DeepMind uses AlphaEvolve to optimize matrix multiplication exponent to 2.371177
rohanpaul_ai · x · 2026-08-21
Google DeepMind released a paper demonstrating how AI (AlphaEvolve) assisted in optimizing code to improve the theoretical upper bound of the matrix multiplication exponent $\omega$ from 2.371339 to 2.371177.
Key contributions include:
- Problem Reformulation: Enabling the solution of the core optimization problem in a larger setting than previously possible.
- ML Algorithm Design: Leveraging recent advances in machine learning to design a new optimization algorithm.
- AlphaEvolve Iteration: Using AlphaEvolve to iteratively modify and improve the optimization algorithm.
The research team rigorously verified the results, marking a breakthrough application of AI in fundamental algorithmic theory optimization.
Related event: DeepMind Improves Matrix Multiplication Bound with AlphaEvolve(2 posts)→
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