Different matrices for the same linear operator

In summary: Otherwise you can have blocks that are the same size and have the same eigenvalues, but are not the same block.In summary, the two matrices representing the linear operator T on \mathcal{C}^2 in the standard basis and in the basis with vectors \frac{1}{\sqrt{2}} \bmatrix i \\ 1 \endbmatrix, \frac{1}{\sqrt{2}} \bmatrix -i \\ 1 \endbmatrix can be seen as the same operator by using the change of basis matrix v. The new operator in the basis with vectors v is found by computing v* T v/2. The two matrices represent the same operator if they have the same Jordan decomposition
  • #1
octol
61
0
Consider the linear operator T on [tex]\mathcal{C}^2[/tex] with the matrix
[tex] \bmatrix 2 && -3\\3 && 2 \endbmatrix [/tex]
in the standard basis. With the basis vectors
[tex] \frac{1}{\sqrt{2}} \bmatrix i \\ 1 \endbmatrix, \quad \frac{1}{\sqrt{2}} \bmatrix -i \\ 1 \endbmatrix [/tex]
this operator can be written
[tex] \bmatrix 2+3i && 0\\0 && 2-3i \endbmatrix [/tex]

My question is, how can I see this? How can I see that the two matrices represent the same operator? I understand that what these matrices represent is how they transform the basis vectors, e.g., for the first matrix above we have
[tex] T \bmatrix 1 \\ 0 \endbmatrix = 2 \bmatrix 1 \\ 0 \endbmatrix + 3 \bmatrix 0 \\ 1 \endbmatrix. [/tex]

Was along time since I took a class in linear algebra so I have totally forgotten how to think about these things.
 
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  • #2
Let v be the matrix whose columns contain the new basis vectors: [tex] v = \bmatrix i && -i\\1 && 1 \endbmatrix [/tex] and let
v* be the the complex conjugate transpose of v. Compute
v* T v/2, which is the operator in the new basis. In other
words, the matrix v contains the eigenvectors of T and the matrix T
in the new basis contains the eigenvalues on the diagonal.
 
  • #3
Two matrices represent the same operator w.r.t. different bases if and only if they're conjugate. Which is if and only if they have the same Jordan decomposition.
 
  • #4
matt grime said:
Two matrices represent the same operator w.r.t. different bases if and only if they're conjugate. Which is if and only if they have the same Jordan decomposition.

How do you check this explicitly then i the above case? Sorry I'm a bit lost here.
 
  • #5
Sorry, I appear to have written something completely inappropriate.

Just write down the matrix that represents the change of coordinates.

If you have a linear map with matrix M with respect to a basis B_1, and P is the change of basis matrix from B_1 to B_2 for some other basis B_2, then the linear map has matrix PMP^-1 with respect to B_2.
 
  • #6
Lets call the standard basis E and the other basis B. And let's say that in the base E the matrix of T is A and in basis B it's A'. You want to find A'.
If M is the change of coordinates matrix from E to B (which is just a matrix
whose columns are the vectors of B) the A' = M^-1 A M.
 
  • #7
Beyond the fact that I can never get my inverses in the right place, normally, in what way does that say something, daniel, that has not already been said?
 
  • #8
Sorry, I didn't read all the posts before posting :blushing:
 
  • #9
I'm pretty sure that what you're talking about amounts to the two matrices having the same eigenvalues.
 
  • #10
Not at all. It is easy to construct non-conjugate matrices with the same eigen-values, the same number of eigen-vectors for each eigen-value etc.
 
  • #11
daniel_i_l said:
...M is the change of coordinates matrix from E to B (which is just a matrix
whose columns are the vectors of B) ...

Is this really correct? I thought it was the other way around? *confused*
i.e that the matrix to change the basis from B to E is the matrix where the columns are given by the vectors of B?
 
  • #12
octol said:
Is this really correct? I thought it was the other way around? *confused*
i.e that the matrix to change the basis from B to E is the matrix where the columns are given by the vectors of B?
Darned if I know! I, like matt, can never get the inverses in the right place.

matt grime said:
Not at all. It is easy to construct non-conjugate matrices with the same eigen-values, the same number of eigen-vectors for each eigen-value etc.
matt, is it sufficient that the two matrices have the same eigenvalues and the same eigenvectors (not just the same number of eigenvectors) for each eigenvalue?
 
  • #13
All that means is that they have the same number of jordan blocks, and that the e-vectors for each are 'the same' whatever that might mean*. Two matrices are 'the same' if they have identical Jordan decompositions. This is easy to visualize - there is a bijection between blocks that preserves the diagonal element and the size of the block - but hard to formalize succinctly on an ad hoc basis. I suppose we should say

M~N iff for any decompositions

[tex] M \sim \oplus_{i=1}^m J(\lambda_i,r_i)[/tex]

and

[tex] N \sim \oplus_{i=i}^n J(\mu_i,s_i)[/tex]

then, up to reordering, n=m, and [itex] \lambda_i =\mu_i[/itex] and [itex]r_i=s_i[/itex]

where J(x,y) means the jordan block of size y with x in the diagonal.




* I really don't know what you mean by 'the same eigenvectors', to be honest: you give me any set of linearly independent vectors, and any other set with the same cardinality, and there is a change of basis (and usually infinitely many) that maps one to the other.
 
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  • #14
HallsofIvy said:
matt, is it sufficient that the two matrices have the same eigenvalues and the same eigenvectors (not just the same number of eigenvectors) for each eigenvalue?

If two matrices have the same eigenvalues and corresponding eigenvectors, then they must be the same transformation if those eigenvectors are a basis of the space.

However, it's not necessarily the case that the eigenvectors are unique if the eigenvalues aren't distinct. (If there are two eigenvectors [itex]\vec{v}_1,\vec{v}_2[/itex] with the same eigenvalue [itex]\lambda[/tex] then any linear combination of those two will also be an eigenvector with the same eigenvalue - trivial examples include the zero or identity matrix in 2 or more dimensions.)

Not at all. It is easy to construct non-conjugate matrices with the same eigen-values, the same number of eigen-vectors for each eigen-value etc.

Yeah, that only works if the matrix is diagonalizable.
 
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1. What is a linear operator?

A linear operator is a mathematical function that maps one vector space to another in a linear fashion. It is often represented as a matrix and is used to describe transformations in various fields of mathematics and physics.

2. What are matrices for the same linear operator?

Matrices for the same linear operator are different representations of the same function. They are obtained by choosing different bases for the vector spaces involved, and each matrix represents the linear transformation in a specific coordinate system.

3. How can different matrices represent the same linear operator?

Since a linear operator is a function, it can be represented in various ways. Different matrices can represent the same linear operator if they are related by a change of basis or a similarity transformation. This means that the matrices have the same eigenvalues and eigenvectors, and thus, describe the same transformation.

4. What are the implications of having different matrices for the same linear operator?

The existence of different matrices for the same linear operator highlights the importance of choosing an appropriate basis for a given problem. It also means that the same transformation can be described using different coordinate systems, which can be useful in certain applications.

5. Are there any limitations to using different matrices for the same linear operator?

The use of different matrices for the same linear operator is limited by the fact that they must have the same eigenvalues and eigenvectors. This means that the matrices must have the same dimension and cannot be arbitrarily chosen. Additionally, using different matrices can sometimes result in more complex calculations and may not always be necessary for a given problem.

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