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Matrix fundamentals = from equation ...

Saff, E. B.

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Matrix fundamentals = from equation solving to signal processing /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Matrix fundamentals/ by Edward Barry Saff, Arthur David Snider.
Reminder of title:	from equation solving to signal processing /
Author:	Saff, E. B.
other author:	Snider, Arthur David.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xi, 392 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Part I -- Introduction: Three Examples -- 1 Systems of Linear Algebraic Equations -- 2 Matrix Algebra -- Part II -- Introduction: The Structure of General Solutions to Linear Algebraic Equations -- 3 Vector Spaces -- 4 Orthogonality in Real Vector Spaces -- Part III -- Introduction: Through the Looking Glass -- 5 Eigenvectors and Eigenvalues of Real Matrices -- 6 Similarity -- 7 Linear Systems of Differential Equations.
Contained By:	Springer Nature eBook
Subject:	Matrices. -
Online resource:	https://doi.org/10.1007/978-3-031-97222-5
ISBN:	9783031972225

Matrix fundamentals = from equation solving to signal processing /
Saff, E. B.

Matrix fundamentalsfrom equation solving to signal processing /[electronic resource] :by Edward Barry Saff, Arthur David Snider. - Second edition. - Cham :Springer Nature Switzerland :2025. - xi, 392 p. :ill. (some col.), digital ;24 cm. - Springer undergraduate mathematics series,2197-4144. - Springer undergraduate mathematics series..

Part I -- Introduction: Three Examples -- 1 Systems of Linear Algebraic Equations -- 2 Matrix Algebra -- Part II -- Introduction: The Structure of General Solutions to Linear Algebraic Equations -- 3 Vector Spaces -- 4 Orthogonality in Real Vector Spaces -- Part III -- Introduction: Through the Looking Glass -- 5 Eigenvectors and Eigenvalues of Real Matrices -- 6 Similarity -- 7 Linear Systems of Differential Equations.

Matrix Fundamentals introduces tools for working with matrices, their applications, and their significance in the broader context of linear algebra. Assuming no previous exposure to matrices, the first four chapters provide a foundation accessible to students with a basic knowledge of calculus, covering essential matrix methods used in various quantitative fields. The book formulates algorithms and discusses their practical implementation. Later chapters introduce more advanced topics, such as singular value decomposition, along with some modern applications. Emphasizing visualization and experimentation, this text is designed for undergraduate courses for students in STEM, as well as business, economics and social sciences.

ISBN: 9783031972225

Standard No.: 10.1007/978-3-031-97222-5doiSubjects--Topical Terms:

516894
Matrices.

LC Class. No.: QA188

Dewey Class. No.: 512.9434

Matrix fundamentals = from equation solving to signal processing /
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245 1 0 $a Matrix fundamentals $h [electronic resource] : $b from equation solving to signal processing / $c by Edward Barry Saff, Arthur David Snider.
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300 $a xi, 392 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer undergraduate mathematics series, $x 2197-4144
505 0 $a Part I -- Introduction: Three Examples -- 1 Systems of Linear Algebraic Equations -- 2 Matrix Algebra -- Part II -- Introduction: The Structure of General Solutions to Linear Algebraic Equations -- 3 Vector Spaces -- 4 Orthogonality in Real Vector Spaces -- Part III -- Introduction: Through the Looking Glass -- 5 Eigenvectors and Eigenvalues of Real Matrices -- 6 Similarity -- 7 Linear Systems of Differential Equations.
520 $a Matrix Fundamentals introduces tools for working with matrices, their applications, and their significance in the broader context of linear algebra. Assuming no previous exposure to matrices, the first four chapters provide a foundation accessible to students with a basic knowledge of calculus, covering essential matrix methods used in various quantitative fields. The book formulates algorithms and discusses their practical implementation. Later chapters introduce more advanced topics, such as singular value decomposition, along with some modern applications. Emphasizing visualization and experimentation, this text is designed for undergraduate courses for students in STEM, as well as business, economics and social sciences.
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