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English(EN) A Unified Lyapunov-IQC Framework for Uniform Stability of Smooth Quadratic First-Order Accelerated Optimizers

新框架统一Lyapunov-IQC以实现加速优化器稳定性

研究人员开发了一个新的框架,使用Lyapunov函数和积分二次约束(IQC)来分析加速一阶优化算法的统一稳定性。该方法将先前关于随机梯度下降(SGD)的工作扩展到Nesterov加速梯度(NAG)等方法,这些方法由于动量动力学而更加复杂。该框架将优化器建模为反馈互连,并使用可通过半定规划(SDP)求解的线性矩阵不等式(LMIs)来认证稳定性,为验证优化算法提供了一种模块化方法。 AI

影响 为理解和认证机器学习中使用的优化算法的稳定性提供了更强大的理论基础。

排序理由 该集群包含一篇详细介绍分析优化器新理论框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新框架统一Lyapunov-IQC以实现加速优化器稳定性

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该集群包含一篇详细介绍分析优化器新理论框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Don Li, Dacian Daescu ·

    光滑二次一阶加速优化器的统一Lyapunov-IQC框架用于均匀稳定性

    arXiv:2605.08488v2 Announce Type: replace-cross Abstract: We develop a unified Lyapunov-integral quadratic constraint (IQC) framework for establishing uniform stability of first-order accelerated optimization algorithms in the $\beta$-smooth and $\gamma$-strongly convex regime. C…