Author

Taira Tsuchiya

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Preprint Aug 2026

Muon with Finite Newton-Schulz: The Smoothing Benefit in Nonsmooth Nonconvex Optimization

Muon has emerged as a strong optimizer for the matrix-valued parameters in large language model pretraining, approximately orthogonalizing its momentum with a few Newton-Schulz iterations. Existing theory either replaces this iteration with the exact polar factor it approximates, or treats its finite depth as an approx...

Ming-Yi Li, Taira Tsuchiya · 4 citations · ⚡1

Conference Open access Sep 2026

A Sampling-Based Relaxation Approach to Contextual Inverse Optimization

Decision-making pipelines increasingly rely on prediction models whose outputs serve as inputs to downstream optimization problems. Decision-Focused Learning (DFL) has emerged as a promising approach to training such models by directly optimizing decision quality rather than predictive accuracy alone. While most existi...

Yasunari Hikima, N. Kamiyama, Shinsaku Sakaue et al. · Proceedings of the Thirty-Fi... · 0 citations