Compressed representation of matrix decomposition algorithm-singular value decomposition for full-wave analysis microstrip problems
Xiaoqing Hu,
Rushan Chen,
Xiao Chen,
Xin Liu,
Hairong Zheng and
Ye Li
Journal of Electromagnetic Waves and Applications, 2015, vol. 29, issue 6, 832-842
Abstract:
In order to efficiently solve large dense complex linear systems arising from electric field integral equations (EFIE) of electromagnetic problems, matrix decomposition algorithm-singular value decomposition (MDA-SVD) is used to accelerate the matrix-vector product (MVP) operations and decrease memory usage. Based on the symmetry of the impedance matrix resulting from the discretization of the EFIE, we introduce a compressed representation of MDA-SVD in this paper. We obtain a sparse representation of the far-field interaction parts of impedance matrix and perform a fast MVP operation. Numerical experiments demonstrate that the compressed representation of MDA-SVD can reduce both the MVP time and memory usage by around 50% with similar accuracy.
Date: 2015
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/09205071.2015.1017013 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tewaxx:v:29:y:2015:i:6:p:832-842
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tewa20
DOI: 10.1080/09205071.2015.1017013
Access Statistics for this article
Journal of Electromagnetic Waves and Applications is currently edited by Mohamad Abou El-Nasr and Pankaj Kumar Choudhury
More articles in Journal of Electromagnetic Waves and Applications from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().