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2011•Journal of Yangtze UniversityRequires access

A Neural Network Method for Nonlinear Bilevel Programming Problems

Yibing Lü

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Abstract

This paper mainly studies the algorithm for solving the nonlinear bilevel programming problem,of which the lower problem is convex programming.When the lower problem is convex programming problem,the K-T conditions are used instead of the lower problem to transform the nonlinear bilevel programming into the corresponding single level programs,then a penalty function is structured for the nonlinear programming problem,the method of neural networks is proposed for solving the problem,the numerical experiment results show the method is feasible and effective.

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What this paper is about

This paper mainly studies the algorithm for solving the nonlinear bilevel programming problem,of which the lower problem is convex programming.When the lower problem is convex programming problem,the K-T conditions are used instead of the lower problem to transform the nonlinear bilevel programming into the corresponding single level programs,then a penalty function is structured for the nonlinear programming problem,the method of neural networks is proposed for solving the problem,the numerical experiment results show the method is feasible and effective.

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Available abstract

This paper mainly studies the algorithm for solving the nonlinear bilevel programming problem,of which the lower problem is convex programming.When the lower problem is convex programming problem,the K-T conditions are used instead of the lower problem to transform the nonlinear bilevel programming into the corresponding single level programs,then a penalty function is structured for the nonlinear programming problem,the method of neural networks is proposed for solving the problem,the numerical experiment results show the method is feasible and effective.

Key concepts: Bilevel optimization, Nonlinear programming, Mathematical optimization, Nonlinear system, Fractional programming, Artificial neural network, Computer science, Convex optimization

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