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- W2161535207 abstract "We propose a new approach towards derandomization in the uniform setting, where it is computationally hard to find 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 infinitely often (i.o.) to every efficient adversary. A similar result by Impagliazzo and Wigderson (1998) states that BPP allows deterministic subexponential-time simulations that appear correct with respect to any efficiently sampleable distribution i.o., under the assumption that EXP/spl ne/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 deterministic subexponential-time algorithm that appears correct i.o. to every efficient adversary, or EXP=ZPP. In particular this implies that if ZPP is somewhat easy, e.g., ZPP/spl sube/DTIME(2(n/sup c/)) for some fixed constant c, then RP is subexponentially easy in the uniform setting described above." @default.
- W2161535207 created "2016-06-24" @default.
- W2161535207 creator A5066747130 @default.
- W2161535207 date "2002-11-07" @default.
- W2161535207 modified "2023-10-16" @default.
- W2161535207 title "Easiness assumptions and hardness tests: trading time for zero error" @default.
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- W2161535207 doi "https://doi.org/10.1109/ccc.2000.856746" @default.
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