arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2404.03383math.OC
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用于分析连续时间加速梯度方法的统一 Euler–Lagrange 系统

A unified Euler--Lagrange system for analyzing continuous-time accelerated gradient methods

Mitsuru Toyoda, Akatsuki Nishioka, Mirai Tanaka

AI总结:

本文构建统一 Euler–Lagrange 系统与 Lyapunov 分析框架,涵盖多种连续时间加速梯度 ODE,并推导参数条件、改进收敛速率及不可微情形证明。

摘要:

本文针对光滑凸优化中加速梯度方法的连续时间模型,提出了一个 Euler–Lagrange 系统,并给出了相应的基于 Lyapunov 函数的收敛性分析框架。近年来,带有阻尼项的常微分方程(ODE)已被用于直观解释加速梯度方法,研究者也在探索设计能够描述各种独立 ODE 模型的统一模型。在已有报告中,导出 Euler–Lagrange 方程的 Lagrange 函数以及用于收敛性分析的 Lyapunov 函数,都是针对每个 ODE 分别提出的。本文提出一个统一的 Euler–Lagrange 系统及其 Lyapunov 函数,以涵盖现有多种模型。在利用该 Lyapunov 函数进行收敛性分析时,本文推导了 Lagrange 函数与 Lyapunov 函数中参数必须满足的条件,而用于提升收敛速率的参数设计会自然产生那些神秘的阻尼系数。特别地,对称 Bregman 散度可带来更宽松的参数条件,并由此获得改进的收敛速率。作为本研究的应用,对 Lyapunov 函数稍作修改,即可为不可微目标函数最小化中采用光滑近似的 ODE 建立类似的收敛证明。

英文摘要:

This paper presents an Euler--Lagrange system for a continuous-time model of the accelerated gradient methods in smooth convex optimization and proposes an associated Lyapunov-function-based convergence analysis framework. Recently, ordinary differential equations (ODEs) with dumping terms have been developed to intuitively interpret the accelerated gradient methods, and the design of unified model describing the various individual ODE models have been examined. In existing reports, the Lagrangian, which results in the Euler-Lagrange equation, and the Lyapunov function for the convergence analysis have been separately proposed for each ODE. This paper proposes a unified Euler--Lagrange system and its Lyapunov function to cover the existing various models. In the convergence analysis using the Lyapunov function, a condition that parameters in the Lagrangian and Lyapunov function must satisfy is derived, and a parameter design for improving the convergence rate naturally results in the mysterious dumping coefficients. Especially, a symmetric Bregman divergence can lead to a relaxed condition of the parameters and a resulting improved convergence rate. As an application of this study, a slight modification in the Lyapunov function establishes the similar convergence proof for ODEs with smooth approximation in nondifferentiable objective function minimization.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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