A recurrent neural network for solving nonlinear convex programs subject to linear constraints

Youshen Xia, Jun Wang

Research output: Contribution to journalArticlepeer-review

159 Citations (Scopus)

Abstract

In this paper, we propose a recurrent neural network for solving nonlinear convex programming problems with linear constraints. The proposed neural network has a simpler structure and a lower complexity for implementation than the existing neural networks for solving such problems. It is shown here that the proposed neural network is stable in the sense of Lyapunov and globally convergent to an optimal solution within a finite time under the condition that the objective function is strictly convex. Compared with the existing convergence results, the present results do not require Lipschitz continuity condition on the objective function. Finally, examples are provided to show the applicability of the proposed neural network.

Original languageEnglish
Pages (from-to)379-386
Number of pages8
JournalIEEE Transactions on Neural Networks
Volume16
Issue number2
DOIs
Publication statusPublished - Mar 2005
Externally publishedYes

Keywords

  • Continuous methods
  • Global convergence
  • Linear constraints
  • Recurrent neural networks
  • Strictly convex programming

Fingerprint

Dive into the research topics of 'A recurrent neural network for solving nonlinear convex programs subject to linear constraints'. Together they form a unique fingerprint.

Cite this