Lanczos Algorithms for Large Symmetric Eigenvalue Computations Volume I: Theory .

Jane K. Cullum and Ralph A. Willoughby.

ISBN 10: 0817630589 / ISBN 13: 9780817630584

Published by Birkhäuser, Boston, MA, 1985

About this Item

EX-INSTITUTIONAL LIBRARY copy, showing the usual marks and treatments. Else, Very Good: shows moderate scuffing to the extremities, but only very mild rubbing to the panels; slight spine lean.The binding is secure; the text is clean. Free of creased or dog-eared pages in the text. Free of any underlining, hi-lighting or marginalia or marks in the text. A solid working copy, structurally sound and tightly bound, showing moderate wear and minor flaws. NOT a Remainder or Book-Club. 8vo. (9 x 6 x 1.25 inches). 268 pages. Indexes. Language: English. Weight: 21.7 ounces. Progress in Scientific Computing Series: (Book 3). First Edition (1985), unstated. Hardback: Laminate Boards.

Seller Inventory # 55817

Bibliographic Details

• Title: Lanczos Algorithms for Large Symmetric Eigenvalue Computations Volume I: Theory .
• Publisher: Birkhäuser, Boston, MA
• Publication Date: 1985
• Language: English
• ISBN 10: 0817630589
• ISBN 13: 9780817630584
• Binding: Hardcover
• Edition: First Edition (1985), unstated.
• Seller catalogs:
  • Mathematics

About this title

Synopsis

First published in 1985, this book 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.