Numerical Linear Algebra with Julia
Author: Eric Darve (Author), Mary Wootters (Author)
Publisher finelybook 出版社: SIAM – Society for Industrial and Applied Mathematics
Publication Date 出版日期: 2021-09-02
Language 语言: English
Print Length 页数: 406 pages
ISBN-10: 1611976545
ISBN-13: 9781611976540
Book Description
By finelybook
Numerical Linear Algebra with Julia provides in-depth coverage of fundamental topics in numerical linear algebra, including how to solve dense and sparse linear systems, compute QR factorizations, compute the eigendecomposition of a matrix, and solve linear systems using iterative methods such as conjugate gradient. Julia code is provided to illustrate concepts and allow readers to explore methods on their own. Written in a friendly and approachable style, the book contains detailed descriptions of algorithms along with illustrations and graphics that emphasize core concepts and demonstrate the algorithms. Numerical Linear Algebra with Julia is a textbook for advanced undergraduate and graduate students in most STEM fields and is appropriate for courses in numerical linear algebra. It may also serve as a reference for researchers in various fields who depend on numerical solvers in linear algebra.
About the Author
Eric Darve is a professor of mechanical engineering at Stanford University and a member of the Institute for Computational and Mathematical Engineering. His research interests include numerical linear algebra, machine learning for engineering, and high-performance and GPU computing
Mary Wootters is an assistant professor of computer science and electrical engineering at Stanford University. She works in theoretical computer science, applied math, and information theory, and her research interests include error correcting codes and randomized algorithms for dealing with high-dimensional data.
Mary Wootters is an assistant professor of computer science and electrical engineering at Stanford University. She works in theoretical computer science, applied math, and information theory, and her research interests include error correcting codes and randomized algorithms for dealing with high-dimensional data.
