國立虎尾科技大學 |

Applied linear algebra and matrix methods

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Applied linear algebra and matrix methods/ by Timothy G. Feeman.
Author:	Feeman, Timothy G.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xiii, 321 p. :illustrations, digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Algebras, Linear. -
Online resource:	https://doi.org/10.1007/978-3-031-39562-8
ISBN:	9783031395628

Applied linear algebra and matrix methods
Feeman, Timothy G.

Applied linear algebra and matrix methods[electronic resource] /by Timothy G. Feeman. - Cham :Springer International Publishing :2023. - xiii, 321 p. :illustrations, digital ;24 cm. - Springer undergraduate texts in mathematics and technology,1867-5514. - Springer undergraduate texts in mathematics and technology..

Introduction -- 1. Vectors -- 2. Matrices -- 3. Matrix Contexts -- 4. Linear Systems -- 5. Least Squares and Matrix Geometry. 6. Orthogonal Systems -- 7. Eigenvalues -- 8. Markov Processes -- 9. Symmetric Matrices -- 10. Singular Value Decomposition -- 11. Function Spaces.-Bibliography.-Index.

This textbook is designed for a first course in linear algebra for undergraduate students from a wide range of quantitative and data driven fields. By focusing on applications and implementation, students will be prepared to go on to apply the power of linear algebra in their own discipline. With an ever-increasing need to understand and solve real problems, this text aims to provide a growing and diverse group of students with an applied linear algebra toolkit they can use to successfully grapple with the complex world and the challenging problems that lie ahead. Applications such as least squares problems, information retrieval, linear regression, Markov processes, finding connections in networks, and more, are introduced on a small scale as early as possible and then explored in more generality as projects. Additionally, the book draws on the geometry of vectors and matrices as the basis for the mathematics, with the concept of orthogonality taking center stage. Important matrix factorizations as well as the concepts of eigenvalues and eigenvectors emerge organically from the interplay between matrix computations and geometry. The R files are extra and freely available. They include basic code and templates for many of the in-text examples, most of the projects, and solutions to selected exercises. As much as possible, data sets and matrix entries are included in the files, thus reducing the amount of manual data entry required.

ISBN: 9783031395628

Standard No.: 10.1007/978-3-031-39562-8doiSubjects--Topical Terms:

528115
Algebras, Linear.

LC Class. No.: QA188 / .F44 2023

Dewey Class. No.: 512.5

Applied linear algebra and matrix methods
LDR:02814nam a2200337 a 4500 001 1119867
003 DE-He213
005 20231124151428.0
006 m d
007 cr nn 008maaau
008 240612s2023 sz s 0 eng d
020 $a 9783031395628 $q (electronic bk.)
020 $a 9783031395611 $q (paper)
024 7 $a 10.1007/978-3-031-39562-8 $2 doi
035 $a 978-3-031-39562-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA188 $b .F44 2023
072 7 $a PBF $2 bicssc
072 7 $a MAT002050 $2 bisacsh
072 7 $a PBF $2 thema
082 0 4 $a 512.5 $2 23
090 $a QA188 $b .F295 2023
100 1 $a Feeman, Timothy G. $3 1069893
245 1 0 $a Applied linear algebra and matrix methods $h [electronic resource] / $c by Timothy G. Feeman.
260 $a Cham : $c 2023. $b Springer International Publishing : $b Imprint: Springer,
300 $a xiii, 321 p. : $b illustrations, digital ; $c 24 cm.
490 1 $a Springer undergraduate texts in mathematics and technology, $x 1867-5514
505 0 $a Introduction -- 1. Vectors -- 2. Matrices -- 3. Matrix Contexts -- 4. Linear Systems -- 5. Least Squares and Matrix Geometry. 6. Orthogonal Systems -- 7. Eigenvalues -- 8. Markov Processes -- 9. Symmetric Matrices -- 10. Singular Value Decomposition -- 11. Function Spaces.-Bibliography.-Index.
520 $a This textbook is designed for a first course in linear algebra for undergraduate students from a wide range of quantitative and data driven fields. By focusing on applications and implementation, students will be prepared to go on to apply the power of linear algebra in their own discipline. With an ever-increasing need to understand and solve real problems, this text aims to provide a growing and diverse group of students with an applied linear algebra toolkit they can use to successfully grapple with the complex world and the challenging problems that lie ahead. Applications such as least squares problems, information retrieval, linear regression, Markov processes, finding connections in networks, and more, are introduced on a small scale as early as possible and then explored in more generality as projects. Additionally, the book draws on the geometry of vectors and matrices as the basis for the mathematics, with the concept of orthogonality taking center stage. Important matrix factorizations as well as the concepts of eigenvalues and eigenvectors emerge organically from the interplay between matrix computations and geometry. The R files are extra and freely available. They include basic code and templates for many of the in-text examples, most of the projects, and solutions to selected exercises. As much as possible, data sets and matrix entries are included in the files, thus reducing the amount of manual data entry required.
650 0 $a Algebras, Linear. $3 528115
650 1 4 $a Linear Algebra. $3 1207620
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Springer undergraduate texts in mathematics and technology. $3 1021402
856 4 0 $u https://doi.org/10.1007/978-3-031-39562-8
950 $a Mathematics and Statistics (SpringerNature-11649)