math.NA · 2026-06-26 · No. 35
Numerical Analysis, 2026-06-26.
2 new papers in math.NA. Titles, authors,
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01 — The papers
2 entries-
01
Hierarchical Muon: Tiled Newton-Schulz Updates for Efficient Muon Optimization
Ziyuan Tang, Tianshi Xu, Yousef Saad, Yuanzhe Xi
math.NA · cs.LG
Muon-type optimizers construct update directions for dense neural-network weights by applying a finite Newton-Schulz map to momentum-gradient matrices. For an $H \times W$ matrix, with $r=\min\{H,W\}$ and $s=\max\{H,W\}$, $K$ steps of the full-matrix Newton-Schulz update require $O(r^2 s K)$ work and couple all rows and columns through repeated Gram matrix products. We introduce Hierarchical Muon (HiMuon), a tiled Newton-Schulz scheme for...
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02
Accelerated sampling using SamAdams variable timesteps and position-adaptive Langevin dynamics
Benedict Leimkuhler, Peter A. Whalley
math.NA · cs.LG · stat.CO
We introduce an accelerated Langevin-based sampling method that is based on two complementary devices: \emph{SamAdams} adaptive timestepping, which automatically shrinks the effective integration step in stiff regions of phase space using a relaxed stiffness monitor, and \emph{position-adaptive Langevin} (PAL) dynamics, which concentrates friction along the local force direction while preserving the canonical distribution as the exact...
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