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

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...

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Bibliographic Details
Published in:	Neurocomputing (Amsterdam) Vol. 507; pp. 383 - 396
Main Authors:	Yang, Jianyu, He, Xing
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.10.2022
Subjects:	Continuous-time neurodynamic algorithm Distributed nonsmooth nonconvex optimization Finite-time convergence Distributed nonsmooth nonconvex optimization Continuous-time neurodynamic algorithm Finite-time convergence
ISSN:	0925-2312, 1872-8286
Online Access:	Get full text
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Summary:	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.
ISSN:	0925-2312 1872-8286
DOI:	10.1016/j.neucom.2022.08.035