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New research outlines SGD preconditioner design for stability and noise reduction

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv details design criteria for stochastic gradient descent (SGD) preconditioners, focusing on local conditioning, noise floors, and basin stability. The paper derives bounds where convergence rate and noise floor are influenced by a symmetric positive definite matrix M. For nonconvex objectives, it establishes a preconditioner-dependent basin-stability guarantee, particularly relevant for Scientific Machine Learning (SciML) applications where physical fidelity and numerical stability are crucial. Experiments on diagnostic and SciML benchmarks validate the proposed design principle of choosing M to improve local conditioning while reducing noise. AI

IMPACT Provides theoretical insights into optimizing machine learning model training, potentially leading to more stable and efficient learning processes.

RANK_REASON This is a research paper published on arXiv detailing theoretical and experimental findings in optimization algorithms for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research outlines SGD preconditioner design for stability and noise reduction

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This is a research paper published on arXiv detailing theoretical and experimental findings in optimization algorithms for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mitchell Scott, Tianshi Xu, Ziyuan Tang, Alexandra Pichette-Emmons, Qiang Ye, Yousef Saad, Yuanzhe Xi ·

    Design Criteria for SGD Preconditioners: Local Conditioning, Noise Floors, and Basin Stability

    arXiv:2511.19716v3 Announce Type: replace-cross Abstract: Stochastic Gradient Descent (SGD) often slows in the late stage of training due to anisotropic curvature and gradient noise. We analyze preconditioned SGD in the geometry induced by a symmetric positive definite matrix $\m…

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