A Neural Network Approach to Solve Semiinfinite Linear Programming Problems
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 SemiInfinite 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 KarushKuhnTucker (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.
Keywords
 SemiInfinite linear programming
 Neural network
 Local reduction method
 KKT conditions.
MSC
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