Pseudodistributions that beat all pseudorandom generators (extended abstract)

Edward Pyne, S. Vadhan
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引用次数: 14

Abstract

A recent paper of Braverman, Cohen, and Garg (STOC 2018) introduced the concept of a weighted pseudorandom generator (WPRG), which amounts to a pseudorandom generator (PRG) whose outputs are accompanied with real coefficients that scale the acceptance probabilities of any potential distinguisher. They gave an explicit construction of WPRGs for ordered branching programs whose seed length has a better dependence on the error parameter ε than the classic PRG construction of Nisan (STOC 1990 and Combinatorica 1992). In this work, we give an explicit construction of WPRGs that achieve parameters that are impossible to achieve by a PRG. In particular, we construct a WPRG for ordered permutation branching programs of unbounded width with a single accept state that has seed length Õ(log3/2 n) for error parameter ε = 1/poly(n), where n is the input length. In contrast, recent work of Hoza et al. (ITCS 2021) shows that any PRG for this model requires seed length Ω(log2 n) to achieve error ε = 1/poly(n). As a corollary, we obtain explicit WPRGs with seed length Õ(log3/2 n) and error ε = 1/poly(n) for ordered permutation branching programs of width w = poly(n) with an arbitrary number of accept states. Previously, seed length o(log2 n) was only known when both the width and the reciprocal of the error are subpolynomial, i.e. w = no(1) and ε = 1/no(1) (Braverman, Rao, Raz, Yehudayoff, FOCS 2010 and SICOMP 2014). The starting point for our results are the recent space-efficient algorithms for estimating random-walk probabilities in directed graphs by Ahmadenijad, Kelner, Murtagh, Peebles, Sidford, and Vadhan (FOCS 2020), which are based on spectral graph theory and space-efficient Laplacian solvers. We interpret these algorithms as giving WPRGs with large seed length, which we then derandomize to obtain our results. We also note that this approach gives a simpler proof of the original result of Braverman, Cohen, and Garg, as independently discovered by Cohen, Doron, Renard, Sberlo, and Ta-Shma (these proceedings).
击败所有伪随机生成器的伪分布(扩展摘要)
Braverman, Cohen和Garg (STOC 2018)最近的一篇论文引入了加权伪随机生成器(WPRG)的概念,这相当于一个伪随机生成器(PRG),其输出伴随着缩放任何潜在区分符的接受概率的实系数。与Nisan (STOC 1990和Combinatorica 1992)的经典PRG构造相比,他们给出了有序分支规划的wprg的显式构造,其种子长度对误差参数ε的依赖性更好。在这项工作中,我们给出了一个明确的WPRGs结构,它可以实现PRG不可能实现的参数。特别地,我们构造了一个具有无界宽度的有序排列分支规划的WPRG,该规划具有单一接受状态,种子长度为Õ(log3/ 2n),误差参数ε = 1/poly(n),其中n为输入长度。相比之下,Hoza等人最近的研究(ITCS 2021)表明,该模型的任何PRG都需要种子长度Ω(log2 n)才能实现误差ε = 1/poly(n)。作为推论,对于宽度为w = poly(n)且具有任意数目接受状态的有序排列分支规划,我们得到了种子长度为Õ(log3/ 2n),误差ε = 1/poly(n)的显式wprg。以前,只有当宽度和误差倒数都是次多项式,即w = no(1)和ε = 1/no(1)时,才能知道种子长度o(log2n) (Braverman, Rao, Raz, Yehudayoff, FOCS 2010和SICOMP 2014)。我们研究结果的起点是最近由Ahmadenijad、Kelner、Murtagh、Peebles、Sidford和Vadhan (FOCS 2020)提出的用于估计有向图中随机游走概率的空间高效算法,这些算法基于谱图理论和空间高效的拉普拉斯解算器。我们将这些算法解释为给出具有大种子长度的wprg,然后对其进行非随机化以获得我们的结果。我们还注意到,这种方法对由Cohen、Doron、Renard、Sberlo和Ta-Shma独立发现的Braverman、Cohen和Garg的原始结果给出了更简单的证明(这些记录)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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