Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)

A New Algorithm for Solving Ill-conditioned Linear System with an Auto- Parameter

Authors
Tianyu He, Jinshan Wang, Zonghao Tian
Corresponding Author
Tianyu He
Available Online July 2016.
DOI
10.2991/iccia-17.2017.20How to use a DOI?
Keywords
Particle swarm optimization algorithm, auto-parameter, ill-conditioned linear system.
Abstract

A new simple and effective method for solving ill-conditioned linear systems is presented in this paper. This method tried mainly not to decrease the error caused in direct solving, instead, it tries to transfer this error to a medium variable. At the same time, a parameter is introduced. In order to obtain the best parameter, PSO is used. An algorithm based on this method is presented, and examples show that this algorithm could solve extremely ill-conditioned linear systems correctly and stably.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
Series
Advances in Computer Science Research
Publication Date
July 2016
ISBN
10.2991/iccia-17.2017.20
ISSN
2352-538X
DOI
10.2991/iccia-17.2017.20How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Tianyu He
AU  - Jinshan Wang
AU  - Zonghao Tian
PY  - 2016/07
DA  - 2016/07
TI  - A New Algorithm for Solving Ill-conditioned Linear System with an Auto- Parameter
BT  - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SP  - 128
EP  - 131
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.20
DO  - 10.2991/iccia-17.2017.20
ID  - He2016/07
ER  -