A deterministic annealing neural network for convex programming

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)629-641
Journal / PublicationNeural Networks
Volume7
Issue number4
Publication statusPublished - 1994
Externally publishedYes

Abstract

A recurrent neural network, called a deterministic annealing neural network, is proposed for solving convex programming problems. The proposed deterministic annealing neural network is shown to be capable of generating optimal solutions to convex programming problems. The conditions for asymptotic stability, solution feasibility, and solution optimality are derived. The design methodology for determining design parameters is discussed. Three detailed illustrative examples are also presented to demonstrate the functional and operational characteristics of the deterministic annealing neural network in solving linear and quadratic programs. © 1994.

Research Area(s)

  • Convergence analysis, Convex programming, Recurrent neural network