Evaluation of a New Eigen Decomposition Algorithm for Symmetric Tridiagonal Matrices

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概要

• 論文の詳細を見る

• This paper focuses on a new extension version of double Divide and Conquer (dDC) algorithm to eigen decomposition. Recently, dDC was proposed for singular value decomposition (SVD) of rectangular matrix. The dDC for SVD consists of two parts. One is Divide and Conquer (D〓C) for singular value and the other is twisted factorization for singular vector. The memory usage of dDC is smaller than that of D〓C. Both theoretical and running time are also shorter than those of D〓C. In this paper, a new dDC for eigen decomposition is proposed. A shift of origin is introduced into our dDC. By some numerical tests, dDC is evaluated with respect to running time and accuracy.

• 一般社団法人情報処理学会の論文
• 2006-06-26

著者

• Nakamura Yoshimasa

  Sorst Jst And Graduate School Of Informatics Kyoto University

• Kimura Kinji

  Crest Jst And College Of Science Rikkyo University

• Takata Masami

  Graduate School of Humanities and Sciences, Nara Women's University

• Konda Taro

  SORST, JST Graduate School of Informatics Kyoto University

• Tsuboi Hiroaki

  SORST, JST and Graduate School of Informatics Kyoto University

• Iwasaki Masashi

  SORST, JST and Graduate School of Informatics Kyoto University

• Takata Masami

  Graduate School Of Humanity And Science Nara Women's University

• Takata Masami

  Graduate School Of Human Culture Nara Women's University

• Tsuboi Hiroaki

  Sorst Jst And Graduate School Of Informatics Kyoto University

• Iwasaki Masashi

  Sorst Jst And Graduate School Of Informatics Kyoto University

• Iwasaki Masashi

  Sorst Jst And Department Of Applied Mathematics And Physics Graduate School Of Informatics Kyoto Uni

• Nakamura Yoshimasa

  Sorst Jst And Department Of Applied Mathematics And Physics Graduate School Of Informatics Kyoto Uni

• Konda Taro

  SORST, JST and Graduate School of Informatics Kyoto University

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