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A neural network for solving a convex quadratic bilevel programming problem

被引：31
|
作者：
Lv, Yibing [1 ]
Chen, Zhong [1 ]
Wan, Zhongping [2 ]
机构：
[1] Yangtze Univ, Sch Informat & Math, Jinzhou 434023, Peoples R China
[2] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Peoples R China
来源：
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2010年 / 234卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Convex quadratic bilevel programming; Asymptotic stability; Neural network; Optimal solution;
D O I：
10.1016/j.cam.2009.12.041
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A neural network is proposed for solving a convex quadratic bilevel programming problem. Based on Lyapunov and LaSalle theories, we prove strictly an important theoretical result that, for an arbitrary initial point, the trajectory of the proposed network does converge to the equilibrium, which corresponds to the optimal solution of a convex quadratic bilevel programming problem. Numerical simulation results show that the proposed neural network is feasible and efficient for a convex quadratic bilevel programming problem. (C) 2010 Elsevier By. All rights reserved.
引用
收藏
页码：505 / 511
页数：7
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