Author

J. Kim

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#artificial intelligence May 2026

Anytime Training with Schedule-Free Spectral Optimization

SF-NorMuon, a schedule-free spectral optimizer that closes the performance gap with tuned AdamW baselines, makes horizon-free optimization more practical, taking a step towards truly open-ended, continual learning.

Anuj Apte, Pranav Deshpande, Niraj Kumar et al. · arXiv.org · 4 citations

Preprint Sep 2026

Near-Optimal Quantum Lower Bounds for Convex Optimization via Fourier Rank

We establish a near-linear quantum query lower bound for high-accuracy convex optimization over an explicit family of $n$-dimensional ellipsoids. We focus on linear optimization with an explicitly given objective, where the feasible set is accessed through a membership oracle. We show that any algorithm that, for every...

Brandon Augustino, Shouvanik Chakrabarti, Enrico Fontana et al. · 1 citation