Improving the matrix multiplication exponent with modern optimization and AlphaEvolve
The current best bounds on the matrix multiplication exponent $ω$ 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 opt
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- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- LinkedLinked via arxiv author · 85%Emilien Dupont →
“Improving the matrix multiplication exponent with modern optimization and AlphaEvolve”
- LinkedLinked via arxiv author · 85%Marvin Eisenberger →
“Improving the matrix multiplication exponent with modern optimization and AlphaEvolve”
- LinkedLinked via arxiv author · 85%Borislav Kozlovskii →
“Improving the matrix multiplication exponent with modern optimization and AlphaEvolve”
- LinkedLinked via arxiv author · 85%Abbas Mehrabian →
“Improving the matrix multiplication exponent with modern optimization and AlphaEvolve”
- LinkedLinked via arxiv author · 85%Francisco J. R. Ruiz →
“Improving the matrix multiplication exponent with modern optimization and AlphaEvolve”
- LinkedLinked via arxiv author · 85%Abigail See →
“Improving the matrix multiplication exponent with modern optimization and AlphaEvolve”
- LinkedLinked via arxiv author · 85%Renfei Zhou →
“Improving the matrix multiplication exponent with modern optimization and AlphaEvolve”