Solving convex quadratic programming problems by an modified neural network with exponential convergence

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

3 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
Pages306-309
Volume1
Publication statusPublished - 2003
Externally publishedYes

Publication series

Name
Volume1

Conference

Title2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
PlaceChina
CityNanjing
Period14 - 17 December 2003

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. © 2003 IEEE.

Citation Format(s)

Solving convex quadratic programming problems by an modified neural network with exponential convergence. / Xia, Yousben; Feng, Gang.
Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03. Vol. 1 2003. p. 306-309 1279271.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review