For those who find Linear Algebra Done Right too much to start with, and those who don't get why Strang starts with matrices, I can't recommend more "The dark art of linear algebra" read this first. With this you can then tackle every other book on the topic more easily
Strang is simpler and clearer. Axler is more advanced in the sense that it doesn’t tie it to matrices. Strange is a “first course” book, Axler is a second course.
Depends how you think. I found Strang impenetrable and Axler simple and lucid. Some people seem to find abstract vector spaces weird and unmotivated without doing a load of stuff with lists and grids of numbers first. I find determinants weird and unmotivated without learning exterior algebra first. I wish Axler had been my first course.
Lately been deep diving into linear algebra. And a way which i engage with it is that I tell AI to generate interactive examples + questions on Lean or Haskell. Its so fun, just deriving the intuition in these languages.
Overrated and tendentious book. There are many better linear algebra texts. His polemic against determinants is poorly motivated, misguided, and distracting. The writing is quite formal and not terribly inspiring. The coverage is adequate but nothing more.
I found it really insightful (and always overlooked) to distinguish between vector and co-vector spaces. It doesn't necessarily produce new knowledge, but makes things more clear.
For those who find Linear Algebra Done Right too much to start with, and those who don't get why Strang starts with matrices, I can't recommend more "The dark art of linear algebra" read this first. With this you can then tackle every other book on the topic more easily
Last night I was looking into what to read after or along with 3Blue1Brown's series of Linear algebra videos [1]
The contenders seems to be:
- Linear Algebra Done Right - Sheldon Axler
- Liner Algebra Done Wrong - Sergei Treil
- Introduction to Linea Algebra - Gilbert Strang
- Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe
[1] https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x...
You zoomers are making a list of linear algebra books and not citing Lang? Get off my lawn ;)
+1 for Boyd
Strang is simpler and clearer. Axler is more advanced in the sense that it doesn’t tie it to matrices. Strange is a “first course” book, Axler is a second course.
Depends how you think. I found Strang impenetrable and Axler simple and lucid. Some people seem to find abstract vector spaces weird and unmotivated without doing a load of stuff with lists and grids of numbers first. I find determinants weird and unmotivated without learning exterior algebra first. I wish Axler had been my first course.
Axler does limit itself to vector spaces over real and complex fields, though.
That‘s fine, but I would have appreciated notices, which proofs and theorems do not hold in the general case.
It‘s an exercise for the reader.
Lorenzo Sadun's Linear Algebra: The Decoupling Principle would probably be enough too if you added something about determinants.
+1 for Strang.
Previously on Hacker News:
Linear Algebra Done Right 58 points, July 2023, 4 comments https://news.ycombinator.com/item?id=36576114
Linear Algebra Done Right – 4th Edition, 631 points, Oct 2023, 294 comments https://news.ycombinator.com/item?id=38060159
Linear Algebra Done Right [pdf], 85 points, Sept 2024, 39 comments https://news.ycombinator.com/item?id=41416799
Previously in my ~/Downloads:
linear_algebra_done_right.pdf, 0 pages read, July 2023
linear_algebra_done_right (1).pdf, 0 pages read, Oct 2023
linear_algebra_done_right (2).pdf, 0 pages read, Sept 2024
Downloading (3) now.
Lately been deep diving into linear algebra. And a way which i engage with it is that I tell AI to generate interactive examples + questions on Lean or Haskell. Its so fun, just deriving the intuition in these languages.
Overrated and tendentious book. There are many better linear algebra texts. His polemic against determinants is poorly motivated, misguided, and distracting. The writing is quite formal and not terribly inspiring. The coverage is adequate but nothing more.
I found it really insightful (and always overlooked) to distinguish between vector and co-vector spaces. It doesn't necessarily produce new knowledge, but makes things more clear.
Kindle format link == 404
I passed the class just because of how good the book is.
These days, linear algebra done right should be accompanied with some CAS to view how algorithms are used.
Possibly paired with some numerical algebra free text (many on the Internet)
Do not get the latest edition, the layout and typesetting is atrocious!