Faster Matrix Multiplication via Asymmetric Hashing
Ran Duan, Hongxun Wu, Renfei Zhou
摘要
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 . 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 , which improves the previous best bound of [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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引用它的顶会 Paper40
- A Refined Laser Method and Faster Matrix MultiplicationJosh Alman, Virginia Vassilevska WilliamsSODA 2021 · 被引用 275 次
- New Bounds for Matrix Multiplication: from Alpha to OmegaVirginia Vassilevska Williams, Yinzhan Xu, Zixuan Xu, Renfei ZhouSODA 2024 · 被引用 90 次
- Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear TimeYuzhou Gu, Zhao Song, Junze Yin, Lichen ZhangICLR 2024 · 被引用 37 次
- More Asymmetry Yields Faster Matrix MultiplicationJosh Alman, Ran Duan, Virginia Vassilevska Williams, Yinzhan Xu 等SODA 2025 · 被引用 35 次
- Faster Algorithms for Structured John Ellipsoid ComputationYang Cao, Xiaoyu Li, Zhao Song, Xin Yang 等NeurIPS 2025 · 被引用 33 次
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