Linear Algebra Done Right 2nd Edition Pdf

[Tyler Bannowsky]

Nowavailable in Open Access, this best-selling textbook for a second course in linear algebra is aimed at undergraduate math majors and graduate students. The fourth edition gives an expanded treatment of the singular value decomposition and its consequences. It includes a new chapter on multilinear algebra, treating bilinear forms, quadratic forms, tensor products, and an approach to determinants via alternating multilinear forms. This new edition also increases the use of the minimal polynomial to provide cleaner proofs of multiple results. Also, over 250 new exercises have been added.

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 simplify proofs. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. Beautiful formatting creates pages with an unusually student-friendly appearance in both print and electronic versions.

No prerequisites are assumed other than the usual demand for suitable mathematical maturity. 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.

Axler has served as Editor-in-Chief of the Mathematical Intelligencer and Associate Editor of the American Mathematical Monthly. He has been a member of the Council of the American Mathematical Society and a member of the Board of Trustees of the Mathematical Sciences Research Institute. He is a Fellow of the American Mathematical Society and has been a recipient of numerous grants from the National Science Foundation.

Is this book going to be bad for me, then?Or, should I read another linear algebra book after reading it? I want to avoid reading two linear algebra books because reading such a textbook consumes a lot of time.

It's unconventional in the sense that it works mostly with lists, as opposed to sets (a minor adjustment that makes certain proofs, like the complex spectral theorem, easier) and it avoids determinants until the very end. Also, by developing the theory of linear transformations first, then about matrices, it really emphasizes a key thought to keep in mind with linear algebra: Think in terms of linear transformations, compute with matrices. It's a very good book and easy to follow. And even when he skips a few steps, he explicitly says, "I'm skipping steps here, you should do it" so you aren't left feeling lost.

$0$. The main feature of Axler's book is its determinant-free development of the theory which leads to cleaner proofs. I am sure that you will enjoy this approach. However, determinant has its own theoretical importance and neglecting it is not possible and not even beneficial!

$2$. Axler prefers writing proofs with words instead of equations! I mean that he likes using words and the mind of reader instead of writing it down. As an example see this post. This may be a little annoying for some beginners or those who prefer detailed equations instead of words. Also, this may cause you feeling lost in some places when this tradition combines with the typos in the proof! However, I might say that there are really elegant proofs in the book too!

$3$. Axler's book is different in most of the aspects from the all books on linear algebra so it may cause you confusion when you want to take a look at other resources for reviewing or learning some topics. However, in most of the cases he mentions the differences. One of the differences not mentioned in the text (but mentioned in the preface for instructor) is the definition of polynomials.

$4$. The material is a little insufficient to me. No topic about multi-linear forms and tensor products is included! No examples or discussions are made for vector spaces over finite fields! No emphasis is made in the book on algebraic structures like fields, modules, rings, groups and algebras that one should know in a theoretical book. Also, some important concepts like double dual space are not in the text and just some exercises are included for them. Also, there is nothing about the inverse matrix of an operator in the book! Worse than that is you do not get used to work with matrices and linear algebraic equations in this book. I mean come on, no Gaussian elimination, no LU and related decompositions! Although the Gram-Schmidt procedure is mentioned, its relevant decomposition, the QR decomposition, is not addressed. In general, the book does not give you matrix pictures so much! I understand that Axler is trying to emphasize the abstraction of the concept of the vector space; however, these pictures really help you to keep the ideas in mind and have some examples for yourself!

$4$. Also there is no solution manual of the book yet! So you are on your own when dealing with exercises. But I would say that there are nice exercises in the book so be sure to look at them while reading the book.

This edition has major improvements and I am really satisfied with it. He has tried to give more geometric intuitions. As an example, see how the norm of an operator is related to deformation of a ball to an ellipsoid on Chapter 7. QR decomposition is included. SVD is treated with more details and in a more general form for operators from a linear space to a possibly different space and used to prove the Polar decomposition neatly. However, the polar decomposition is still proved for operators but it could be proved for more general linear maps and then specialized to such operators. Determinants are treated by multi-linear forms which is a much better approach than the previous edition. The only thing is that I am not really a fan of Axler anti determinant point of view. Tensor products are also introduced. I can just say "Well Done, Sheldon"!

If you have encountered a standard abstract linear algebra course, then it is fun to go over Axler's Linear Algebra Done Right to see how the theory can be presented in a different way. Axler's book has the potential to be the best linear algebra book ever and it has improved substantially over time!

Perfectionism, Elegance, and Extreme Beauty. Anyone reading this book will discover how the author has put great efforts in perfecting every detail: every proof has been scrutinized and polished again and again, to make them as elegant as possible; every example has been greatly considered and carefully selected; the arrangement of materials is neat and compact, without any waste of words; the $3$rd edition features luxuriously beautiful formatting, like usage of colors and boxes, which is rare among theory books. I would also like to mention that every theorem in this book has a descriptive name. For example, see this theorem on page $281$ of the book:

The article said that the book contains insufficient materials for a second course in linear algebra, and concluded that it is more suitable as a textbook for a challenging first course. Well, this I agree indeed. I agree that the book is best used in a challenging and demanding first course on linear algebra, as I have talked earlier.

I read Axler in my undergraduate and started a M.S. statistics program this year, and have read up on data science. I would consider data science and stats to have very, very similar applications for linear algebra.

As for the actual question, I didn't find Axler at all useful for learning linear algebra for the reasons I needed it (statistics, data science). I recall reading in a review of Axler's book that it is more of an algebraic take of linear algebra: from what I understand of "algebraic," I think this is a valid point. There is little emphasis (from what I recall of that book) of things you would find useful in data science (and should really know), such as $QR$ decompositions, $LU$ decompositions, singular-value decompositions, generalized inverses, pseudoinverses, calculating the rank of a matrix, etc. That isn't to say Axler couldn't help you with this, but Axler, from what I recall, doesn't cover a lot of this material that I'm mentioning here.

MIT OCW has a "Scholar's" (independent study) edition of Strang's Linear Algebra course, including videos of his lectures, grad student recitations, notes, homework, and exams (with solutions for homework and exams). It's free, but non-credit. Can be used with the 4th or 5th edition of Strang's book. Strang uses the traditional, determinants-early approach.

FWIW, Benedict Gross, in his Abstract Algebra lectures expressed his preference for the "done right" approach to the prerequisite Linear Algebra course to facilitate better understanding of Abstract Algebra.

As the other answers point out, this is a theoretical book. If you don't think you will ever need to write a proof, and are only interested in computational linear algebra and its applications, then this book may not be for you.

However, if you do appreciate proofs and theoretical understanding, then I recommend learning the theory of linear algebra before worrying about the applied stuff, and this is a good book for that. Axler's book won't teach you everything you might ever want to know about linear algebra, but it will provide the foundations necessary to make it relatively easy to learn anything from linear algebra not in his book. For instance, much of the applied stuff you can learn on demand as you need it, from Wikipedia and other sources, so long as you have a good theoretical foundation.

I should point out that Axler's book was unfortunately not my first exposure to linear algebra. My first exposure was with Friedberg, Insel and Spence (FIS). My experience with FIS was miserable, and I still have PTSD from it. I think that FIS might be tolerable if you have a really good instructor to guide you through the jungle. My instructor was not great and I would never even consider FIS for self-study as a beginner.

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