Title :
Solving convex quadratic programming problems by an modified neural network with exponential convergence
Author :
Xia, Youshen ; Feng, Gang
Author_Institution :
Dept. of Manuf. Eng. & Eng. Manage., City Univ. of Hong Kong, China
Abstract :
This paper presents using a modified neural network with exponential convergence to solve strictly quadratic programming problems with general linear constraints. It is shown that the proposed neural network is globally convergent to a unique optimal solution within a finite time. Compared with the existing the primal-dual neural network and the dual neural network for solving such problems, the proposed neural network has a low complexity for implementation and can be guaranteed to have a exponential convergence rate.
Keywords :
convergence; convex programming; neural nets; quadratic programming; convex quadratic programming problems; exponential convergence rate; finite time; general linear constraints; modified neural network; primal-dual neural network; unique optimal solution; Application software; Computer networks; Convergence; Neural networks; Pulp manufacturing; Quadratic programming; Recurrent neural networks; Research and development; Research and development management; Symmetric matrices;
Conference_Titel :
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
0-7803-7702-8
DOI :
10.1109/ICNNSP.2003.1279271