Numerical Linear Algebra with Julia
Published on 30. November 2021
Book
Paperback/Softback
277 pages
978-1-61197-654-0 (ISBN)
Description
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. The style is friendly and approachable and cartoon characters guide the way.
Inside this book, readers will find
detailed descriptions of algorithms,
implementations in Julia that illustrate concepts and allow readers to explore methods on their own, and
illustrations and graphics that emphasize core concepts and demonstrate algorithms.
Numerical Linear Algebra with Julia is a textbook for undergraduate and graduate students. It is appropriate for the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory.
The book may also serve as a reference for researchers in various fields such as computational engineering, statistics, data-science, and machine learning, who depend on numerical solvers in linear algebra.
Inside this book, readers will find
detailed descriptions of algorithms,
implementations in Julia that illustrate concepts and allow readers to explore methods on their own, and
illustrations and graphics that emphasize core concepts and demonstrate algorithms.
Numerical Linear Algebra with Julia is a textbook for undergraduate and graduate students. It is appropriate for the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory.
The book may also serve as a reference for researchers in various fields such as computational engineering, statistics, data-science, and machine learning, who depend on numerical solvers in linear algebra.
More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Weight
874 gr
ISBN-13
978-1-61197-654-0 (9781611976540)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Professor Eric Darve became a postdoctoral scholar with Professors Moin and Pohorille at Stanford and NASA Ames in 1999. In 2001, he joined the faculty at Stanford University and is now a professor of Mechanical Engineering and a member of the Institute for Computational and Mathematical Engineering. His research interests include numerical linear algebra, machine learning for engineering, high-performance and GPU computing
Mary Wootters is an assistant professor of Computer Science and Electrical Engineering at Stanford University. From 2014 to 2016, she was an NSF postdoctoral fellow at Carnegie Mellon University. She works in theoretical computer science, applied math, and information theory. Her research interests include error correcting codes and randomized algorithms for dealing with high dimensional data. She is the recipient of an NSF CAREER award and was named a Sloan Research Fellow in 2019.
Mary Wootters is an assistant professor of Computer Science and Electrical Engineering at Stanford University. From 2014 to 2016, she was an NSF postdoctoral fellow at Carnegie Mellon University. She works in theoretical computer science, applied math, and information theory. Her research interests include error correcting codes and randomized algorithms for dealing with high dimensional data. She is the recipient of an NSF CAREER award and was named a Sloan Research Fellow in 2019.