A new one-layer neural network for linear and quadratic programming

Xingbao Gao*, Lizhi LIAO

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

52 Citations (Scopus)


In this paper, we present a new neural network for solving linear and quadratic programming problems in real time by introducing some new vectors. The proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem when the objective function is convex on the set defined by equality constraints. Compared with existing one-layer neural networks for quadratic programming problems, the proposed neural network has the least neurons and requires weak stability conditions. The validity and transient behavior of the proposed neural network are demonstrated by some simulation results.

Original languageEnglish
Article number5446295
Pages (from-to)918-929
Number of pages12
JournalIEEE Transactions on Neural Networks
Issue number6
Publication statusPublished - Jun 2010

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • Convergence
  • Linear and quadratic programming
  • Neural network
  • Stability


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