Overview
- Interactive, compact textbook with links to interesting contributions
- Introduces algorithmic-numerical thinking using the linear algebra to students of mathematics from the second semester onwards
- Offers a focused introduction to error analysis and perturbation theory
- Includes complete programs and numeric examples in MATLAB and Julia
Part of the book series: Springer Undergraduate Mathematics Series (SUMS)
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Table of contents (5 chapters)
Keywords
About this book
This book offers an introduction to the algorithmic-numerical thinking using basic problems of linear algebra. By focusing on linear algebra, it ensures a stronger thematic coherence than is otherwise found in introductory lectures on numerics. The book highlights the usefulness of matrix partitioning compared to a component view, leading not only to a clearer notation and shorter algorithms, but also to significant runtime gains in modern computer architectures. The algorithms and accompanying numerical examples are given in the programming environment MATLAB, and additionally – in an appendix – in the future-oriented, freely accessible programming language Julia. This book is suitable for a two-hour lecture on numerical linear algebra from the second semester of a bachelor's degree in mathematics.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Numerical Linear Algebra
Book Subtitle: A Concise Introduction with MATLAB and Julia
Authors: Folkmar Bornemann
Translated by: Walter Simson
Series Title: Springer Undergraduate Mathematics Series
DOI: https://doi.org/10.1007/978-3-319-74222-9
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG 2018
Softcover ISBN: 978-3-319-74221-2Published: 28 February 2018
eBook ISBN: 978-3-319-74222-9Published: 29 January 2018
Series ISSN: 1615-2085
Series E-ISSN: 2197-4144
Edition Number: 1
Number of Pages: X, 153
Number of Illustrations: 1 illustrations in colour
Topics: Linear and Multilinear Algebras, Matrix Theory, Numerical Analysis