How do I determine orthogonal eigenvectors for a repeated eigenvalue?

In summary, the conversation discusses an Hamiltonian operation involving a 3x3 matrix and an arbitrary eigenvector. The eigenvalues are determined to be 1 and 4, with the eigenvector corresponding to eigenvalue 1 being repeated. The conversation then addresses the issue of finding the eigenvectors, with the suggestion to choose them to be orthogonal. It is also mentioned that the operator being handled is Hermitian, which ensures orthogonality between eigenvectors of distinct eigenvalues.
  • #1
J.Asher
12
0

Homework Statement


There is an Hamiltonian operation which is given by
(2 1 1)
(1 2 1) = H ; 3-by-3 matrix
(1 1 2)
And let's have an arbtrary eigenvector
(a)
(b) = v ; (3x1) matrix
(c)

Then, from the characteristic equation, the eigenvalues are 1,4. Here eigenvalue 1 is
repeated one.

Homework Equations


Now, my question arises. I know that the eigenvectors that corresponds to eigenvalue 1 is two and both are orthgonal to each other. However, I can't find any of them because when
I substitute eigenvalue into 1, I get a kind of meaningless(?) equation,

(2 1 1) (a) (a)
(1 2 1) (b) = (b)
(1 1 2) (c) (c) .

And it gives 3-equivalent equation, a+b+c = 0
I couldn't determine any relation between a,b,c.


The Attempt at a Solution



So I quess that do I have to choose a,b,c satisfying the condition a+b+c=0
but still problem doesn't disappear beacuse there would be a lot of soulutions...

What did I wrong?
 
Last edited:
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  • #2
You didn't take it far enough.

a + b + c = 0 ==>
a = -b - c
b = b
c = ... c

The eigenvectors form a two-dimensional subspace of R3 that is spanned by two vectors. If you look closely at the system above, you can pick out the two vectors. These two vectors are linearly independent but they don't happen to be orthogonal.
 
  • #3
The operator we handle is Hermitian since Hamlitonian is real physical operator.
it is no doubt that the eigenvectors of the Hermitian is orthogonal.
Actually I cannot understand what you are saying. What do you mean by that?
...??
 
  • #4
J.Asher said:
The operator we handle is Hermitian since Hamlitonian is real physical operator.
it is no doubt that the eigenvectors of the Hermitian is orthogonal.
Actually I cannot understand what you are saying. What do you mean by that?
...??

If it's hermitian the eigenvectors belonging to distinct eigenvalues are orthogonal. Eigenvectors belonging to the same eigenvalue don't have to be. You have to select them to be orthogonal.
 

FAQ: How do I determine orthogonal eigenvectors for a repeated eigenvalue?

What is a repeated eigenvalue problem?

A repeated eigenvalue problem is a mathematical problem in which a matrix has at least one eigenvalue that has a multiplicity greater than one. This means that there is more than one corresponding eigenvector for that eigenvalue.

Why is a repeated eigenvalue problem important?

A repeated eigenvalue problem is important because it can help us understand the behavior of a system or matrix. It can also provide insight into the stability of a system, as repeated eigenvalues can indicate that the system may have multiple steady states.

How do you solve a repeated eigenvalue problem?

To solve a repeated eigenvalue problem, you can use various methods such as the Jordan canonical form, the generalized eigenvector method, or the Cayley-Hamilton theorem. These methods involve finding the eigenvectors and eigenvalues of the matrix and using them to construct a diagonal or block diagonal matrix.

What are the applications of repeated eigenvalue problems?

Repeated eigenvalue problems have various applications in fields such as physics, engineering, and economics. They can be used to analyze the behavior of mechanical systems, study the stability of electrical circuits, and model population growth in biology, among others.

What are the limitations of solving a repeated eigenvalue problem?

One limitation of solving a repeated eigenvalue problem is that it can be computationally intensive, especially for large matrices. Additionally, the methods used to solve these problems may not always provide exact solutions, and approximations may be necessary. Furthermore, repeated eigenvalue problems may not have unique solutions, making it challenging to interpret the results.

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