Hermitian and Unitary matrices

In summary, the conversation was about finding a unitary matrix U given the matrix H and using the eigenvectors to do so. The correct approach is to find the normalized eigenvectors of H, and use them as columns in U. However, if some eigenvectors have the same eigenvalue, they need to be made orthogonal using the Gram-Schmidt process.
  • #1
snakebite
16
0

Homework Statement



Hello,

the problem is asking me to find a unitary matrix U such that (U bar)^T(H)(U) is diagonal. And we have H = [{7,2,0},{2,4,-2},{0,-2,5}]


The Attempt at a Solution


I don't know where to start. I tried getting the eigenvalues of the matrix A but that lead to very long and complicated equations with 3rd roots which i doubt is the correct way.
Other than that I have no idea what to start off with

Thanks you
 
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  • #2
What is the matrix A? Do you mean H? If so, are you sure you have the right H? That one leads to messy results, so maybe you are doing the problem correctly.
 
  • #3
Yea I'm sorry there is no A i meant H. And yes I'm sure i have to correct matrix H on. So the correct way would be to just find the eigenvectors and U would just be a matrix with the eigenvectors as the columns?
 
  • #4
Yes, with the eigenvector normalized.
 
  • #5
I did what you said on another example where H = [{-2,1+i,1-i},{1-i,-1,-2i},{1+i,2i,-1}]
I got the eigenvalues which are 2, -3, -3 (I'm sure about this) and the normalized eigenvectors are
e1=[{(1-i)/[tex]\sqrt{10}[/tex],-i[tex]\sqrt{2/5}[/tex],[tex]\sqrt{2/5}[/tex]}]
e2 = [{(-1-i)/[tex]\sqrt{3}[/tex],1/[tex]\sqrt{3}[/tex], 0}]
e3 = [{(-1+i)/[tex]\sqrt{3}[/tex],0,1/[tex]\sqrt{3}[/tex],}]

Once again I am positive about these eigenvalues,
So let U = [e1,e2,e3] (column of U are the normalized eigenvectors)
When I do the operation
(U bar)^T.U I get [{1,0,0},{0,1,-2i/3},{0,2i/3,1}] which is not quite the identity matrix (which i should be getting)

Is there something i should do to the eigenvectors to get U to be unitary?

Thanks again
 
  • #6
Since e2 and e3 have the same eigenvalue, any linear combination of e2 and e3 is also an eigenvector of H, and the usual process for finding eigenvectors won't guarantee orthogonality. You need to make them orthogonal by hand, using the Gram-Schmidt process. (In contrast, if the eigenvalues were all distinct, you get orthogonality automatically.)
 

Related to Hermitian and Unitary matrices

1. What is a Hermitian matrix?

A Hermitian matrix is a square matrix that is equal to its own conjugate transpose. This means that the matrix is symmetrical along its main diagonal, and its off-diagonal elements are complex conjugates of each other.

2. What is a Unitary matrix?

A Unitary matrix is a square matrix whose conjugate transpose is equal to its inverse. This means that the matrix preserves the length of vectors and the angles between them, making it useful for performing operations like rotations and reflections.

3. How are Hermitian and Unitary matrices related?

A Unitary matrix is a special case of a Hermitian matrix, as it is Hermitian and also has the additional property of being unitary. This means that all Unitary matrices are also Hermitian, but not all Hermitian matrices are Unitary.

4. What is the significance of Hermitian and Unitary matrices in quantum mechanics?

In quantum mechanics, Hermitian and Unitary matrices are used to represent observables and transformations, respectively. This is because they have special properties that make them useful for describing physical systems and predicting their behavior.

5. How are Hermitian and Unitary matrices used in other fields of science?

Hermitian and Unitary matrices have applications in various fields of science, such as physics, engineering, and computer science. They are used for solving equations, performing calculations, and analyzing data, among other things.

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