Table of contents
About this book
After reading this book, students should be able to analyze computational problems in linear algebra such as linear systems, least squares- and eigenvalue problems, and to develop their own algorithms for solving them.Since these problems can be large and difficult to handle, much can be gained by understanding and taking advantage of special structures. This in turn requires a good grasp of basic numerical linear algebra and matrix factorizations. Factoring a matrix into a product of simpler matrices is a crucial tool in numerical linear algebra, because it allows us to tackle complex problems by solving a sequence of easier ones.
The main characteristics of this book are as follows:
It is self-contained, only assuming that readers have completed first-year calculus and an introductory course on linear algebra, and that they have some experience with solving mathematical problems on a computer. The book provides detailed proofs of virtually all results. Further, its respective parts can be used independently, making it suitable for self-study.
The book consists of 15 chapters, divided into five thematically oriented parts. The chapters are designed for a one-week-per-chapter, one-semester course. To facilitate self-study, an introductory chapter includes a brief review of linear algebra.
- Book Title Numerical Linear Algebra and Matrix Factorizations
- Series Title Texts in Computational Science and Engineering
- Series Abbreviated Title Texts in Computat. Science
- DOI https://doi.org/10.1007/978-3-030-36468-7
- Copyright Information Springer Nature Switzerland AG 2020
- Publisher Name Springer, Cham
- eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
- Hardcover ISBN 978-3-030-36467-0
- Softcover ISBN 978-3-030-36470-0
- eBook ISBN 978-3-030-36468-7
- Series ISSN 1611-0994
- Series E-ISSN 2197-179X
- Edition Number 1
- Number of Pages XXIII, 371
- Number of Illustrations 144 b/w illustrations, 37 illustrations in colour
Linear and Multilinear Algebras, Matrix Theory
Computational Science and Engineering
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