Finding Eigenvectors for Two Matrices using the Generalized Jacobi Method

In summary, the conversation discusses the process of finding eigenvectors for two matrices, A and B, and the concept of simultaneous diagonalization. The example of x and y coordinate operators in Quantum Mechanics is used to explain the concept further. The conversation also mentions the use of the generalized Jacobi method to calculate the eigensystem problem for two matrices, but it is noted that it may not be applicable if the matrices do not commute. The conversation ends by suggesting to use a unitary matrix to diagonalize the matrices.
  • #1
hoshangmustafa
4
0
TL;DR Summary
If I have two matrices A and B, how can I find an eigenvector for the two matrices?
If I have two matrices A and B, how can I find an eigenvector for the two matrices?
 
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  • #2
Eigenvectors belong to a matrix. Matrix A has its eigenvectors. Matrix B has its eigenvectors.
 
  • #3
Hi,
I meant simultaneous diagonalization of two matrices.
 
  • #4
I do not think simultaneously diagonalizable matters in procedure of getting eigenvectors of A and B for each.

As examples in QM, x coordinate operator X and y coordinate operator Y are simultaneously diagonarizable.
[tex]XY=YX[/tex]
X has eigenvectors of {|x>}. Y has eigenvectors of {|y>}.

[tex]XX^2=X^2X[/tex]
X^2 has denenerated eigenbectors of |x> and |-x> for eigenvalue x^2
 
Last edited:
  • #5
Hi,
for example

K=2,1;1,2
M=2,0;0,0
use the generalized Jacobi method to calculate the eigensystem problem
KΦ=λMΦ
 
  • #6
As [tex]KM \neq MK[/tex], I am afraid that we cannot simultaneously diagonalize them. I might be wrong due to scarce knowledge on Jacobi method.
 
  • #7
K=1,-1;-1,1
M=2,1;1,2
 
  • #8
Then KM=MK. M=K+2I where I is identity matrix. Diagonalization of K by product of unitary matrix ##P, P^{-1}## would also diagonalize M. Why don't you try to get it ?
 

1. What is the Generalized Jacobi Method?

The Generalized Jacobi Method is an algorithm used to find the eigenvalues and eigenvectors of a square matrix. It is an iterative method that involves repeatedly computing matrix multiplications and rotations until the desired accuracy is achieved.

2. How does the Generalized Jacobi Method work?

The Generalized Jacobi Method works by transforming the given matrix into a diagonal matrix, where the diagonal elements are the eigenvalues. This is achieved by performing a series of rotations on the original matrix, which gradually reduces the off-diagonal elements to zero.

3. Why is the Generalized Jacobi Method useful?

The Generalized Jacobi Method is useful because it is a reliable and efficient method for finding eigenvalues and eigenvectors of a matrix. It is also relatively easy to implement and can be used for both symmetric and non-symmetric matrices.

4. What are the advantages of using the Generalized Jacobi Method over other methods?

One advantage of the Generalized Jacobi Method is that it does not require the matrix to be known in advance. This makes it useful for finding eigenvalues and eigenvectors of large, sparse matrices. Additionally, it is a stable method and can handle matrices with complex eigenvalues.

5. Are there any limitations to the Generalized Jacobi Method?

One limitation of the Generalized Jacobi Method is that it may converge slowly for matrices with multiple eigenvalues that are close together. In these cases, other methods such as the QR algorithm may be more efficient. Additionally, the Generalized Jacobi Method may not converge for matrices with non-real eigenvalues.

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