Solving nonlinear complementarity problems with neural networks: A reformulation method approach

Li Zhi Liao, Houduo Qi, Liqun Qi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

47 Citations (Scopus)


In this paper, we present a neural network approach for solving nonlinear complementarity problems. The neural network model is derived from an unconstrained minimization reformulation of the complementarity problem. The existence and the convergence of the trajectory of the neural network are addressed in detail. In addition, we also explore the stability properties, such as the stability in the sense of Lyapunov, the asymptotic stability and the exponential stability, for the neural network model. The theory developed here is also valid for neural network models derived from a number of reformulation methods for nonlinear complementarity problems. Simulation results are also reported.
Original languageEnglish
Pages (from-to)343-359
Number of pages17
JournalJournal of Computational and Applied Mathematics
Issue number1-2
Publication statusPublished - 1 Jun 2001
Externally publishedYes


  • Neural network
  • Nonlinear complementarity problem
  • Reformulation
  • Stability

ASJC Scopus subject areas

  • Applied Mathematics
  • Computational Mathematics
  • Numerical Analysis


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