Sinossi
This new book offers a fresh approach to matrix and linear algebra by providing a balanced blend of applications, theory, and computation, while highlighting their interdependence. Intended for a one-semester course, Applied Linear Algebra and Matrix Analysis places special emphasis on linear algebra as an experimental science, with numerous examples, computer exercises, and projects. While the flavor is heavily computational and experimental, the text is independent of specific hardware or software platforms. Throughout the book, significant motivating examples are woven into the text, and each section ends with a set of exercises.
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Informazioni sull?autore
Thomas S. Shores is Professor Emeritus of Mathematics at the University of Nebraska–Lincoln, where he has received awards for his teaching. His research touches on group theory, commutative algebra, mathematical modeling, numerical analysis, and inverse theory.
Dalla quarta di copertina
In its second edition, this textbook offers a fresh approach to matrix and linear algebra. Its blend of theory, computational exercises, and analytical writing projects is designed to highlight the interplay between these aspects of an application. This approach places special emphasis on linear algebra as an experimental science that provides tools for solving concrete problems.
The second edition’s revised text discusses applications of linear algebra like graph theory and network modeling methods used in Google’s PageRank algorithm. Other new materials include modeling examples of diffusive processes, linear programming, image processing, digital signal processing, and Fourier analysis. These topics are woven into the core material of Gaussian elimination and other matrix operations; eigenvalues, eigenvectors, and discrete dynamical systems; and the geometrical aspects of vector spaces.
Intended for a one-semester undergraduate course without a strict calculus prerequisite, Applied Linear Algebra and Matrix Analysis augments the key elements of linear algebra with a wide choice of optional sections. With the book’s selection of applications and platform-independent assignments, instructors can tailor the curriculum to suit specific interests and ensure students across various disciplines are equipped with the powerful tools of linear algebra.
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Applied Linear Algebra and Matrix Analysis (Undergraduate Texts in Mathematics)
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APPLIED LINEAR ALGEBRA AND MATRIX ANALYSIS 2ED (HB 2018)
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Applied Linear Algebra and Matrix Analysis
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Springer, Springer Mai 2018, 2018
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ISBN 13: 9783319747477
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In its second edition, this textbook offers a fresh approach to matrix and linear algebra. Its blend of theory, computational exercises, and analytical writing projects is designed to highlight the interplay between these aspects of an application. This approach places special emphasis on linear algebra as an experimental science that provides tools for solving concrete problems.The second edition's revised text discusses applications of linear algebra like graph theory and network modeling methods used in Google's PageRank algorithm. Other new materials include modeling examples of diffusive processes, linear programming, image processing, digital signal processing, and Fourier analysis. These topics are woven into the core material of Gaussian elimination and other matrix operations; eigenvalues, eigenvectors, and discrete dynamical systems; and the geometrical aspects of vector spaces.Intended for a one-semester undergraduate course without a strict calculus prerequisite,Applied Linear Algebra and Matrix Analysisaugments the key elements of linear algebra with a wide choice of optional sections. With the book's selection of applications and platform-independent assignments, instructors can tailor the curriculum to suit specific interests and ensure students across various disciplines are equipped with the powerful tools of linear algebra. 492 pp. Englisch. Codice articolo 9783319747477
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Applied Linear Algebra and Matrix Analysis
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