RT by @GoogleDeepMind: Matrix multiplication is the basic computational operation that powers…
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TL;DR - Google DeepMind and academic collaborators announced a new upper bound on the matrix multiplication exponent, ω < 2.371177, achieved with help from the Gemini-powered AlphaEvolve coding agent. The result advances a longstanding complexity-theory problem underlying computational workloads including AI.
- Matrix multiplication’s theoretically fastest asymptotic complexity remains unknown.
- The new result improves the best known upper bound for ω.
- The work builds on combination loss analysis, a refinement of the laser method.
- AlphaEvolve contributed to the team effort, highlighting AI coding agents’ potential for mathematical and algorithmic discovery.
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RT by @GoogleDeepMind: Matrix multiplication is the basic computational operation that powers…
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TL;DR - Google DeepMind and academic collaborators announced a new upper bound on the matrix multiplication exponent, ω < 2.371177, achieved with help from the Gemini-powered AlphaEvolve coding agent. The result advances a longstanding complexity-theory problem underlying computational workloads including AI.
- Matrix multiplication’s theoretically fastest asymptotic complexity remains unknown.
- The new result improves the best known upper bound for ω.
- The work builds on combination loss analysis, a refinement of the laser method.
- AlphaEvolve contributed to the team effort, highlighting AI coding agents’ potential for mathematical and algorithmic discovery.