In this Book

  • Matrix Computations and Semiseparable Matrices: Eigenvalue and Singular Value Methods
  • Book
  • Raf Vandebril, Marc Van Barel, and Nicola Mastronardi
  • 2008
  • Published by: Johns Hopkins University Press
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summary
The general properties and mathematical structures of semiseparable matrices were presented in volume 1 of Matrix Computations and Semiseparable Matrices. In volume 2, Raf Vandebril, Marc Van Barel, and Nicola Mastronardi discuss the theory of structured eigenvalue and singular value computations for semiseparable matrices. These matrices have hidden properties that allow the development of efficient methods and algorithms to accurately compute the matrix eigenvalues. This thorough analysis of semiseparable matrices explains their theoretical underpinnings and contains a wealth of information on implementing them in practice. Many of the routines featured are coded in Matlab and can be downloaded from the Web for further exploration.

Table of Contents

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  1. Cover
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  1. Title Page, Copyright Page
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  1. Contents
  2. pp. v-x
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  1. Preface
  2. pp. xi-xiv
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  1. Notation
  2. p. xv
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  1. Chapter 1 Introduction to semiseparable matrices
  2. pp. 1-14
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  1. Part I The reduction of matrices
  2. pp. 15-18
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  1. Chapter 2 Algorithms for reducing matrices
  2. pp. 19-66
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  1. Chapter 3 Convergence properties of the reduction algorithms
  2. pp. 67-108
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  1. Chapter 4 Implementation of the algorithms and numerical experiments
  2. pp. 109-148
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  1. Part II QR-algorithms (eigenvalue problems)
  2. pp. 149-154
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  1. Chapter 5 Introduction: traditional sparse QR-algorithms
  2. pp. 155-170
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  1. Chapter 6 Theoretical results for structured rank QR-algorithms
  2. pp. 171-198
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  1. Chapter 7 Implicit QR-methods for semiseparable matrices
  2. pp. 199-238
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  1. Chapter 8 Implementation and numerical experiments
  2. pp. 239-276
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  1. Chapter 9 More on QR-related algorithms
  2. pp. 277-362
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  1. Part III Some generalizations and miscellaneous topics
  2. pp. 363-366
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  1. Chapter 10 Divide-and-conquer algorithms for the eigendecomposition
  2. pp. 367-392
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  1. Chapter 11 A Lanczos-type algorithm and rank revealing
  2. pp. 393-410
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  1. Part IV Orthogonal (rational) functions (Inverse eigenvalue problems)
  2. pp. 411-414
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  1. Chapter 12 Orthogonal polynomials and discrete least squares
  2. pp. 415-428
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  1. Chapter 13 Orthonormal polynomial vectors
  2. pp. 429-446
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  1. Chapter 14 Orthogonal rational functions
  2. pp. 447-466
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  1. Chapter 15 Concluding remarks & software
  2. pp. 467-470
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  1. Bibliography
  2. pp. 471-486
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  1. Author/Editor Index
  2. pp. 487-491
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  1. Subject Index
  2. pp. 492-498
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