A neurodynamic approach to nonsmooth constrained pseudoconvex optimization problem

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

1 Scopus Citations
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Author(s)

  • Chen Xu
  • Yiyuan Chai
  • Sitian Qin
  • Zhenkun Wang
  • Jiqiang Feng

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)180-192
Journal / PublicationNeural Networks
Volume124
Online published30 Dec 2019
Publication statusPublished - Apr 2020

Abstract

This paper presents a new neurodynamic approach for solving the constrained pseudoconvex optimization problem based on more general assumptions. The proposed neural network is equipped with a hard comparator function and a piecewise linear function, which make the state solution not only stay in the feasible region, but also converge to an optimal solution of the constrained pseudoconvex optimization problem. Compared with other related existing conclusions, the neurodynamic approach here enjoys global convergence and lower dimension of the solution space. Moreover, the neurodynamic approach does not depend on some additional assumptions, such as the feasible region is bounded, the objective function is lower bounded over the feasible region or the objective function is coercive. Finally, both numerical illustrations and simulation results in support vector regression problem show the well performance and the viability of the proposed neurodynamic approach.

Research Area(s)

  • Global convergence, Lyapunov function, Neurodynamic approach, Nonsmooth pseudoconvex optimization

Citation Format(s)

A neurodynamic approach to nonsmooth constrained pseudoconvex optimization problem. / Xu, Chen; Chai, Yiyuan; Qin, Sitian; Wang, Zhenkun; Feng, Jiqiang.

In: Neural Networks, Vol. 124, 04.2020, p. 180-192.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal