertices, most of which have no clique or independent set of size 2 log n.

      This gives a short list containing many Ramsey graphs. We don't know how to verify a Ramsey graph efficiently so even though we have the short list we don't know how to create a single one. Contrast this to the best known deterministic construction that creates a graph with no clique or independent set of size 2(log n)o(1).

      We also get essentially optimal extractors. Given an distribution D on strings of length n where every string has probability at most 2-k and O(log n) additional truly random coins we can output a distribution on length k strings very close to uniform. The truly random coins are used both for the seed of the pseudorandom generator to create the extractor and in applying the extractor itself.

      One also gets polynomial-time computable universal traversal sequences, a path that hits every vertex on any undirected graph on n nodes. The generator will give a polynomial list of sequences but one can just concatenate those sequences. The hardness assumption above won't put the sequence in log space, though we do believe such log-space computable sequences exist. Reingold's proof that we can decide undirected connectivity in logarithm space does not produce a universal traversal sequence though it does give a related exploration sequence.

      There are many more implications of full derandomization including other complexity class inclusions, combinatorial constructions and some applications for resource-bounded Kolmogorov complexity.

      Posted by Lance to Computational Complexity at 7/31/2006 08:39:00 AM

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