On Computing Multilinear Polynomials Using Multi-r-ic Depth Four Circuits

IF 0.8 Q3 COMPUTER SCIENCE, THEORY & METHODS ACM Transactions on Computation Theory Pub Date : 2020-03-10 DOI:10.1145/3460952
S. Chillara
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引用次数: 2

Abstract

In this article, we are interested in understanding the complexity of computing multilinear polynomials using depth four circuits in which the polynomial computed at every node has a bound on the individual degree of r≥ 1 with respect to all its variables (referred to as multi-r-ic circuits). The goal of this study is to make progress towards proving superpolynomial lower bounds for general depth four circuits computing multilinear polynomials, by proving better bounds as the value of r increases. Recently, Kayal, Saha and Tavenas (Theory of Computing, 2018) showed that any depth four arithmetic circuit of bounded individual degree r computing an explicit multilinear polynomial on nO(1) variables and degree d must have size at least (n/r1.1)Ω(√d/r). This bound, however, deteriorates as the value of r increases. It is a natural question to ask if we can prove a bound that does not deteriorate as the value of r increases, or a bound that holds for a larger regime of r. In this article, we prove a lower bound that does not deteriorate with increasing values of r, albeit for a specific instance of d = d(n) but for a wider range of r. Formally, for all large enough integers n and a small constant η, we show that there exists an explicit polynomial on nO(1) variables and degree Θ (log2 n) such that any depth four circuit of bounded individual degree r ≤ nη must have size at least exp(Ω(log2 n)). This improvement is obtained by suitably adapting the complexity measure of Kayal et al. (Theory of Computing, 2018). This adaptation of the measure is inspired by the complexity measure used by Kayal et al. (SIAM J. Computing, 2017).
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用多ic深度四电路计算多线性多项式
在本文中,我们感兴趣的是理解使用深度四电路计算多线性多项式的复杂性,其中在每个节点计算的多项式相对于其所有变量(称为多r-ic电路)的单个度r≥1有一个界。本研究的目标是通过证明r值增加时更好的边界,在证明一般深度四电路计算多线性多项式的超多项式下界方面取得进展。最近,Kayal, Saha和Tavenas (Theory of Computing, 2018)表明,在nO(1)个变量和d次上计算显式多线性多项式的任何有限个体度r的深度四算术电路必须具有至少(n/r1.1)Ω(√d/r)的大小。然而,随着r值的增加,这个界限会变差。一个自然的问题是,我们是否能证明一个不随r值的增加而恶化的边界,或者一个适用于更大范围r的边界。在本文中,我们证明了一个不随r值的增加而恶化的下界,尽管是针对d = d(n)的特定实例,但适用于更广泛的r范围。形式上,对于所有足够大的整数n和一个小的常数η,我们证明了在nO(1)个变量和阶次Θ (log2 n)上存在一个显式多项式,使得任何有界的单个阶次r≤nη的深度四回路必须至少具有exp(Ω(log2 n))的大小。这种改进是通过适当地采用Kayal等人的复杂性度量来获得的(Theory of Computing, 2018)。这种对度量的适应受到Kayal等人使用的复杂性度量的启发(SIAM J. Computing, 2017)。
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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来源期刊
ACM Transactions on Computation Theory
ACM Transactions on Computation Theory COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
2.30
自引率
0.00%
发文量
10
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