Novel reduced matrix equation constructing method accelerates iterative solution of characteristic basis function method

Zhonggen Wang, Qiang Chen, Wen Yan Nie, Han Lin

Research output: Contribution to journalArticle

Abstract

In this paper, a new construction method of reduced matrix equation is proposed to improve the iterative solution efficiency of characteristic basis function method (CBFM). Firstly, the singular value decomposition (SVD) technique is applied to compress the incident excitations and these new excitations retained on each block after SVD are defined as the excitation basis functions (EBFs). Then, the characteristic basis functions (CBFs) of each block are solved from these EBFs. Lastly, these EBFs and CBFs are used as the testing functions and the basis functions to construct the reduction matrix equation, respectively. The diagonal sub-matrices of the reduced matrix constructed by the proposed method are all identity matrices. Thus, the condition of the reduced matrix is improved resulting in a smaller number of iterations required for the solution of the reduced matrix equation. The numerical results validate the accuracy of the proposed method. Compared with the traditional CBFM, the iterative solution efficiency of the reduced matrix equation constructed by the proposed method is significantly improved.

Original languageEnglish
Pages (from-to)1814-1820
Number of pages7
JournalApplied Computational Electromagnetics Society Journal
Volume34
Issue number12
Publication statusPublished - 2019

Keywords

  • Characteristic basis function method
  • Characteristic basis functions
  • Reduced matrix equation
  • Singular value decomposition
  • Testing functions

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Electrical and Electronic Engineering

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