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首页> 外文期刊>LIPIcs : Leibniz International Proceedings in Informatics >Near-Optimal Pseudorandom Generators for Constant-Depth Read-Once Formulas
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Near-Optimal Pseudorandom Generators for Constant-Depth Read-Once Formulas

机译:恒定深度读取一次公式的近似最佳伪随机生成器

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We give an explicit pseudorandom generator (PRG) for read-once AC^0, i.e., constant-depth read-once formulas over the basis {wedge, vee, neg} with unbounded fan-in. The seed length of our PRG is O~(log(n/epsilon)). Previously, PRGs with near-optimal seed length were known only for the depth-2 case [Gopalan et al., 2012]. For a constant depth d 2, the best prior PRG is a recent construction by Forbes and Kelley with seed length O~(log^2 n + log n log(1/epsilon)) for the more general model of constant-width read-once branching programs with arbitrary variable order [Michael A. Forbes and Zander Kelley, 2018]. Looking beyond read-once AC^0, we also show that our PRG fools read-once AC^0[oplus] with seed length O~(t + log(n/epsilon)), where t is the number of parity gates in the formula. Our construction follows Ajtai and Wigderson's approach of iterated pseudorandom restrictions [Ajtai and Wigderson, 1989]. We assume by recursion that we already have a PRG for depth-d AC^0 formulas. To fool depth-(d + 1) AC^0 formulas, we use the given PRG, combined with a small-bias distribution and almost k-wise independence, to sample a pseudorandom restriction. The analysis of Forbes and Kelley [Michael A. Forbes and Zander Kelley, 2018] shows that our restriction approximately preserves the expectation of the formula. The crux of our work is showing that after poly(log log n) independent applications of our pseudorandom restriction, the formula simplifies in the sense that every gate other than the output has only polylog n remaining children. Finally, as the last step, we use a recent PRG by Meka, Reingold, and Tal [Meka et al., 2019] to fool this simpler formula.
机译:我们为一次读取AC ^ 0提供了一个显式的伪随机发生器(PRG),即基于{wedge,vee,neg}的无限制扇入的恒定深度一次读取公式。 PRG的种子长度为O〜(log(n / epsilon))。以前,只有在深度为2的情况下才知道种子长度接近最佳的PRG [Gopalan等,2012]。对于恒定深度d> 2,最好的先前PRG是Forbes和Kelley最近构造的,种子长度为O〜(log ^ 2 n + log n log(1 / epsilon)),用于更宽的恒定宽度读取模型一次具有任意变量顺序的分支程序[Michael A. Forbes和Zander Kelley,2018年]。除了一次读取AC ^ 0,我们还显示我们的PRG愚弄了一次种子长度为O〜(t + log(n / epsilon))的一次AC ^ 0 [oplus],其中t是其中奇偶门的数量公式。我们的构造遵循Ajtai和Wigderson迭代伪随机限制的方法[Ajtai和Wigderson,1989年]。递归地假设我们已经为深度d AC ^ 0公式提供了PRG。为了欺骗深度(d +1)AC ^ 0公式,我们使用给定的PRG,结合小偏差分布和几乎k方向的独立性,对伪随机约束进行采样。对《福布斯》和《凯利》的分析[Michael A.Forbes和Zander Kelley,2018年]显示,我们的限制大致保留了该公式的期望。我们工作的症结在于,在对伪随机限制进行poly(log log n)独立应用之后,该公式在某种意义上简化了,即除输出之外的每个门都只有polylog n个剩余的子代。最后,作为最后一步,我们使用了Meka,Reingold和Tal的最新PRG [Meka et al。,2019]来欺骗这个更简单的公式。

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