Proof: Relationship between a linear map and the associated matrix

Join the discussion
Ask a follow-up here, or get your own question answered by working scientists, mathematicians and engineers — people, not an autocomplete.
Real named experts · corrections over time · the nuance an AI answer skips
5 replies · 2K views
Fochina
Messages
4
Reaction score
1
Hi!
I don't understand how to demonstrate the following exercise.

Let ##F: R^{n} \rightarrow R^{n}## be a linear map which is invertible. Show that if ##A## is the matrix associated with ##F##, then ##A^{-1}## is the matrix associated with the inverse of ##F##.
 
on Phys.org
Fochina said:
Hi!
I don't understand how to demonstrate the following exercise.

Let ##F: R^{n} \rightarrow R^{n}## be a linear map which is invertible. Show that if ##A## is the matrix associated with ##F##, then ##A^{-1}## is the matrix associated with the inverse of ##F##.
:welcome:

I've aksed that this be moved to Homework. In the meantime, we'd like to see your best effort at proving this.
 
  • Like
Likes   Reactions: berkeman
I am very confused and I don't know if my attempt makes sense:

Composing linear maps corresponds to multiplying the associated matrix each other.
So we know that ##A## is the associated matrix with ##F## and ##F^{-1}F=I##.
Now we have that ##XA## must be the matrix associated with the identity map (the identity matrix)
and becouse ##X## must be equal to ##A^{-1}## and each matrix has a unique linear map associated, from this
it follow that ##A^{-1}## is the matrix associated to ##f^{-1}##
 
Fochina said:
Composing linear maps corresponds to multiplying the associated matrix each other.

This is the key. If you know this, then the rest should follow.

Fochina said:
So we know that ##A## is the associated matrix with ##F## and ##F^{-1}F=I##.
Now we have that ##XA## must be the matrix associated with the identity map (the identity matrix)
and becouse ##X## must be equal to ##A^{-1}## and each matrix has a unique linear map associated, from this
it follow that ##A^{-1}## is the matrix associated to ##f^{-1}##

This is the right idea. What you have done is:

Note that we are talking about the matrices representing the linear transformations in some fixed basis:

Let ##A## be the matrix representing ##F## and ##X## be the matrix representing ##F^{-1}##.

##AX## is the matrix representing ##FF^{-1}##, which is the identity transformation, hence ##AX = I## (the identity matrix) and we see that ##X = A^{-1}##.

That just seems a bit neater.
 
Last edited:
  • Like
Likes   Reactions: Fochina
okok the answer now seems clear to me. I've made a lot of confusion for something that now seems very simple. thank you very much :biggrin:
 
  • Like
Likes   Reactions: PeroK
I think it should be pointed out that there is not ONE "matrix associated with a linear transformation" (that is, a matrix, A, associated with linear transformation, T, such that if T(x)= y then Ax= y). If T is a linear transformation from vector space V to vector space U then the matrix representation depends upon the bases selected for both V and U.

Of course, it is still true that if A is the matrix representing invertible linear transformation T in given bases then A is an invertible matrix and [tex]A^{-1}[/tex] is the matrix representing [tex]T^{-1}[/tex] using those same bases.