Proof: Relationship between a linear map and the associated matrix

In summary, when a linear map ##F## is invertible, its associated matrix ##A## is also invertible. The matrix associated with the inverse of ##F## is the inverse of ##A##, and this is true for any chosen bases for the vector spaces involved.
  • #1
Fochina
4
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##.
 
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  • #2
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.
 
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  • #3
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}##
 
  • #4
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.
 
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  • #5
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:
 
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  • #6
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.
 

1. What is a linear map in mathematics?

A linear map, also known as a linear transformation, is a function between two vector spaces that preserves the operations of addition and scalar multiplication. In other words, the output of a linear map is always a linear combination of its inputs.

2. How is a linear map related to an associated matrix?

Every linear map can be represented by a matrix, and every matrix represents a linear map. The columns of the matrix correspond to the images of the standard basis vectors in the output space, and the linear combination of these columns gives the output of the linear map for any input vector.

3. How can you determine the associated matrix of a linear map?

To determine the associated matrix of a linear map, you can apply the linear map to the standard basis vectors in the input space and write down the resulting vectors in the output space. These vectors will form the columns of the associated matrix.

4. What is the relationship between the dimensions of the input and output spaces and the associated matrix?

If the input space has m dimensions and the output space has n dimensions, then the associated matrix will have m rows and n columns. This is because the matrix is used to transform vectors from the input space to the output space, and the dimensions of the matrix must match the dimensions of the spaces.

5. Can a linear map be represented by multiple associated matrices?

No, a linear map can only be represented by one associated matrix. This is because the associated matrix is unique for a given linear map and input/output spaces. Different matrices may represent different linear maps or the same linear map with different bases for the input/output spaces.

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