DocumentCode
476001
Title
Sub-gradient based projection neural networks for non-differentiable optimization problems
Author
Li, Guo-cheng ; Dong, Zhi-Ling
Author_Institution
Dept. of Math., Beijing Inf. Sci. & Technol. Univ., Beijing
Volume
2
fYear
2008
fDate
12-15 July 2008
Firstpage
835
Lastpage
839
Abstract
This paper further investigates the sub-gradient projection neural networks model for solving non- differentiable convex optimization problems proposed by Li et al. (2006). 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.
Keywords
gradient methods; neural nets; optimisation; network trajectories; nondifferentiable optimization problems; objective function; primal optimal problem; sub-gradient based projection neural networks; Constraint optimization; Cybernetics; Hopfield neural networks; Information science; Linear programming; Machine learning; Mathematical model; Mathematics; Neural networks; Virtual colonoscopy; Differential inclusions; Projection neural network; Sub-gradient;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location
Kunming
Print_ISBN
978-1-4244-2095-7
Electronic_ISBN
978-1-4244-2096-4
Type
conf
DOI
10.1109/ICMLC.2008.4620520
Filename
4620520
Link To Document