A continuous-time neurodynamic approach and its discretization for distributed convex optimization over multi-agent systems

作     者：Wen, Xingnan Luan, Linhua Qin, Sitian 

作者机构：Harbin Inst Technol Dept Math Weihai Peoples R China 

出 版 物：《NEURAL NETWORKS》 (神经网络)

年 卷 期：2021年第143卷

页      面：52-65页

核心收录：

学科分类：1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：national science foundation [61773136  11871178] 

主　　题：Distributed optimization problem Neurodynamic approach Penalty method Multi-agent systems Convergence 

摘      要：Distributed optimization problem (DOP) over multi-agent systems, which can be described by minimizing the sum of agents local objective functions, has recently attracted widespread attention owing to its applications in diverse domains. In this paper, inspired by penalty method and subgradient descent method, a continuous-time neurodynamic approach is proposed for solving a DOP with inequality and set constraints. The state of continuous-time neurodynamic approach exists globally and converges to an optimal solution of the considered DOP. Comparisons reveal that the proposed neurodynamic approach can not only resolve more general convex DOPs, but also has lower dimension of solution space. Additionally, the discretization of the neurodynamic approach is also introduced for the convenience of implementation in practice. The iteration sequence of discrete-time method is also convergent to an optimal solution of DOP from any initial point. The effectiveness of the neurodynamic approach is verified by simulation examples and an application in L-1-norm minimization problem in the end. (C) 2021 Elsevier Ltd. All rights reserved.