English(EN) Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework

李雅普诺夫框架分析随机算法收敛性

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-29 13:41

研究人员发表了一篇论文，详细介绍了一个基于李雅普诺夫的框架，用于分析随机迭代算法的有限时间收敛性。该方法使用广义莫罗包络作为通用的李雅普诺夫函数，适用于各种范数和噪声类型。该框架提供了均方收敛保证，并可扩展到随机梯度下降等算法以及Q学习和时间差分学习等强化学习方法。 AI

影响为分析强化学习和其他随机算法的收敛性提供了一个统一的框架。

排序理由这是一篇在arXiv上发表的研究论文，详细介绍了一种用于随机算法的新分析框架。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Zaiwei Chen, Siva Theja Maguluri · 2026-06-01 04:00

随机迭代算法的非渐近收敛性：一个李雅普诺夫框架

arXiv:2605.31309v1 Announce Type: cross Abstract: We survey Lyapunov-based techniques for the finite-time analysis of stochastic iterative algorithms, also known as stochastic approximation (SA) algorithms, for solving fixed-point equations $\bar{F}(x)=x$, where the operator $\ba…
arXiv stat.ML TIER_1 English(EN) · Siva Theja Maguluri · 2026-05-29 13:41

随机迭代算法的非渐近收敛：一个李雅普诺夫框架

We survey Lyapunov-based techniques for the finite-time analysis of stochastic iterative algorithms, also known as stochastic approximation (SA) algorithms, for solving fixed-point equations $\bar{F}(x)=x$, where the operator $\bar{F}(\cdot)$ can only be accessed through a noisy …