A Neural Network Approach to Solve Semi-infinite Linear Programming Problems

Volume 5, Issue 3, pp 160 - 166 http://dx.doi.org/10.22436/jmcs.05.03.03

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Authors

Alireza Fakharzadeh - Department of Mathematics, Faculty of basic Sciences, Shiraz University of Technology, Shiraz, Iran. Zahra Alamdar - Department of Mathematics, Faculty of Basic Sciences, Shiraz University of Technology, Shiraz, Iran. Masoumeh Hosseinipour - Department of Mathematics, Faculty of Basic Sciences, Shiraz University of Technology, Shiraz, Iran.

Abstract

In this article, we present a new algorithm for solving Semi-Infinite Linear Programming (SILP) problems based on an artificial neural network concept. First the local reduction method for solving the SILP problems is introduced. Based on the local reduction method, the Karush-Kuhn-Tucker (KKT) conditions and gradient method are used to convert the SILP problem to an unconstrained optimization problem; then, a neural network model is constructed to solve it. Numerical example has been employed to indicate the accuracy of the new method.

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Keywords

• Semi-Infinite linear programming
• Neural network
• Local reduction method
• KKT conditions.

MSC

•  90C34
•  90C47

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