Numerical Matrix Analysis
Linear Systems and Least Squares
$62.00 (P)
- Author: Ilse Ipsen, North Carolina State University
- Date Published: July 2009
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
- format: Paperback
- isbn: 9780898716764
$
62.00
(P)
Paperback
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The purpose of this book is to promote understanding of two phenomena: sensitivity of linear systems and least squares problems, and numerical stability of algorithms. Sensitivity and stability are analyzed as mathematical properties, without reference to finite precision arithmetic. The material is presented at a basic level, emphasizing ideas and intuition, but in a mathematically rigorous fashion. The derivations are simple and elegant, and the results are easy to understand and interpret. The book is self-contained. It was written for students in all areas of mathematics, engineering, and the computational sciences, but can easily be used for self-study. This text differs from other numerical linear algebra texts by offering the following: 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.
Read more- The material is presented at a basic level, emphasising ideas and intuition
- Each chapter offers simple exercises for use in the classroom and more challenging exercises for student practice
- Written for advanced undergraduates and graduates in mathematics, engineering and computational science
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×Product details
- Date Published: July 2009
- format: Paperback
- isbn: 9780898716764
- length: 140 pages
- dimensions: 228 x 152 x 9 mm
- weight: 0.2kg
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
Table of Contents
Preface
Introduction
1. Matrices
2. Sensitivity, errors, and norms
3. Linear systems
4. Singular value decomposition
5. Least square problems
6. Subspaces
Index.
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