Distribution of 2 matrices with the same eigenvalues

In summary, the conversation discusses the relationship between two matrices with the same eigenvalues and whether they share the same probability density function. The matrices in question are assumed to have i.i.d. complex Gaussian entries and differ in dimension, but have the same nonzero eigenvalues. The question is whether they also follow the same complex wishart distribution with the same parameters. The conversation concludes with a suggestion to implement the matrices in Matlab to compare their corresponding eigenvalues.
  • #1
nikozm
54
0
Hi,

I was wondering if two matrices with the same eigenvalues share the same PDF.

Any ideas and/or references would be helpful.
Thanks in advance
 
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  • #2
You'll have to provide some more information about what you're seeking.
Matrices in general don't have anything that is widely referred to as a 'PDF'.
The only PDF I know is 'probability density function' in probability theory. Is that what you mean? If so, how do you want to relate it to a matrix? Matrices are used in probability theory and statistics in numerous different ways.
 
  • #3
Hello,

Assume that H is a n \times m matrix with i.i.d. complex Gaussian entries each with zero mean and variance \sigma. Also, let n>=m. I ' m interested in finding the relation between the distribution of HHH and HHH, where H stands for the Hermittian transposition. I anticipate that both follow the complex wishart distribution with the same parameters (since they share the same nonzero eigenvalues), but I m not sure about this.

Any ideas ? Thanks in advance..
 
  • #4
The matrix ##H^HH## will be ##m\times m## while ##HH^H## will be ##n\times n##. They will have different numbers of eigenvalues. Why do you think the nonzero ones will be the same?
 
  • #5
Indeed, they have different dimensions. However their non-zero eigenvalues are the same. This is a fact. If you hold reservations about the latter just implement it in Matlab and see with the command eig their corresponding eigenvalues.

My question is: if they also have the same probability density function.
 

1. How can two matrices have the same eigenvalues?

Two matrices can have the same eigenvalues if they have the same characteristic polynomial. The characteristic polynomial is calculated by subtracting a scalar from the diagonal entries of the matrix and finding the determinant. If two matrices have the same characteristic polynomial, they will have the same eigenvalues.

2. Is it possible for two matrices with the same eigenvalues to have different eigenvectors?

Yes, it is possible for two matrices with the same eigenvalues to have different eigenvectors. The eigenvectors are not unique and can vary depending on the matrix. However, the eigenspaces, which are the subspaces spanned by the eigenvectors, will be the same for both matrices.

3. Can two matrices with the same eigenvalues have different ranks?

Yes, two matrices with the same eigenvalues can have different ranks. The rank of a matrix is determined by the number of linearly independent rows or columns. The eigenvalues do not affect the rank of a matrix.

4. Are there any special properties of matrices with the same eigenvalues?

Yes, matrices with the same eigenvalues have the same trace and determinant. This is because the trace and determinant are calculated using the eigenvalues of a matrix. Additionally, if two matrices have the same eigenvalues, they will also have the same characteristic polynomial and therefore the same minimal polynomial.

5. How does the distribution of eigenvalues affect the properties of a matrix?

The distribution of eigenvalues can provide insight into the properties of a matrix. For example, if a matrix has a large number of distinct eigenvalues, it may be diagonalizable. If a matrix has repeated eigenvalues, it may have a larger Jordan block structure. Additionally, the distribution of eigenvalues can affect the stability and convergence of numerical methods used to solve systems of linear equations involving the matrix.

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