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Nearly Optimal Pseudorandomness From Hardness

机译:硬度接近最佳的伪随机性

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Existing proofs that deduce BPP = P from circuit lower bounds convert randomized algorithms into deterministic algorithms with a large polynomial slowdown. We convert randomized algorithms into deterministic ones with little slowdown. Specifically, assuming exponential lower bounds against nondeterministic circuits, we convert any randomized algorithm over inputs of length n running in time t n to a deterministic one running in time t 2+ for an arbitrarily small constant 0" 0 . Such a slowdown is nearly optimal, as, under complexity-theoretic assumptions, there are problems with an inherent quadratic derandomization slowdown. We also convert any randomized algorithm that errs rarely into a deterministic algorithm having a similar running time (with pre-processing).Our results follow from a new, nearly optimal, explicit pseudorandom generator fooling circuits of size s with seed length (1 + ) log s , under the assumption that there exists a function f E that requires nondeterministic circuits of size at least 2 (1 ? ) n , where = O ( ) . The construction uses, among other ideas, a new connection between pseudoentropy generators and locally list recoverable codes.
机译:从电路下限推导出BPP = P的现有证据将随机算法转换为多项式速度变慢的确定性算法。我们将随机算法转换为确定性算法,且速度变慢。具体而言,假设针对不确定性电路的指数下界,对于任意小的常数0> 0,我们将在时间tn上运行的长度为n的输入的任何随机算法转换为在时间t 2+上运行的确定性算法。 ,因为在复杂性理论的假设下,固有的二次去随机化速度变慢存在问题,我们还将任何很少出错的随机算法转换为运行时间相似的确定性算法(经过预处理)。 ,假设存在一个函数f E,该函数需要大小至少为2(1?)n的不确定电路,其中= O ()在其他思想中,该构造使用了伪熵生成器与本地列出可恢复代码之间的新连接。

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