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 in:	Applied mathematics and computation Vol. 188; no. 2; pp. 1103 - 1114
Main Authors:	Effati, S., Ghomashi, A., Nazemi, A.R.
Format:	Journal Article
Language:	English
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