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Compressed representation of matrix decomposition algorithm-singular value decomposition for full-wave analysis microstrip problems

Author

Listed:
  • Xiaoqing Hu
  • Rushan Chen
  • Xiao Chen
  • Xin Liu
  • Hairong Zheng
  • Ye Li

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.

Suggested Citation

  • Xiaoqing Hu & Rushan Chen & Xiao Chen & Xin Liu & Hairong Zheng & Ye Li, 2015. "Compressed representation of matrix decomposition algorithm-singular value decomposition for full-wave analysis microstrip problems," Journal of Electromagnetic Waves and Applications, Taylor & Francis Journals, vol. 29(6), pages 832-842, April.
    • Handle: RePEc:taf:tewaxx:v:29:y:2015:i:6:p:832-842
    DOI: 10.1080/09205071.2015.1017013
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