Abstract

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 innitely often (i.o.) to every ecient adversary. A similar result by Impagliazzo and Wigderson (FOCS'98) states that BPP allows deterministic subexponential-time simulations that appear correct with respect to any eciently 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...

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