带阻尼的带电粒子动力学快速优化

IF 2.6 1区 数学 Q1 MATHEMATICS, APPLIED SIAM Journal on Optimization Pub Date : 2024-07-02 DOI:10.1137/23m1599045
Weiping Yan, Yu Tang, Gonglin Yuan
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引用次数: 0

摘要

SIAM 优化期刊》,第 34 卷第 3 期,第 2287-2313 页,2024 年 9 月。 摘要本文从磁场中自主耗散惯性连续动力学的角度出发,对凸优化问题的加速二阶方法进行了收敛性分析。与带阻尼的经典重球模型不同,我们考虑的是带电粒子在磁场中的运动模型,涉及线性渐近消失阻尼。它是一个耦合常微分系统,在重球系统中加入了磁耦合项 [math]。为了开发快速优化方法,我们的第一个贡献是通过巴拿赫定点定理证明了该系统在某些正则性条件下光滑解的全局存在性和唯一性。我们的第二个贡献是通过磁场下惯性动力学的离散时间版本,建立了涉及惯性特征的相应算法的收敛率。同时,建立了重球模型与磁场中带电粒子运动模型之间算法的联系。
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Fast Optimization of Charged Particle Dynamics with Damping
SIAM Journal on Optimization, Volume 34, Issue 3, Page 2287-2313, September 2024.
Abstract. In this paper, the convergence analysis of accelerated second-order methods for convex optimization problems is developed from the point of view of autonomous dissipative inertial continuous dynamics in the magnetic field. Different from the classical heavy ball model with damping, we consider the motion of a charged particle in a magnetic field model involving the linear asymptotic vanishing damping. It is a coupled ordinary differential system by adding the magnetic coupled term [math] to the heavy ball system with [math]. In order to develop fast optimization methods, our first contribution is to prove the global existence and uniqueness of a smooth solution under certain regularity conditions of this system via the Banach fixed point theorem. Our second contribution is to establish the convergence rate of corresponding algorithms involving inertial features via discrete time versions of inertial dynamics under the magnetic field. Meanwhile, the connection of algorithms between the heavy ball model and the motion of a charged particle in a magnetic field model is established.
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来源期刊
SIAM Journal on Optimization
SIAM Journal on Optimization 数学-应用数学
CiteScore
5.30
自引率
9.70%
发文量
101
审稿时长
6-12 weeks
期刊介绍: The SIAM Journal on Optimization contains research articles on the theory and practice of optimization. The areas addressed include linear and quadratic programming, convex programming, nonlinear programming, complementarity problems, stochastic optimization, combinatorial optimization, integer programming, and convex, nonsmooth and variational analysis. Contributions may emphasize optimization theory, algorithms, software, computational practice, applications, or the links between these subjects.
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