Linear Algebra Done Right, 4th Edition

Linear Algebra Done Right, 4th Edition

The fourth edition of Linear Algebra Done Right contains over 250 new exercises and over 70 new examples, along with several new topics and multiple improvements throughout the book.

Publication date: 09 Mar 2025

ISBN-10: n/a

ISBN-13: n/a

Paperback: 404 pages

Views: 583

Type: Textbook

Publisher: Springer-Verlag GmbH

License: Creative Commons Attribution-NonCommercial 4.0 International

Post time: 19 Mar 2025 12:00:00

Linear Algebra Done Right, 4th Edition

Linear Algebra Done Right, 4th Edition The fourth edition of Linear Algebra Done Right contains over 250 new exercises and over 70 new examples, along with several new topics and multiple improvements throughout the book.
Tag(s): Linear Algebra
Publication date: 09 Mar 2025
ISBN-10: n/a
ISBN-13: n/a
Paperback: 404 pages
Views: 583
Document Type: Textbook
Publisher: Springer-Verlag GmbH
License: Creative Commons Attribution-NonCommercial 4.0 International
Post time: 19 Mar 2025 12:00:00
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Excerpts from the Introduction:
This best-selling textbook for a second course in linear algebra is aimed at undergraduate math majors and graduate students. The novel approach taken here banishes determinants to the end of the book. The text focuses on the central goal of linear algebra: understanding the structure of linear operators on finite-dimensional vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra.

No prerequisites are assumed other than the usual demand for suitable mathematical maturity. Thus the text starts by discussing vector spaces, linear independence, span, basis, and dimension. The book then deals with linear maps, eigenvalues, and eigenvectors. Inner product spaces are then introduced, leading to the finite-dimensional spectral theorem and its consequences such as the singular value decomposition. Generalized eigenvectors are then used to provide insight into the structure of a linear operator. Determinants are cleanly introduced via alternating multilinear forms.




About The Author(s)


Axler is a Fellow of the American Mathematical Society and has been a recipient of numerous grants from the National Science Foundation. Axler joined San Francisco State University as chair of the Mathematics Department in 1997. He served as dean of the College of Science & Engineering from 2002 to 2015, when he returned to a regular faculty appointment as a professor in the Mathematics Department.

Sheldon Axler

Axler is a Fellow of the American Mathematical Society and has been a recipient of numerous grants from the National Science Foundation. Axler joined San Francisco State University as chair of the Mathematics Department in 1997. He served as dean of the College of Science & Engineering from 2002 to 2015, when he returned to a regular faculty appointment as a professor in the Mathematics Department.


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