# Linear Algebra Done Right / by Sheldon Axler.

Material type: TextSeries: Undergraduate Texts in MathematicsPublisher: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 3rd ed. 2015Description: 1 online resource (XVII, 340 pages 26 illustrations in color.)Content type:- text

- computer

- online resource

- 9783319110790

- 512.5 23 AXL

Item type | Current library | Collection | Call number | Copy number | Status | Notes | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|---|---|

Reference | IIT Goa Central Library | Technical | 512.5/AXL (Browse shelf(Opens below)) | 1 | Reference | TTPP/307/2021-22|| 04-02-22|| 32.50%|| EUR 44.99 | 3988 |

This best-selling textbook for a second course in linear algebra is aimed at undergrad 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. The third edition contains major improvements and revisions throughout the book. More than 300 new exercises have been added since the previous edition. Many new examples have been added to illustrate the key ideas of linear algebra. New topics covered in the book include product spaces, quotient spaces, and dual spaces. Beautiful new formatting creates pages with an unusually pleasant appearance in both print and electronic versions. 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 introduced, leading to the finite-dimensional spectral theorem and its consequences. Generalized eigenvectors are then used to provide insight into the structure of a linear operator.

Description based on publisher-supplied MARC data.