Graduate Finding Eigenvectors for Two Matrices using the Generalized Jacobi Method

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To find eigenvectors for two matrices A and B using the generalized Jacobi method, one must consider the simultaneous diagonalization of the matrices. While eigenvectors are specific to each matrix, simultaneous diagonalization is relevant when the matrices commute, as shown in quantum mechanics with operators X and Y. The discussion highlights that if KM does not equal MK, the matrices cannot be simultaneously diagonalized, complicating the eigenvector determination. An example is provided with matrices K and M, illustrating the conditions under which diagonalization is possible. Ultimately, the conversation emphasizes the importance of matrix commutation in the diagonalization process.
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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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Eigenvectors belong to a matrix. Matrix A has its eigenvectors. Matrix B has its eigenvectors.
 
Hi,
I meant simultaneous diagonalization of two matrices.
 
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.
XY=YX
X has eigenvectors of {|x>}. Y has eigenvectors of {|y>}.

XX^2=X^2X
X^2 has denenerated eigenbectors of |x> and |-x> for eigenvalue x^2
 
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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
 
As KM \neq MK, I am afraid that we cannot simultaneously diagonalize them. I might be wrong due to scarce knowledge on Jacobi method.
 
K=1,-1;-1,1
M=2,1;1,2
 
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 ?
 

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