English(EN) Exact Theory and Optimal Stepsizes for Stochastic Gradient Descent in Smooth Convex Minimization

新理论精确分析了凸优化的SGD步长

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

本文对光滑凸优化中的随机梯度下降（SGD）进行了精确的最坏情况分析，重点关注最终迭代的预期次优性。该研究提供了一个匹配的目标函数和噪声分布，以证明其对任何N个步长计划的理论保证。当方差界足够大时，本文确定了一个独特的、单调递减的、极小极大最优的SGD步长计划，这与确定性设置中看到的加速计划不同。 AI

影响为机器学习中使用的优化算法提供了理论基础。

排序理由关于优化理论的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

Signal score: 1 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
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arXiv cs.LG TIER_1 English(EN) · Benjamin Grimmer, Qining Shen · 2026-11-02 05:00

Exact Theory and Optimal Stepsizes for Stochastic Gradient Descent in Smooth Convex Minimization

arXiv:2610.42048v1 Announce Type: cross Abstract: This work considers the design of stepsize schedules for stochastic gradient descent in smooth convex optimization. Throughout, we assume that gradient noise is conditionally zero-mean with bounded variance. Among problems with di…

本文如何被排名

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Exact Theory and Optimal Stepsizes for Stochastic Gradient Descent in Smooth Convex Minimization

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