Primal neural networks for solving convex quadratic programs

Youshen Xia; Jun Wang

Primal neural networks for solving convex quadratic programs

Youshen Xia, Jun Wang

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

8 Citations (Scopus)

Abstract

In this paper, we propose two primal neural networks with globally exponential stability for solving quadratic programming problems. Compared with Bouzerdoum and Pattison's network, there is no choice of both the self-feedback and lateral connection matrices in the present network. Moreover, the size of the proposed networks is same as that of the original problem, smaller than that of primal-dual networks.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	IEEE
Pages	582-587
Volume	1
Publication status	Published - 1999
Externally published	Yes
Event	1999 International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, United States Duration: 10 Jul 1999 → 16 Jul 1999

Publication series

Name
Volume	1

Conference

Conference	1999 International Joint Conference on Neural Networks (IJCNN'99)
Place	United States
City	Washington, DC
Period	10/07/99 → 16/07/99

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@inproceedings{095d9d2eb8d446c9a0343d83ab1edd0a,

title = "Primal neural networks for solving convex quadratic programs",

abstract = "In this paper, we propose two primal neural networks with globally exponential stability for solving quadratic programming problems. Compared with Bouzerdoum and Pattison's network, there is no choice of both the self-feedback and lateral connection matrices in the present network. Moreover, the size of the proposed networks is same as that of the original problem, smaller than that of primal-dual networks.",

author = "Youshen Xia and Jun Wang",

year = "1999",

language = "English",

volume = "1",

publisher = "IEEE",

pages = "582--587",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

address = "United States",

note = "1999 International Joint Conference on Neural Networks (IJCNN'99) ; Conference date: 10-07-1999 Through 16-07-1999",

}

TY - GEN

T1 - Primal neural networks for solving convex quadratic programs

AU - Xia, Youshen

AU - Wang, Jun

PY - 1999

Y1 - 1999

N2 - In this paper, we propose two primal neural networks with globally exponential stability for solving quadratic programming problems. Compared with Bouzerdoum and Pattison's network, there is no choice of both the self-feedback and lateral connection matrices in the present network. Moreover, the size of the proposed networks is same as that of the original problem, smaller than that of primal-dual networks.

AB - In this paper, we propose two primal neural networks with globally exponential stability for solving quadratic programming problems. Compared with Bouzerdoum and Pattison's network, there is no choice of both the self-feedback and lateral connection matrices in the present network. Moreover, the size of the proposed networks is same as that of the original problem, smaller than that of primal-dual networks.

UR - http://www.scopus.com/inward/record.url?scp=0033351844&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0033351844&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

VL - 1

SP - 582

EP - 587

BT - Proceedings of the International Joint Conference on Neural Networks

PB - IEEE

T2 - 1999 International Joint Conference on Neural Networks (IJCNN'99)

Y2 - 10 July 1999 through 16 July 1999

ER -

Primal neural networks for solving convex quadratic programs

Abstract

Publication series

Conference

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this