A new class of neural networks for NCPs using smooth perturbations of the natural residual function

Jan Harold Alcantara, Jein Shan Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

We present a new class of neural networks for solving nonlinear complementarity problems (NCPs) based on some family of real-valued functions (denoted by ℱ) that can be used to construct smooth perturbations of the level curve defined by ΦNR(x,y)=0, where ΦNR is the natural residual function (also called the “min” function). We introduce two important subclasses of ℱ, which deserve particular attention because of their significantly different theoretical and numerical properties. One of these subfamilies yields a smoothing function for ΦNR, while the other subfamily only yields a smoothing curve for ΦNR(x,y)=0. We also propose a simple framework for generating functions from these subclasses. Using the smoothing approach, we build two types of neural networks and provide sufficient conditions to guarantee asymptotic and exponential stability of equilibrium solutions. Finally, we present extensive numerical experiments to validate the theoretical results and to illustrate the difference in numerical performance of functions from the two subclasses. Numerical comparisons with existing neural networks for NCPs are also demonstrated.

Original languageEnglish
Article number114092
JournalJournal of Computational and Applied Mathematics
Volume407
DOIs
Publication statusPublished - 2022 Jun

Keywords

  • Complementarity problem
  • Neural network
  • Smoothing method
  • Stability

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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