Graduate Eigenstates of "summed" matrix

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SUMMARY

The discussion focuses on calculating the eigenvalues and eigenstates of a Hermitian matrix ##A##, which is expressed as the sum of two matrices, ##B## and ##C##. The matrix ##C## contains only the diagonal elements, allowing for a potential simplification in calculations. The proposed method involves solving the eigenvalue problem for matrix ##B## once, while varying the diagonal elements in matrix ##C## for subsequent calculations. However, it is concluded that for general changes to the diagonal elements, re-solving the eigenvalue problem for each case may be necessary, as no simpler solution is evident.

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ilvreth
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Hi to all.

Say that you have an eigenvalue problem of a Hermitian matrix ##A## and want (for many reasons) to calculate the eigenvalues and eigenstates for many cases where only the diagonal elements are changed in each case.

Say the common eigenvalue problem is ##Ax=λx##. The ##A## matrix is a sum of two matrices ##A=B+C## and the ##C## matrix contains only the diagonal elements non-zero. For example

\begin{equation}
\begin{pmatrix}
E_{1}& a & b \\
c & E_{2}& d \\
e & f & E_{3}
\end{pmatrix}
=
\begin{pmatrix}
0& a & b \\
c& 0 & d \\
e& f & 0
\end{pmatrix}
+
\begin{pmatrix}
E_{1}& 0 & 0 \\
0& E_{2} & 0 \\
0& 0 & E_{3}
\end{pmatrix}
\end{equation}Say you want to calculate the eigenstates of the ##A## matrix but in a different way.

I was thinking is there is a method which is used to split the problem in two other sub-problems. The first step to be something like to calculate something for the ##B## matrix which remains unchanged in each case so the calculation is performed only one time, in the second step you only perform calculations for the ##C## matrix which in any case onlyn the diagonal elements are changed so you have to work only with the ##C## matrix problem and finally you combine the data of these two matrices in order to obtain the final result for the ##A## matrix.

Whats your opinion?
 
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If the diagonal elements are only changed by adding the same amount to all three then there is a simple solution. First solve the eigenvalue problem for ##A##. Then the eigenvalues for the matrix that is ##A## with ##h## added to all diagonal elements are simply the eigenvalues of ##A## with ##h## added to them. The eigenvectors are unchanged by the addition of ##h## along the diagonal.

For more general changes to the diagonal elements, I see no reason to expect there to be a solution that is any easier than just re-solving the eigenvalue problem in each case.
 
I am studying the mathematical formalism behind non-commutative geometry approach to quantum gravity. I was reading about Hopf algebras and their Drinfeld twist with a specific example of the Moyal-Weyl twist defined as F=exp(-iλ/2θ^(μν)∂_μ⊗∂_ν) where λ is a constant parametar and θ antisymmetric constant tensor. {∂_μ} is the basis of the tangent vector space over the underlying spacetime Now, from my understanding the enveloping algebra which appears in the definition of the Hopf algebra...

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