莫罗扫描过程的不精确追赶算法

IF 1.7 2区 数学 Q2 MATHEMATICS, APPLIED
Juan Guillermo Garrido, Maximiliano Lioi, Emilio Vilches
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引用次数: 0

摘要

在本文中,我们开发了一个不精确版本的扫描过程追赶算法。我们定义了一个近似投影的新概念,它与任何近似精确投影的数值方法兼容,因为这个新概念不局限于集合内。我们提供了新的近似投影的几个性质,使我们能够证明不精确追赶算法在三种一般框架下的收敛性:准正则移动集、亚光滑移动集和仅仅封闭集。此外,我们将我们的数值结果应用于互补动力系统,特别是具有理想二极管的电路。在这种情况下,我们使用原始对偶优化方法实现非精确追赶算法,该方法通常不一定保证可行点。我们的结果通过一个具有理想二极管的电路来说明。我们的结果恢复了文献中的经典存在结果,并为扫描过程的数值模拟提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Inexact Catching-Up Algorithm for Moreau’s Sweeping Processes

Inexact Catching-Up Algorithm for Moreau’s Sweeping Processes

Inexact Catching-Up Algorithm for Moreau’s Sweeping Processes

In this paper, we develop an inexact version of the catching-up algorithm for sweeping processes. We define a new notion of approximate projection, which is compatible with any numerical method for approximating exact projections, as this new notion is not restricted to remain strictly within the set. We provide several properties of the new approximate projections, which enable us to prove the convergence of the inexact catching-up algorithm in three general frameworks: prox-regular moving sets, subsmooth moving sets, and merely closed sets. Additionally, we apply our numerical results to address complementarity dynamical systems, particularly electrical circuits with ideal diodes. In this context, we implement the inexact catching-up algorithm using a primal-dual optimization method, which typically does not necessarily guarantee a feasible point. Our results are illustrated through an electrical circuit with ideal diodes. Our results recover classical existence results in the literature and provide new insights into the numerical simulation of sweeping processes.

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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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