Eigenstates of "summed" matrix

In summary, the conversation discusses the problem of calculating eigenvalues and eigenstates for a Hermitian matrix with changed diagonal elements. One proposed method suggests splitting the problem into two sub-problems involving the unchanged and changed diagonal elements separately. However, another speaker suggests that for simple changes, such as adding the same amount to all diagonal elements, there is a straightforward solution. For more complex changes, it is necessary to re-solve the eigenvalue problem in each case.
  • #1
ilvreth
33
0
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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  • #2
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.
 

Related to Eigenstates of "summed" matrix

What are eigenstates?

Eigenstates are the states of a system that are associated with a particular value of a physical property, known as an eigenvalue. In quantum mechanics, eigenstates refer to the states of a quantum system that correspond to the possible values of an observable quantity, such as energy or momentum.

What is a "summed" matrix?

A "summed" matrix refers to the mathematical operation of adding two or more matrices together. This can be done by simply adding the corresponding elements of the matrices, as long as they have the same dimensions.

How are eigenstates related to a "summed" matrix?

The eigenstates of a "summed" matrix are the states that satisfy the eigenvalue equation for the combined matrix. This means that the eigenstates of the individual matrices will also be eigenstates of the combined matrix.

Can a "summed" matrix have an infinite number of eigenstates?

Yes, it is possible for a "summed" matrix to have an infinite number of eigenstates. This is often the case in quantum mechanics, where the state space is infinite-dimensional and there are infinitely many possible eigenstates for a given observable.

How are eigenstates of a "summed" matrix used in physics?

Eigenstates of a "summed" matrix are used in physics to describe the possible states of a system and to calculate the probabilities of measuring certain values for observable quantities. They are also used in the analysis of quantum systems, where they can provide information about the energy levels and other properties of a system.

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