Easiness Assumptions and Hardness Tests : Trading Time for Zero

نویسنده

  • Valentine Kabanets
چکیده

We propose a new approach towards derandomization in the uniform setting, where it is computationally hard to nd possible mistakes in the simulation of a given probabilistic algorithm. The approach consists in combining both easiness and hardness complexity assumptions: if a derandomization method based on an easiness assumption fails, then we obtain a certain hardness test that can be used to remove error in BPP algorithms. As an application, we prove that every RP algorithm can be simulated by a zero-error probabilistic algorithm, running in expected subexponential time, that appears correct innnitely often (i.o.) to every eecient adversary. A similar result by Impagliazzo and Wigderson (FOCS'98) states that BPP allows de-terministic subexponential-time simulations that appear correct with respect to any eeciently sampleable distribution i.o., under the assumption that EXP 6 = BPP; in contrast, our result does not rely on any unproven assumptions. As another application of our techniques, we get the following gap theorem for ZPP: either every RP algorithm can be simulated by a deter-ministic subexponential-time algorithm that appears correct i.o. to every eecient adversary, or EXP = ZPP. In particular, this implies that if ZPP is somewhat easy, e.g., ZPP DTIME(2 n c) for some xed constant c, then RP is subexponentially easy in the uniform setting described above.

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تاریخ انتشار 2000