凸-凹双线性鞍点问题的二阶主-双动力系统

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
Xin He, Rong Hu, Yaping Fang
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引用次数: 0

摘要

凸凹双线性鞍点问题包含许多重要的凸优化模型,这些模型在广泛的应用中出现。现有的用于鞍点问题的初等二元动力学系统大多基于一阶常微分方程(ODE),在凸情况下只拥有({mathcal {O}}(1/t)\) 收敛率,而快速收敛率分析总是需要一些额外的假设,如强凸性。在本文中,我们基于二阶 ODEs,考虑了一个凸-凹双线性鞍点问题的一般惯性原始双动力系统,该系统具有阻尼、缩放和外推系数。通过 Lyapunov 分析方法,在适当的假设条件下,我们研究了所提出的动力学系统的原始双间隙和速度的收敛速率以及轨迹的有界性。在特殊参数下,我们的结果可以恢复波利雅克重球加速方案和涅斯捷罗夫加速方案。我们还提供了数值示例来支持我们的理论主张。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Second Order Primal–Dual Dynamical System for a Convex–Concave Bilinear Saddle Point Problem

A Second Order Primal–Dual Dynamical System for a Convex–Concave Bilinear Saddle Point Problem

The class of convex–concave bilinear saddle point problems encompasses many important convex optimization models arising in a wide array of applications. The most of existing primal–dual dynamical systems for saddle point problems are based on first order ordinary differential equations (ODEs), which only own the \({\mathcal {O}}(1/t)\) convergence rate in the convex case, and fast convergence rate analysis always requires some additional assumption such as strong convexity. In this paper, based on second order ODEs, we consider a general inertial primal–dual dynamical system, with damping, scaling and extrapolation coefficients, for a convex–concave bilinear saddle point problem. By the Lyapunov analysis approach, under appropriate assumptions, we investigate the convergence rates of the primal–dual gap and velocities, and the boundedness of the trajectories for the proposed dynamical system. With special parameters, our results can recover the Polyak’s heavy ball acceleration scheme and Nesterov’s acceleration scheme. We also provide numerical examples to support our theoretical claims.

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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