Improving the matrix multiplication exponent with modern optimization and AlphaEvolve

Abstract:The current best bounds on the matrix multiplication exponent $\omega$ are obtained through a refinement of the laser method called combination loss analysis (Duan et al., 2022; Williams et al., 2024; Alman et al., 2025). In this note, we address the optimization problem at the core of this approach and propose several improvements. First, we reformulate the optimization problem allowing us to solve it in a larger setting than was previously possible. Second, we leverage recent advances in machine learning to design a new optimization algorithm for this problem. Finally, we refine the resulting optimization algorithm with AlphaEvolve. Our combined approach yields an upper bound of $\omega$ < 2.371177, improving the previous best bound of 2.371339.

Data Structures and Algorithms (cs.DS); Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Machine Learning (cs.LG)

arXiv:2608.16884 [cs.DS]

(or arXiv:2608.16884v1 [cs.DS] for this version)

https://doi.org/10.48550/arXiv.2608.16884 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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