矩阵乘法指数的普遍性

Lek-Heng Lim, Ke Ye
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引用次数: 7

摘要

矩阵乘法的渐近指数是最小的ω,使得人们可以在O(nω+ε)内乘以两个n × n矩阵或对一个n × n矩阵求逆——对于ε > 0的复杂度任意小。在复杂性理论和数值线性代数中最大的开放问题之一是它的假设值ω = 2。这篇文章是关于ω的通用性。我们将证明ω不仅是矩阵代数中乘积运算的渐近指数,而且是各种无穷族的李代数、Jordan代数和Clifford代数的渐近指数。此外,我们将证明ω不仅是矩阵乘积和反演的渐近指数,而且是任何矩阵值多项式和矩阵变量的有理函数的求值的渐近指数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ubiquity of the exponent of matrix multiplication
The asymptotic exponent of matrix multiplication is the smallest ω such that one may multiply two n × n matrices or invert an n × n matrix in O(nω+ε)-complexity for ε > 0 arbitrarily small. One of the biggest open problem in complexity theory and numerical linear algebra is its conjectured value ω = 2. This article is about the universality of ω. We will show that ω is not only the asymptotic exponent for the product operation in matrix algebras but also that for various infinite families of Lie algebras, Jordan algebras, and Clifford algebras. In addition, we will show that ω is not just the asymptotic exponent for matrix product and inversion but also that for the evaluation of any matrix-valued polynomial and rational functions of matrix variables.
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