Eigenvalue and and eigenvector

In summary, the conversation discusses the calculation of eigenvalues for a matrix represented as ##M^{ab} = A\delta^{ab} + B \phi^a \phi^b##, with ##\delta^{ab}## being the identity matrix and ##\phi## being a column vector. The paper mentions that the eigenvalues are A with multiplicity 1 and ##A + \phi^2 B## with multiplicity N-1. After discussing and considering the addition of ##A\delta^{ab}##, the issue is resolved.
  • #1
Gianfelici
4
0
Hi, I have a problem with the calculation of the eigenvalue of a matrix. That matrix is an N x N matrix which can be written as:

##M^{ab} = A\delta^{ab} + B \phi^a \phi^b##

where ##\delta^{ab}## is the identity matrix and the ##\phi## is a column vector. The paper I'm studying says that the eigenvalue of this matrix are:

A with molteplicity 1

##A + \phi^2 B## with molteplicity N-1

but I can't understand why! Can anyone help me?
 
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  • #2
You should put ## around the latex code to render it :smile:
 
  • #3
adjacent said:
You should put ## around the latex code to render it :smile:


Thank you, now it'right
 
  • #4
Gianfelici said:
The paper I'm studying says that the eigenvalue of this matrix are:

A with molteplicity 1

##A + \phi^2 B## with molteplicity N-1

I think it should be
A with multiplicity N-1
##A + \phi^2 B## with multiplicity 1.

First find the eigenvalues of the rank 1 matrix ##B\phi^a\phi^b##.
Then think about what happens when you add ##A\delta^{ab}##, which is A times the identity matrix.
 
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  • #5
yes, you're right, it was A with multeplicity N-1 and the other with multeplicity 1. I tried to use your suggestion and I solved it! thank you very much!
 

1. What is an eigenvalue and eigenvector?

An eigenvalue is a scalar value that represents the factor by which an eigenvector is stretched or shrunk when it is multiplied by a matrix. An eigenvector is a non-zero vector that remains in the same direction when multiplied by a matrix.

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

Eigenvalues and eigenvectors are commonly used in data analysis to reduce the dimensionality of a dataset and to identify patterns and relationships between variables. They can also be used for feature extraction and image processing.

3. Can a matrix have more than one eigenvalue and eigenvector?

Yes, a matrix can have multiple eigenvalues and eigenvectors. However, each eigenvalue will have a corresponding eigenvector that is unique to that eigenvalue.

4. What is the relationship between eigenvalues and determinants?

The determinant of a matrix is equal to the product of its eigenvalues. Additionally, if a matrix has a determinant of 0, then it has at least one eigenvalue of 0.

5. How are eigenvalues and eigenvectors calculated?

Eigenvalues and eigenvectors can be calculated by solving the characteristic equation for a given matrix. This involves finding the roots of a polynomial equation using techniques such as Gaussian elimination or the QR algorithm.

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