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9781611972450 Academic Inspection Copy

Eigenvalues of Matrices

  • ISBN-13: 9781611972450
  • Publisher: SIAM - SOCIETY FOR INDUSTRIAL AND APPLIED
    Imprint: SIAM - SOCIETY FOR INDUSTRIAL AND APPLIED
  • By Francoise Chatelin
  • Price: AUD $260.00
  • Stock: 0 in stock
  • Availability: This book is temporarily out of stock, order will be despatched as soon as fresh stock is received.
  • Local release date: 28/02/2013
  • Format: Paperback (226.00mm X 154.00mm) 440 pages Weight: 598g
  • Categories: Numerical analysis [PBKS]
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Biography
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This classic textbook provides a modern and complete guide to the calculation of eigenvalues of matrices, written at an accessible level that presents in matrix notation the fundamental aspects of the spectral theory of linear operators in finite dimension. Unique features of this book are: The convergence of eigensolvers serving as the basis of the notion of the gap between invariant subspaces. Its coverage of the impact of the high nonnormality of the matrix on its eigenvalues. The comprehensive nature of the material that moves beyond mathematical technicalities to the essential mean carried out by matrix eigenvalues.
Francoise Chatelin is Professor of Mathematics at the University of Toulouse and head of the Qualitative Computing Group at CERFACS. Before moving to CERFACS, she was a professor at the universities of Grenoble and Paris IX Dauphine. She also worked for a decade in the industrial research laboratories of IBM France and Thales, where she was in charge of intensive computing activities. Her areas of expertise include spectral theory for linear operators in Banach spaces and finite precision computation of very large eigenproblems. She currently explores the uncharted domain of mathematical computation that lies beyond real or complex analysis.
Chapter 1: Supplements from Linear Algebra Chapter 2: Elements of Spectral Theory Chapter 3: Why Compute Eigenvalues? Chapter 4: Error Analysis Chapter 5: Foundations of Methods for Computing Eigenvalues Chapter 6: Numerical Methods for Large Matrices Chapter 7: Chebyshev's Iterative Methods Chapter 8: Polymorphic Information Processing with Matrices Appendix A: Solution to Exercises Appendix B: References for Exercises Appendix C: References
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