Matrix representation of a linear mapping

In summary, the conversation discusses the use of matrix representation of the identity transformation to go from a vector with coordinates relative to one basis to another. The statement is believed to be true but requires further proof, possibly using the fact that the mapping is invertible. There is also a discussion about whether to focus on the elements or the vector space itself. Finally, the conversation touches on the use of a linear function to find the matrix representation.
  • #1
JD_PM
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Homework Statement
Is the following statement true or false? If it is the former case, prove it. If it is later, give a counterexample.

Let ##n \in \aleph_0## and ##L:\Re^{n} \rightarrow \Re^{n}## be an injective linear mapping. Let ##A \in \Re^{n \times n}## be an invertible matrix. Then there is a basis ##\alpha## of ##\Re^{n}## and a basis ##\beta## of ##\Re^{n}## such that ##A = L_{\alpha}^{\beta}##
Relevant Equations
Please check out diagram
I know that to go from a vector with coordinates relative to a basis ##\alpha## to a vector with coordinates relative to a basis ##\beta## we can use the matrix representation of the identity transformation: ##\Big( Id \Big)_{\alpha}^{\beta}##.

This can be represented by a diagram:

Screenshot (977).png


Thus note that the linear mapping we are interested in is ##A:X \rightarrow X'##, where:

$$A = \Big( Id \Big)_{\alpha}^{\beta}$$

I think that the statement is true but I think I should use the fact that ##A## is invertible somehow on the above equation in order to prove it. But how?
 
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  • #2
I think it is true (not entirely sure) , but I think it will take some technical work to prove it. Try to play around with the formula's for change of base and try to come up with "inverse" formulas for them.
 
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  • #3
JD_PM said:
Thus note that the linear mapping we are interested in is ##A:X \rightarrow X'##, where:

$$A = \Big( Id \Big)_{\alpha}^{\beta}$$

I think that the statement is true but I think I should use the fact that ##A## is invertible somehow on the above equation in order to prove it. But how?
Interested in for what? How does ##L## fit in here?
 
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  • #4
Math_QED said:
I think it is true (not entirely sure) , but I think it will take some technical work to prove it. Try to play around with the formula's for change of base and try to come up with "inverse" formulas for them.

Alright thanks, I'll think about it and post what I get.
 
  • #5
vela said:
Interested in for what?

I thought ##A:X \rightarrow X'## was the linear mapping we were interested in because it is the one that involves elements of ##\Re^{n}##.

But recently I've been thinking that I shouldn't focus on the elements but in the vector space ##\Re^{n}## itself. In other words: I think I should focus on ##L:\Re^{n} \rightarrow \Re^{n}## instead of ##A:X \rightarrow X'##.
 
  • #6
Given a linear function, F(X), from [itex]R^n->R^m[/itex], and bases for both [itex]R^n[/itex] and [itex]R^m[/itex], then there is a unique matrix with m rows and n columns that represents that function. To find the entries in the matrix, apply the function, F, to each basis vector of [itex]R^n[/itex] in turn, writing the result as a linear combination of the basis vectors of [itex]R^m[/itex]. The coefficients of each such linear combination give one column of the mtatrix.
 
  • #7
HallsofIvy said:
Given a linear function, F(X), from [itex]R^n->R^m[/itex], and bases for both [itex]R^n[/itex] and [itex]R^m[/itex], then there is a unique matrix with m rows and n columns that represents that function. To find the entries in the matrix, apply the function, F, to each basis vector of [itex]R^n[/itex] in turn, writing the result as a linear combination of the basis vectors of [itex]R^m[/itex]. The coefficients of each such linear combination give one column of the mtatrix.

The question does not ask this, but rather the converse. Can you find bases such that a linear transformation has a given matrix?
 

1. What is a matrix representation of a linear mapping?

A matrix representation of a linear mapping is a way to represent a linear transformation between vector spaces using a matrix. It is a concise and efficient way to represent a linear mapping, and it allows for easy calculations and manipulations of the transformation.

2. How is a matrix representation of a linear mapping related to linear algebra?

A matrix representation of a linear mapping is closely related to linear algebra because it involves the use of matrices, which are fundamental objects in linear algebra. By representing a linear mapping as a matrix, we can apply various linear algebra operations and techniques to analyze and manipulate the transformation.

3. What is the significance of the dimensions of a matrix representation in linear mapping?

The dimensions of a matrix representation in linear mapping are significant because they determine the number of inputs and outputs of the linear transformation. The number of columns in the matrix corresponds to the dimension of the input vector space, while the number of rows corresponds to the dimension of the output vector space.

4. How do you determine the matrix representation of a given linear mapping?

To determine the matrix representation of a given linear mapping, you can apply the transformation to a basis of the input vector space and express the resulting vectors as a linear combination of the basis vectors of the output vector space. The coefficients of this linear combination will form the columns of the matrix representation.

5. Can a matrix representation of a linear mapping be used to perform calculations?

Yes, a matrix representation of a linear mapping can be used to perform calculations such as composition of linear mappings, finding the inverse of a linear transformation, and solving systems of linear equations. These calculations are made easier by representing the transformation as a matrix.

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