A subgradient-based neurodynamic algorithm to constrained nonsmooth nonconvex interval-valued optimization

Jingxin Liu

doi:10.1016/j.neunet.2023.01.012

A subgradient-based neurodynamic algorithm to constrained nonsmooth nonconvex interval-valued optimization

2023-03-01

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刘京昕 (Jingxin Liu)

廖

廖晓峰 (Xiaofeng Liao) *

DOI：10.1016/j.neunet.2023.01.012

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In this paper, a subgradient-based neurodynamic algorithm is presented to solve the nonsmooth nonconvex interval-valued optimization problem with both partial order and linear equality con-straints, where the interval-valued objective function is nonconvex, and interval-valued partial order constraint functions are convex. The designed neurodynamic system is constructed by a differential inclusion with upper semicontinuous right-hand side, whose calculation load is reduced by relieving penalty parameters estimation and complex matrix inversion. Based on nonsmooth analysis and the extension theorem of the solution of differential inclusion, it is obtained that the global existence and boundedness of state solution of neurodynamic system, as well as the asymptotic convergence of state solution to the feasible region and the set of LU-critical points of interval-valued nonconvex optimization problem. Several numerical experiments and the applications to emergency supplies distribution and nondeterministic fractional continuous static games are solved to illustrate the applicability of the proposed neurodynamic algorithm.(c) 2023 Elsevier Ltd. All rights reserved.

Keywords:

Subgradient

Neurodynamic algorithm

Nonsmooth nonconvex

Interval -valued optimization

Asymptotic convergence

A subgradient-based neurodynamic algorithm to constrained nonsmooth nonconvex interval-valued optimization

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