A neural network for monotone variational inequalities with linear constraints

Xing Bao Gao, Lizhi LIAO*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Variational inequality is a uniform approach for many important optimization and equilibrium problems. Based on the necessary and sufficient conditions for the solution, this Letter presents a neural network model for solving linearly constrained variational inequalities. Several sufficient conditions are provided to ensure the asymptotic stability of the proposing network. There is no need to estimate the Lipschitz constant, and no extra parameter is introduced. Since the sufficient conditions provided in this Letter can be easily checked in practice, these new results have both theoretical and application values. The validity and transient behavior of the proposing neural network are demonstrated by some numerical examples.

Original languageEnglish
Pages (from-to)118-128
Number of pages11
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Volume307
Issue number2-3
DOIs
Publication statusPublished - 27 Jan 2003

Scopus Subject Areas

  • Physics and Astronomy(all)

User-Defined Keywords

  • Convergence
  • Neural network
  • Stability
  • Variational inequality

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