A continuous-time neurodynamic algorithm for distributed nonconvex nonsmooth optimization problems with affine equality and nonsmooth convex inequality constraints

被引：3
|
作者：
Yang, Jianyu [1 ]
He, Xing [1 ]
机构：
[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing Key Lab Nonlinear Circuits & Intelligent, Chongqing 400715, Peoples R China
来源：
NEUROCOMPUTING | 2022年 / 507卷
基金：
中国国家自然科学基金;
关键词：
Continuous-time neurodynamic algorithm; Distributed nonsmooth nonconvex  optimization; Finite-time convergence; NEURAL-NETWORKS; CONSENSUS; SYSTEM;
D O I：
10.1016/j.neucom.2022.08.035
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a distributed nonsmooth nonconvex optimization (DNNO) problem with affine inequality and nonsmooth convex inequality constraints is studied. A continuous-time distributed neurodynamic algorithm is proposed to solve this problem. Under the assumed conditions, for any initial state, the solution of distributed neurodynamic algorithm is bounded and globally exists, and will converge to the critical point set of distributed problems in a finite time. Compared with other DNNO algorithms, distributed neurodynamic algorithm has a lower dimension and does not need to satisfy the assumption that the feasible region is bounded. Finally, a series of numerical examples are given to verify the effectiveness of the proposed algorithm.CO 2022 Elsevier B.V. All rights reserved.
引用
收藏
页码：383 / 396
页数：14
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