Faster Matrix Multiplication via Asymmetric Hashing

October 18, 2022 ยท Declared Dead ยท ๐Ÿ› IEEE Annual Symposium on Foundations of Computer Science

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Authors Ran Duan, Hongxun Wu, Renfei Zhou arXiv ID 2210.10173 Category cs.DS: Data Structures & Algorithms Citations 142 Venue IEEE Annual Symposium on Foundations of Computer Science Last Checked 1 month ago
Abstract
Fast matrix multiplication is one of the most fundamental problems in algorithm research. The exponent of the optimal time complexity of matrix multiplication is usually denoted by $ฯ‰$. This paper discusses new ideas for improving the laser method for fast matrix multiplication. We observe that the analysis of higher powers of the Coppersmith-Winograd tensor [Coppersmith & Winograd 1990] incurs a "combination loss", and we partially compensate for it using an asymmetric version of CW's hashing method. By analyzing the eighth power of the CW tensor, we give a new bound of $ฯ‰<2.371866$, which improves the previous best bound of $ฯ‰<2.372860$ [Alman & Vassilevska Williams 2020]. Our result breaks the lower bound of $2.3725$ in [Ambainis, Filmus & Le Gall 2015] because of the new method for analyzing component (constituent) tensors.
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