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FOCS2021顶会

Fooling Constant-Depth Threshold Circuits (Extended Abstract)

Pooya Hatami, William M. Hoza, Avishay Tal, Roei Tell

2021年份
4被引次数
2顶会引用

摘要

We present new constructions of pseudorandom generators (PRGs) for two of the most widely studied non-uniform circuit classes in complexity theory. Our main result is a construction of the first non-trivial PRG for linear threshold (LTF) circuits of arbitrary constant depth and super-linear size. This PRG fools circuits with depthd∈Nd\in\mathbb{N}andn1+δn^{1+\delta}wires, whereδ=2−O(d)\delta=2^{-O(d)}, using seed lengthO(n1−δ)O(n^{1-\delta})and with error2−nδ2^{-n^{\delta}}. This tightly matches the best known lower bounds for this circuit class. As a consequence of our result, all the known hardness for LTF circuits has now effectively been translated into pseudorandomness. This brings the extensive effort in the last decade to construct PRGs and deterministic circuit-analysis algorithms for this class to the point where any subsequent improvement would yield breakthrough lower bounds. Our second contribution is a PRG for De Morgan formulas of sizesswhose seed length iss1/3+o(1)⋅polylog(1/ϵ)s^{1/3+o(1)}\cdot\text{polylog}(1/\epsilon)for errorϵ\epsilon. In particular, our PRG can fool formulas of sub-cubic sizes=n3−Ω(1)s=n^{3-\Omega(1)}with an exponentially small errorϵ=exp⁡(−nΩ(1))\epsilon=\exp(-n^{\Omega(1)}). This significantly improves the inverse-polynomial error of the previous state-of-the-art for such formulas by Impagliazzo, Meka, and Zuckerman (FOCS 2012, JACM 2019), and again tightly matches the best currently-known lower bounds for this class. In both settings, a key ingredient in our constructions is a pseudorandom restriction procedure that has tiny failure probability, but simplifies the function to a non-natural “hybrid computational model” that combines several computational models. As part of our proofs we also construct “extremely low-error” PRGs for related circuit classes; for example, we construct a PRG for arbitrary functions ofssLTFs that can handle even the extreme setting of parameterss=n/polylog(n)s=n/\text{polylog}(n)andϵ=2−n/polylog(n)\epsilon=2^{-n/\text{polylog}(n)}.

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