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文章基本信息

  • 标题:Krylov subspace methods for solving quadratic eigenvalue problems
  • 本地全文:下载
  • 作者:Juan Song
  • 期刊名称:Journal of Computations & Modelling
  • 印刷版ISSN:1792-7625
  • 电子版ISSN:1792-8850
  • 出版年度:2017
  • 卷号:7
  • 期号:3
  • 出版社:Scienpress Ltd
  • 摘要:In this paper, the Arnoldi-type process and symmetric Lanczos-type process for solving large scale quadratic eigenvalue problem (l^2A +lB+C)x=0 are given. One decomposition theorem about the matrices A, B and C is obtained based on the Householder transformation. The advantage of the Arnoldi-type process and symmetric Lanczos-type process is that they can preserve the matrix structure and properties of the original problems. Finally, some numerical examples are presented to show the efficiency of the proposed methods.
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