A proximal neurodynamic model for solving inverse mixed variational inequalities

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

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Detail(s)

Original languageEnglish
Pages (from-to)1-9
Journal / PublicationNeural Networks
Volume138
Online published27 Jan 2021
Publication statusPublished - Jun 2021

Abstract

This paper proposes a proximal neurodynamic model (PNDM) for solving inverse mixed variational inequalities (IMVIs) based on the proximal operator. It is shown that the PNDM has a unique continuous solution under the condition of Lipschitz continuity (L-continuity). It is also shown that the equilibrium point of the proposed PNDM is asymptotically stable or exponentially stable under some mild conditions. Finally, three numerical examples are presented to illustrate effectiveness of the proposed PNDM.

Research Area(s)

  • Exponential stability, Inverse mixed variational inequalities, Lipschitz continuous, Proximal neurodynamic model, Strong monotonicity

Citation Format(s)

A proximal neurodynamic model for solving inverse mixed variational inequalities. / Ju, Xingxing; Li, Chuandong; He, Xing et al.
In: Neural Networks, Vol. 138, 06.2021, p. 1-9.

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