Application of projection neural network in solving convex programming problems

In this paper we present that solution of convex programming problems is equivalent with solution of projection formulation, then we introduce neural network models for solving projection formulation and analysis stability conditions and convergence. Simulation shows that the introduced neural netwo...

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Bibliographic Details
Published inApplied mathematics and computation Vol. 188; no. 2; pp. 1103 - 1114
Main Authors Effati, S., Ghomashi, A., Nazemi, A.R.
Format Journal Article
LanguageEnglish
Published New York, NY Elsevier Inc 15.05.2007
Elsevier
Subjects
Calculus of variations and optimal control
Convex programming
Exact sciences and technology
Global analysis, analysis on manifolds
Mathematical analysis
Mathematics
Neural network
Numerical analysis
Numerical analysis. Scientific computation
Projection operator
Sciences and techniques of general use
Stability
Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds
Variational inequality
Projection operator
Stability
Neural network
Variational inequality
Convex programming
Numerical analysis
Applied mathematics
Problem solving
Numerical stability
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Summary:In this paper we present that solution of convex programming problems is equivalent with solution of projection formulation, then we introduce neural network models for solving projection formulation and analysis stability conditions and convergence. Simulation shows that the introduced neural network is effective in solving convex programming problems.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2006.10.088