This self-contained textbook presents matrix analysis in the context of numerical computation with numerical conditioning of problems and numerical stability of algorithms at the forefront. Using a unique combination of numerical insight and mathematical rigour, it advances readers' understanding of two phenomena: sensitivity of linear systems and least squares problems, and numerical stability of algorithms.
This book differs in several ways from other numerical linear algebra texts. It offers a systematic development of numerical conditioning; a simplified concept of numerical stability in exact arithmetic; simple derivations; a high-level view of algorithms; and results for complex matrices. The material is presented at a basic level, emphasizing ideas and intuition, and each chapter offers simple exercises for use in the classroom and more challenging exercises for student practice.
This book differs from other numerical linear algebra texts by offering:
A systematic development of numerical conditioning.
A simplified concept of numerical stability in exact arithmetic.
Simple derivations.
A high-level view of algorithms.
Results for complex matrices.
The material is presented at a basic level, emphasizing ideas and intuition, and each chapter offers simple exercises for use in the classroom and more challenging exercises for student practice.
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
This book is intended for first-year graduate students in engineering, operations research, computational science, and all areas of mathematics. It also is appropriate for self-study.
Produkt-Hinweis
Broschur/Paperback
Klebebindung
Maße
Höhe: 226 mm
Breite: 150 mm
Dicke: 10 mm
Gewicht
ISBN-13
978-0-89871-676-4 (9780898716764)
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Schweitzer Klassifikation
Ilse Ipsen is Professor of Mathematics at North Carolina State University. She is the SIAM Vice President for Programs and a member of the editorial boards of SIAM Review, the SIAM Journal on Matrix Analysis and Applications, Numerische Mathematik, and Numerical Linear Algebra with Applications.
Preface
Introduction
Chapter 1: Matrices
Chapter 2: Sensitivity, Errors, and Norms
Chapter 3: Linear Systems
Chapter 4: Singular Value Decomposition
Chapter 5: Least Square Problems
Chapter 6: Subspaces
Index.