Adaptive cross approximation for MOM matrix fill for PC problem sizes to 157000 unknowns

Recent work on sparse MOM codes for PC applications has reduced LU matrix factorization time to significantly less than matrix fill for problem unknowns approaching 200,000. This paper reports on the use and results of applying the recently developed adaptive cross approximation for significantly re...

Full description

Saved in:
Bibliographic Details
Published inIEEE/ACES International Conference on Wireless Communications and Applied Computational Electromagnetics, 2005 pp. 748 - 753
Main Authors Shaeffer, J., Canning, F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Canning
Computer interfaces
Contracts
Large-scale systems
Matrix decomposition
Message-oriented middleware
Personal communication networks
Singular value decomposition
Sparse matrices
Testing
Online AccessGet full text

Cover

More Information
Summary:Recent work on sparse MOM codes for PC applications has reduced LU matrix factorization time to significantly less than matrix fill for problem unknowns approaching 200,000. This paper reports on the use and results of applying the recently developed adaptive cross approximation for significantly reducing MOM matrix fill time. Results suggest that when problem sizes approach 500,000 unknowns, matrix fill can be reduced from 100 to 10 hours on a modern PC.
ISBN:9780780390683
0780390687
DOI:10.1109/WCACEM.2005.1469694