Exercises in Numerical Linear Algebra and Matrix Factorizations

Exercises in Numerical Linear Algebra and Matrix Factorizations
Author :
Publisher : Springer Nature
Total Pages : 273
Release :
ISBN-10 : 9783030597894
ISBN-13 : 303059789X
Rating : 4/5 (89X Downloads)

Book Synopsis Exercises in Numerical Linear Algebra and Matrix Factorizations by : Tom Lyche

Download or read book Exercises in Numerical Linear Algebra and Matrix Factorizations written by Tom Lyche and published by Springer Nature. This book was released on 2020-11-02 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: To put the world of linear algebra to advanced use, it is not enough to merely understand the theory; there is a significant gap between the theory of linear algebra and its myriad expressions in nearly every computational domain. To bridge this gap, it is essential to process the theory by solving many exercises, thus obtaining a firmer grasp of its diverse applications. Similarly, from a theoretical perspective, diving into the literature on advanced linear algebra often reveals more and more topics that are deferred to exercises instead of being treated in the main text. As exercises grow more complex and numerous, it becomes increasingly important to provide supporting material and guidelines on how to solve them, supporting students’ learning process. This book provides precisely this type of supporting material for the textbook “Numerical Linear Algebra and Matrix Factorizations,” published as Vol. 22 of Springer’s Texts in Computational Science and Engineering series. Instead of omitting details or merely providing rough outlines, this book offers detailed proofs, and connects the solutions to the corresponding results in the textbook. For the algorithmic exercises the utmost level of detail is provided in the form of MATLAB implementations. Both the textbook and solutions are self-contained. This book and the textbook are of similar length, demonstrating that solutions should not be considered a minor aspect when learning at advanced levels.


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