A neurodynamic approach to nonsmooth constrained pseudoconvex optimization problem. (April 2020)

• Record Type: Journal Article
• Authors: Xu, Chen; Chai, Yiyuan; Qin, Sitian; Wang, Zhenkun; Feng, Jiqiang
• Is Part Of: Neural networks. Volume 124(2020)
• Page Start: 180
• Page End: 192
• Publication Date: 2020-04
• Subjects: Nonsmooth pseudoconvex optimization -- Neurodynamic approach -- Lyapunov function -- Global convergence; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2019.12.015
• Languages: English
• ISSNs: 0893-6080

Abstract

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.