Matrix analysis and applied linear algebra

by Carl D. Meyer

This book avoids the traditional definition-theorem-proof format; instead a fresh approach introduces a variety of problems and examples all in a clear and informal style. The in-depth focus on applications separates this book from others, and helps students to see how linear algebra can be applied to real-life situations. Some of the more contemporary topics of applied linear algebra are included here which are not normally found in undergraduate textbooks. Theoretical developments are always accompanied with detailed examples, and each section ends with a number of exercises from which students can gain further insight. Moreover, the inclusion of historical information provides personal insights into the mathematicians who developed this subject. The textbook contains numerous examples and exercises, historical notes, and comments on numerical performance and the possible pitfalls of algorithms. Solutions to all of the exercises are provided, as well as a CD-ROM containing a searchable copy of the textbook.

Genres: Mathematics, Algebra, Textbooks, Science, Nonfiction

730 pages, Textbook Binding

First published June 1, 2000

Reviews

[Simona · Rating 5 out of 5]

This was actually a very pleasant book. Almost every paragraph felt like those useful and rare(at least in the books I have seen) parts of textbooks where the authors try to give you an intuition about the subject, trying to describe what happened in a proof and what would we want to do next. It all felt like reading a pleasant and interesting lecture.

As this is the first Matrix theory book I have seen, I cannot compare it to similar books.

[Tomáš Ševček · Rating 5 out of 5]

This is a great book on matrix analysis. Not only does it cover very interesting real-world applications of linear algebra and matrices, but it also includes a lot of information on camputational/numerical aspects, which are vital to know. I especially liked the material on least squares as it is done geometrically through orthogonal projections, unlike in most statistics/probability courses. Furthermore, the book contains a lot of computational information regarding least squares, which helps when you need to program the method on your own. It is also an excellent choice for someone who wants to revise finite-dimensional linear spaces and operators before delving into functional analysis. In order to fully benefit from the text, it is enough to know calculus. Some applications may go a little deeper (for instance, it is good to be familiar with systems of ODEs when studying matrix functions), but basic calculus knowledge will suffice for the better part.

[Rishabh Soni · Rating 5 out of 5]

Fantastic book for self study. However it gives you only brief idea about numerical methods, use golub's book for that