Numerical Linear Algebra with Julia (Paperback)

Eric Darve

ISBN 10: 1611976545 / ISBN 13: 9781611976540

Verlag: Society for Industrial & Applied Mathematics,U.S., New York, 2021

Beschreibung

Paperback. 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 finddetailed descriptions of algorithms,implementations in Julia that illustrate concepts and allow readers to explore methods on their own, andillustrations 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Bestandsnummer des Verkäufers 9781611976540

Bibliografische Details

• Titel: Numerical Linear Algebra with Julia (Paperback)
• Verlag: Society for Industrial & Applied Mathematics,U.S., New York
• Erscheinungsjahr: 2021
• Sprache: Englisch
• ISBN-10: 1611976545
• ISBN-13: 9781611976540
• Einband: Paperback
• Zustand: new

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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.