Sub-gradient based projection neural networks for non-differentiable optimization problems

被引：0
|
作者：
Li, Guo-Cheng [1 ]
Dong, Zhi-Ling [1 ]
机构：
[1] Beijing Informat Sci & Technol Univ, Dept Math, Beijing 100085, Peoples R China
来源：
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2008年
关键词：
differential inclusions; projection neural network; sub-gradient;
D O I：
10.1109/ICMLC.2008.4620520
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper further investigates the sub-gradient projection neural networks model for solving non-differentiable convex optimization problems proposed in reference [1]. It is proved in this paper that when the initial points are belong to the constraint set or the initial points are not belong to the constraint set and the objective function is strictly convex, the network trajectories converge to an optimal solution of the primal optimal problem.
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页码：835 / 839
页数：5
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