Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide

Bai, Zhaojun (Editor), and Demmel, James W (Editor), and Dongarra, Jack (Editor)

ISBN 10: 0898714710 / ISBN 13: 9780898714715

Verlag: Soc for Industrial & Applied Math, Philadelphia, PA, 2000

Beschreibung

***INTERNATIONAL EDITION*** Read carefully before purchase: This book is the international edition in mint condition with the different ISBN and book cover design, the major content is printed in full English as same as the original North American edition. The book printed in black and white, generally send in twenty-four hours after the order confirmed. All shipments contain tracking numbers. Great professional textbook selling experience and expedite shipping service. Trade paperback (US). Glued binding. 440 p. Contains: Illustrations. Journeybook. Audience: General/trade.

Bestandsnummer des Verkäufers K1240000253

Bibliografische Details

• Titel: Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide
• Verlag: Soc for Industrial & Applied Math, Philadelphia, PA
• Veröffentlichungsdatum: 2000
• Sprache: Englisch
• ISBN-10: 0898714710
• ISBN-13: 9780898714715
• Bindung: Trade paperback
• Ausgabe: First edition.
• Zustand: New in new dust jacket.

Über diesen Titel

Inhaltsangabe

This provides a unified overview of theory, algorithms, and practical software for eigenvalue problems.

Reseña del editor

Large-scale problems of engineering and scientific computing often require solutions of eigenvalue and related problems. This book gives a unified overview of theory, algorithms, and practical software for eigenvalue problems. It organizes this large body of material to make it accessible for the first time to the many nonexpert users who need to choose the best state-of-the-art algorithms and software for their problems. Using an informal decision tree, just enough theory is introduced to identify the relevant mathematical structure that determines the best algorithm for each problem. The algorithms and software at the 'leaves' of the decision tree range from the classical QR algorithm, which is most suitable for small dense matrices, to iterative algorithms for very large generalized eigenvalue problems. Algorithms are presented in a unified style as templates, with different levels of detail suitable for readers ranging from beginning students to experts.