Prerequisites for the textbook "Linear Algebra" (2nd Edition)?

[DartomicTech]

Summary:: What pre-requisites are required in order to learn the textbook
"Linear Algebra (2nd Edition) 2nd Edition
by Kenneth M Hoffman (Author), Ray Kunze (Author)"

Sorry if this is the wrong section to ask what the title and subject state. I read some of chapter 1 already, and that all made sense to me. But I don't want to actually start studying it, only to get to a point where I realize that I am missing a lot of needed knowledge to proceed learning the rest of the book.

 

[fresh_42]

It looks pretty basic to me, so there shouldn't be a problem. Some content seems to be a bit biased towards mathematics, which is o.k. if you study mathematics, but might not be necessary if you study physics.

 

[DartomicTech]

It looks pretty basic to me, so there shouldn't be a problem. Some content seems to be a bit biased towards mathematics, which is o.k. if you study mathematics, but might not be necessary if you study physics.

Thanks!

 

[jasonRF]

In the US that book tends to be used for upper-division, second courses in linear algebra. It requires you are comfortable with constructing proofs and reading abstract mathematics. If this is your first time learning linear algebra and you aren't comfortable with proofs yet, then I would recommend an easier book.

jason

 

[MidgetDwarf]

I prefer the book Linear Algebra Done Right. No prerequisite, only familiarity with proofs.

Its only shortcoming(can be a strength) is that determinants are relegated to the back of the book. You can easy supplement this with a book such as Artin: Algebra. Chapter 1 talks about matrices and determinants. Chapter 3 Vector Spaces, and Chapter 4 Linear Transformations and its properties.

To get concrete examples in R^n, you can view Vector Calculus, Linear Algebra, Differential Forms by Hubbard and Hubbard.

These books complement each other well, and you can learn quite a bit of mathematics doing so.

Hubbard and Hubbard is an interesting math book. A must on any shelf. It made it obvious to me that any linear transformation from R^n to R^m can be represented as T(v)=[T]v, where v is the column vector which is an element of R^n, [T] is the mxn matrix associated to the linear transformation T.