SUB-GRADIENT BASED PROJECTION NEURAL NETWORKS FOR NON-DIFFERENTIABLE OPTIMIZATION PROBLEMS

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.

关键词： Differential inclusions Projection neural network Sub-gradient

作者: GUO-CHENG LI ZHI-LING DONG

作者单位: Department of Mathematics, Beijing Information Science and Technology University, Beijing 100085, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 835-839

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）