Lanczos algorithms for large symmetric eigenvalue computations. Vol. 1, Theory /

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Bibliographic Details
Author / Creator:Cullum, Jane K., 1938-
Imprint:Philadelphia, Pa. : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), 2002.
Description:1 online resource (xx, 273 pages : illustrations) : digital file
Language:English
Series:Classics in applied mathematics ; 41
Classics in applied mathematics ; 41.
Subject:Symmetric matrices -- Data processing.
Eigenvalues -- Data processing.
Eigenvalues -- Data processing.
Symmetric matrices -- Data processing.
Electronic books
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12577168
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Other authors / contributors:Willoughby, Ralph A.
Society for Industrial and Applied Mathematics.
ISBN:9780898719192
0898719194
9780898715231
0898715237
Notes:Originally published: Boston : Birkhäuser, 1985-, in series: Progress in scientific computing ; v. 3.
An online version of Vol. 2: Programs, which contains the FORTRAN code and documentation for each of the Lanczos procedures discussed in Vol. 1, can be found at the numerical analysis community repository, www.netlib.org, under the term "lanczos."
Includes bibliographical references and index.
Restricted to subscribers or individual electronic text purchasers.
Also available in print version.
Title page of print version.
Summary:First published in 1985, Lanczos Algorithms for Large Symmetric Eigenvalue Computations; Vol. 1: Theory presents background material, descriptions, and supporting theory relating to practical numerical algorithms for the solution of huge eigenvalue problems. This book deals with "symmetric" problems. However, in this book, "symmetric" also encompasses numerical procedures for computing singular values and vectors of real rectangular matrices and numerical procedures for computing eigenelements of nondefective complex symmetric matrices. Although preserving orthogonality has been the golden rule in linear algebra, most of the algorithms in this book conform to that rule only locally, resulting in markedly reduced memory requirements. Additionally, most of the algorithms discussed separate the eigenvalue (singular value) computations from the corresponding eigenvector (singular vector) computations. This separation prevents losses in accuracy that can occur in methods which, in order to be able to compute further into the spectrum, use successive implicit deflation by computed eigenvector or singular vector approximations.
Other form:Print version: Cullum, Jane K., 1938- Lanczos algorithms for large symmetric eigenvalue computations. Philadelphia : Society for Industrial and Applied Mathematics, ©2002- 0898715237
Standard no.:CL41
Publisher's no.:CL41 siam