Constructing Eigenvectors from Commuting Matrices: A Unique Classification

In summary, the conversation is discussing the relationship between the eigenvectors of two matrices A and B that commute. The speaker is trying to show that these eigenvectors provide a unique classification of the eigenvectors of another matrix H, but is unsure how to proceed. The conversation also touches on the possibility of obtaining eigenvectors from pairs of eigenvalues and the orthogonality of eigenvectors.
  • #1
greisen
76
0
Hey all,

I have two matrices A,B which commute than I have to show that these eigenvectors provide a unique classification of the eigenvectors of H?

From these pairs of eigenvalue is it possible to obtain the eigenvectors?


I don't quite know how to procede any suggestions?

Thanks in advance
 
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  • #2
What is H?
 
  • #3
greisen said:
Hey all,

I have two matrices A,B which commute than I have to show that these eigenvectors provide a unique classification of the eigenvectors of H?
This makes no sense. Obviously, the eigenvectors of A and B will tell you nothing about the eigenvectors of some arbitrary third matrix H. What relationships are there between A, B, and H?

From these pairs of eigenvalue is it possible to obtain the eigenvectors?


I don't quite know how to procede any suggestions?

Thanks in advance
Yes. Look closely at how A and B are related to H.
 
  • #4
Sorry not H but A the same matrix
 
  • #5
So you're asking if the eigenvectors of B determine the eigenvectors of A, given that A and B commute? This doesn't sound right, since the identity matrix commutes with everything. You can narrow down the possible eigenvectors of A, but you won't get a "unique classification."
 
  • #6
to see if I understand correctly - let's assume that the matrix A har the eigenvalues {1,2,2} and the matrix B has the eigenvalues {-1,1,1} - then it is possible to construct the eigenvectors of B according to the common unique pairs of A and B( (1,1),(2,1),(2,-1)) giving the following eigenvectors: (1,0,0) , (0,1,1) , (0,-1,1) ?

And had it not been possible with unique pairs of eigenvalues would the eigenvectors not be orthogonanle?

Thanks in advance
 

Related to Constructing Eigenvectors from Commuting Matrices: A Unique Classification

1. What are eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are concepts in linear algebra that are used to understand the behavior of linear transformations. An eigenvector is a vector that, when multiplied by a given matrix, will be scaled (stretched or shrunk) by a factor known as the eigenvalue.

2. How are eigenvectors and eigenvalues used in data analysis?

Eigenvectors and eigenvalues are commonly used in data analysis to reduce the dimensionality of high-dimensional datasets. This is accomplished by finding the eigenvectors and eigenvalues of the covariance matrix of the data, and then selecting the eigenvectors with the highest corresponding eigenvalues as the new, lower-dimensional features.

3. What is the significance of the eigenvalue in an eigenvector?

The eigenvalue of an eigenvector represents how much the eigenvector is scaled by when multiplied by a given matrix. In other words, it is a measure of the amount of variance in the data that is captured by that particular eigenvector.

4. Can there be multiple eigenvectors with the same eigenvalue?

Yes, it is possible for multiple eigenvectors to have the same eigenvalue. These are known as degenerate eigenvectors, and they often occur when there is a high degree of symmetry in the data being analyzed.

5. What is the relationship between eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are closely related, as the eigenvalue determines the scaling factor for the eigenvector. Additionally, the set of all eigenvectors corresponding to a given eigenvalue forms a subspace of the vector space being considered.

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