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Muon optimizer fails on convex Lipschitz functions, study finds

By PulseAugur Editorial · [1 sources] ·

A new paper challenges the theoretical underpinnings of the Muon optimization algorithm, demonstrating that it does not converge on convex Lipschitz functions. The research suggests that Muon's practical success likely stems from smoothness properties not captured by this classical model. While error feedback can restore theoretical convergence, it degrades empirical performance in key deep learning tasks. AI

IMPACT Challenges theoretical understanding of a popular optimization algorithm, potentially impacting future deep learning method development.

RANK_REASON Academic paper analyzing the theoretical convergence properties of an optimization algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Muon optimizer fails on convex Lipschitz functions, study finds

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Academic paper analyzing the theoretical convergence properties of an optimization algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Robert M. Gower ·

    Muon Does Not Converge on Convex Lipschitz Functions

    Muon and its variants have shown strong empirical performance in a variety of deep learning tasks. Existing convergence analyses of Muon rely on smoothness assumptions, though arguably the most successful function class for developing deep learning methods (such as AdaGrad, Shamp…

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