Matrix Analysis and Applied Linear Algebra
by C. D. Meyer · first published 2000
86% recommend14% neutral
Why HN recommends it
Concise advanced linear algebra text covering more depth than Strang, with clear exposition and many worked examples; suits readers moving past an introductory course, including applied math and computing practitioners.
Summarised by Claude from recommending comments
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…esoteric and I still haven't hit a situation where it's strictly necessary. The 2-norm seem adequate for most situations and comes out naturally from the least squares problem PS: Strang is great. Meyer's "Matrix analysis and applied linear algebra" is even better (but more advanced)
on “Getting started with linear algebra” ↗…understanding of what the SVD was or why I should care about eigenvalues. I definitely think it's a good place to start though, but if you want something a little more organized I'd really recommend. "Matrix Analysis and Applied Linear Algebra" http://www.amazon.com/Matrix-Analysis-Applied-Algebra-Soluti... It's very clearly written and all the proofs are not too long nor too short. It pretty quickly goes through all the stuff Strang covers…
on “The Best Textbooks on Every Subject” ↗…http://netlib.org/minpack/index.html * ODEPACK (ordinary differential equations) http://netlib.org/odepack/index.html Most of the textbooks on numerical methods are not very approachable, but Carl Meyer's Matrix Analysis and Applied Linear Algebra text is a good start, as are Gilbert Strang's books.
on “Multiprocessing in Python with Fortran and OpenMP” ↗…context of mathematics courses. They are not introductory in applied mathematical sciences contexts. There I would suggest, e.g., Boyd and Vandenberghe’s Introduction to Applied Linear Algebra [1], Meyer’s Matrix Analysis and Applied Linear Algebra [2], Golub and Van Loan’s Matrix Computations [3] etc. > But the book doesn’t like determinants and isn’t focused on computing things around matrices [...]. This is purposeful. [1] https://web.stanford.edu/~boyd/vmls…
on “You're not dumb, the prerequisites are bullshit” ↗A slightly more advance books which I love is: Matrix Analysis and Applied Linear Algebra by Carl Meyer. http://www.amazon.com/gp/product/0898714540 You can find a free PDF version online. I like it more then Strang because it's a lot more concise, covers some more advanced topics and unlike…
on “Surprises from numerical linear algebra (2010)” ↗have you looked at "Matrix Analysis and Applied Linear Algebra" by Carl Meyer? it seems very in line with what you're saying and the exercises are absolutely excellent. I've slowly made my way half way through the book and am very happy over all
on “Immersive Linear Algebra (2016)” ↗…coverage and has fewer problems. Definitely still worth reading through especially since the problems lend themselves well to working through with Julia or Python or similar. For further reading, try Matrix Analysis and Applied Linear Algebra by Meyer.
on “Introduction to Linear Algebra for Applied Machine Learning with Python” ↗I think you'll enjoy Matrix Analysis and Applied Linear Algebra from Carl Meyer: http://matrixanalysis.com/ Almost every topic is illustrated with an application or illustrative example, and the applications are very interesting.
on “Ask HN: Best beginner friendly linear algebra book?” ↗I really recommend Matrix Analysis and Applied Linear Algebra by Carl Meyer. It's both concise and comprehensive. Strange is very good, but feels kinda vague and long winded in comparison (very good for a high level understanding of the tools you're dealing with)
on “Linear Algebra Done Right – Sheldon Axler” ↗The following quote is from "Matrix analysis and applied linear algebra" by Carl D Meyer, p 423-424. "Generalized inverses are useful in formulating theoretical statements as those above, ut, just as in the case of the ordinary inverse, generalized inverses are not practical…
on “Statistics: Losing Ground to CS, Losing Image Among Students” ↗