A Projection Neural Network for Constrained Quadratic Minimax Optimization

Qingshan Liu, Jun Wang

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)


This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the proposed neural network. Compared with some of the existing neural networks for quadratic minimax optimization, the proposed neural network in this paper is capable of solving more general constrained quadratic minimax optimization problems, and the designed neural network does not include any parameter. Moreover, the neural network has lower model complexities, the number of state variables of which is equal to that of the dimension of the optimization problems. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network.

Original languageEnglish
Article number7103358
Pages (from-to)2891-2900
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number11
Publication statusPublished - 1 Nov 2015
Externally publishedYes


  • Global convergence
  • Lyapunov stability
  • projection neural network
  • quadratic minimax optimization


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