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首页> 外文期刊>Computational complexity >PSEUDORANDOM GENERATORS, TYPICALLY-CORRECT DERANDOMIZATION, AND CIRCUIT LOWER BOUNDS
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PSEUDORANDOM GENERATORS, TYPICALLY-CORRECT DERANDOMIZATION, AND CIRCUIT LOWER BOUNDS

机译:伪随机发生器,典型错误的去随机化和电路的下界

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摘要

The area of derandomization attempts to provide efficient deterministic simulations of randomized algorithms in various algorithmic settings. Goldreich and Wigderson introduced a notion of "typically-correct" deterministic simulations, which are allowed to err on few inputs. In this paper, we further the study of typically-correct derandomization in two ways.First, we develop a generic approach for constructing typically-correct derandomizations based on seed-extending pseudorandom generators, which are pseudorandom generators that reveal their seed. We use our approach to obtain both conditional and unconditional typically-correct derandomization results in various algorithmic settings. We show that our technique strictly generalizes an earlier approach by Shaltiel based on randomness extractors and simplifies the proofs of some known results. We also demonstrate that our approach is applicable in algorithmic settings where earlier work did not apply. For example, we present a typically-correct polynomial-time simulation for every language in BPP based on a hardness assumption that is (seemingly) weaker than the ones used in earlier work.Second, we investigate whether typically-correct derandomization of BPP implies circuit lower bounds. Extending the work of Kabanets and Impagliazzo for the zero-error case, we establish a positive answer for error rates in the range considered by Goldreich and Wigderson. In doing so, we provide a simpler proof of the zero-error result. Our proof scales better than the original one and does not rely on the result by Impagliazzo, Kabanets, and Wigderson that NEXP having polynomial-size circuits implies that NEXP coincides with EXP.
机译:去随机化领域试图在各种算法设置中提供有效的随机算法的确定性模拟。 Goldreich和Wigderson引入了“典型正确的”确定性仿真概念,该仿真允许在很少的输入上犯错误。在本文中,我们通过两种方式进一步研究典型正确的随机化方法:首先,我们开发了一种基于种子扩展伪随机生成器(其是揭示其种子的伪随机生成器)构造典型正确的随机化方法的通用方法。我们使用我们的方法在各种算法设置中获得有条件的和无条件的通常正确的去随机化结果。我们表明,我们的技术严格地概括了Shaltiel基于随机性提取器的较早方法,并简化了一些已知结果的证明。我们还证明了我们的方法适用于早期工作不适用的算法设置。例如,我们基于硬度假设(似乎)比早期工作中使用的假设弱,针对BPP中的每种语言提供了通常正确的多项式时间仿真。其次,我们研究了通常正确的BPP去随机化是否意味着电路下界。扩展Kabanets和Impagliazzo在零错误案例中的工作,我们为Goldreich和Wigderson所考虑的范围内的错误率建立了肯定的答案。这样,我们提供了零误差结果的更简单证明。我们的证明比原来的证明更好地缩放,并且不依赖于Impagliazzo,Kabanets和Wigderson的结论,即具有多项式电路的NEXP暗示NEXP与EXP一致。

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