SOURCE-LINKED INTELLIGENCE
Nonsmooth Optimization via Orthogonalized Momentum
Modern real application problems involve matrix-valued parameters, yet conventional optimizers treat them as vectors, thereby motivating matrix-aware methods that exploit input-output geometry, such as Muon which orthogonalizes the momentum matrices before parameter updates. Its empirical success raises a conceptual question: can orthogonalized momentum remain effective beyond smooth optimization? This paper studies this question for locally Lipschitz functions using a generalized derivative framework compatible with backpropagation. Our first contribution is to identify a key limitation: for
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T03:08:36.000Z
First collected: 2026-09-20T16:41:15.630Z. This is not the publication date.