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Numerical Linear Algebra and Matrix Factorizations: Texts in Computational Science and Engineering, cartea 22

Autor Tom Lyche
en Limba Engleză Paperback – 3 mar 2021
After reading this book, students should be able to analyze computational problems in linear algebra such as linear systems, least squares- and eigenvalue problems, and to develop their own algorithms for solving them.
Since these problems can be large and difficult to handle, much can be gained by understanding and taking advantage of special structures. This in turn requires a good grasp of basic numerical linear algebra and matrix factorizations. Factoring a matrix into a product of simpler matrices is a crucial tool in numerical linear algebra, because it allows us to tackle complex problems by solving a sequence of easier ones. The main characteristics of this book are as follows:
It is self-contained, only assuming that readers have completed first-year calculus and an introductory course on linear algebra, and that they have some experience with solving mathematical problems on a computer. The book provides detailed proofs of virtually all results.Further, its respective parts can be used independently, making it suitable for self-study.
The book consists of 15 chapters, divided into five thematically oriented parts. The chapters are designed for a one-week-per-chapter, one-semester course. To facilitate self-study, an introductory chapter includes a brief review of linear algebra. 

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Specificații

ISBN-13: 9783030364700
ISBN-10: 3030364704
Pagini: 371
Ilustrații: XXIII, 371 p. 181 illus., 37 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.61 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria Texts in Computational Science and Engineering

Locul publicării:Cham, Switzerland

Cuprins

A Short Review of Linear Algebra.- LU and QR Factorizations.- Eigenpairs and Singular Values.- Matrix Norms and Least Squares.- Kronecker Products and Fourier Transforms.- Iterative Methods for Large Linear Systems.- Eigenvalues and Eigenvectors.- Index.

Recenzii

“It is more suitable for students with a few more advanced courses that include at least some real analysis and introductory numerical analysis.” (Tom Lyche, MAA Reviews, November 7, 2020)

Notă biografică

The author has a long experience at the university level, teaching numerical analysis, numerical linear algebra and matrix theory, mathematical optimization, approximation theory, and computer aided geometric design. 

He has received the Dagstuhl foundation’s John Gregory Memorial Award for "Outstanding contributions to geometric modeling" and is a member of Norwegian Academy of Science and Letters. 

He has published more than 90 papers in leading international journals and refereed proceedings, edited 17 books and is on the editorial board of 4 international journals. Jointly with Prof. Jean-Louis Merrien,  he has published the book “Exercises in Computational Mathematics with MATLAB “ published by Springer in 2014.

Textul de pe ultima copertă

After reading this book, students should be able to analyze computational problems in linear algebra such as linear systems, least squares- and eigenvalue problems, and to develop their own algorithms for solving them.
Since these problems can be large and difficult to handle, much can be gained by understanding and taking advantage of special structures. This in turn requires a good grasp of basic numerical linear algebra and matrix factorizations. Factoring a matrix into a product of simpler matrices is a crucial tool in numerical linear algebra, because it allows us to tackle complex problems by solving a sequence of easier ones. The main characteristics of this book are as follows:
It is self-contained, only assuming that readers have completed first-year calculus and an introductory course on linear algebra, and that they have some experience with solving mathematical problems on a computer. The book provides detailed proofs of virtually all results. Further, its respective parts can be used independently, making it suitable for self-study.
The book consists of 15 chapters, divided into five thematically oriented parts. The chapters are designed for a one-week-per-chapter, one-semester course. To facilitate self-study, an introductory chapter includes a brief review of linear algebra.


Caracteristici

Detailed proofs of practically all results making it suitable for self-study Detailed algorithms in Matlab The book is self-contained, assuming only first year calculus