Faster Rectangular Matrix Multiplication by Combination Loss Analysis
Data Structures and Algorithms
2023-12-29 v2 Computational Complexity
Symbolic Computation
Abstract
Duan, Wu and Zhou (FOCS 2023) recently obtained the improved upper bound on the exponent of square matrix multiplication by introducing a new approach to quantify and compensate the ``combination loss" in prior analyses of powers of the Coppersmith-Winograd tensor. In this paper we show how to use this new approach to improve the exponent of rectangular matrix multiplication as well. Our main technical contribution is showing how to combine this analysis of the combination loss and the analysis of the fourth power of the Coppersmith-Winograd tensor in the context of rectangular matrix multiplication developed by Le Gall and Urrutia (SODA 2018).
Cite
@article{arxiv.2307.06535,
title = {Faster Rectangular Matrix Multiplication by Combination Loss Analysis},
author = {François Le Gall},
journal= {arXiv preprint arXiv:2307.06535},
year = {2023}
}
Comments
35 pages; v2: minor corrections; accepted to SODA 2024