|
![]() |
|||
|
||||
OverviewApplied Numerical Linear Algebra introduces students to numerical issues that arise in linear algebra and its applications. A wide range of techniques are touched on, including direct to iterative methods, orthogonal factorizations, least squares, eigenproblems, and nonlinear equations. Inside Applied Numerical Linear Algebra, readers will find: Clear and detailed explanations on a wide range of topics from condition numbers to the singular value decomposition. Material on nonlinear systems as well as linear systems. Frequent illustrations using discretizations of boundary-value problems or demonstrating other concepts. Exercises with detailed solutions at the end of the book. Supplemental material available at https://bookstore.siam.org/cl87/bonus. This textbook is appropriate for junior and senior undergraduate students and beginning graduate students in the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory. Full Product DetailsAuthor: William W. HagerPublisher: Society for Industrial & Applied Mathematics,U.S. Imprint: Society for Industrial & Applied Mathematics,U.S. Weight: 0.776kg ISBN: 9781611976854ISBN 10: 1611976855 Pages: 424 Publication Date: 28 February 2022 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print ![]() This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsReviewsWilliam W. Hager is a Distinguished Professor of Mathematics at the University of Florida and co-director of the Center for Applied Optimization. He is a Fellow of the Society for Industrial and Applied Mathematics, and members of both the Mathematical Optimization Society and the American Geophysical Union. His research has focused on convergence analysis for algorithms in optimal control, the development of algorithms for solving large, sparse optimization problems including his dual active set algorithm, and related update/downdate techniques for sparse Cholesky factorizations that arise in active set methods. Author InformationTab Content 6Author Website:Countries AvailableAll regions |