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The Precision Wall: An Optimizer-Dependent Boundary for Optimization under Power-Law Measurement Spectra

Paper recorded by Signals 4 on 2026-10-29 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.LG · 机器学习 · first seen 2026-10-30

Abstract

In low-rank matrix sensing of a rank-$D$ factor $U^\ast \in \mathbb{R}^{V \times D}$, replacing isotropic Gaussian designs with power-law measurement covariance $Σ= \mathrm{diag}(j^{-s})_{j=1}^V$ leaves the Burer--Monteiro landscape free of spurious local minima, yet division-normalised first-order optimizers stall above a critical exponent $s^\ast$. We trace this failure to a numerical precision

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#81 most recent of 703 cs.LG papers we have recorded · ↑ newer: Fast and accurate neural triage decoding of ultra-high-rate quantum LD · ↓ older: Preserving Utility in Continual LLM Unlearning via Orthogonal Projecti
Cite this page: The Precision Wall: An Optimizer-Dependent Boundary for Optimization under Power-Law Measurement Spectra: the #81 most recent of 703 cs.LG papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/the-precision-wall-an-optimizer-dependent-boundary-for-optimization-under-power-.html
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