Diagonalization of complex symmetric matrices

In summary: Woah, U^T in your formula and not U^* ... so in general U^T is not the inverse of U . Why did you choose that?U^T is not the inverse of U because it is block-diagonalizable.
  • #1
petergreat
267
4
Is every complex symmetric (NOT unitary) matrix [tex]M[/tex] diagonalizable in the form [tex]U^T M U[/tex], where [tex]U[/tex] is a unitary matrix? Why?
 
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  • #2
Woah, [tex]U^T[/tex] in your formula and not [tex]U^*[/tex] ... so in general [tex]U^T[/tex] is not the inverse of [tex]U[/tex] . Why did you choose that?
 
  • #3
g_edgar said:
Woah, [tex]U^T[/tex] in your formula and not [tex]U^*[/tex] ... so in general [tex]U^T[/tex] is not the inverse of [tex]U[/tex] . Why did you choose that?

Because I want to diagonalize a quadratic form [tex] v^T M v [/tex] where v is a complex vector. No complex conjugation is involved, so the only useful form of diagonalization is [tex]U^T M U[/tex].

I met this problem in physics. A specific complex symmetric matrix is involved, and it is diagonalized by an ansatz for the unitary matrix [tex]U[/tex]. However, I want to know whether this can work in general.

P.S. I know that [tex]U^T[/tex] is not the inverse of [tex]U[/tex]. Otherwise it would be too standard and I wouldn't need to ask here.
(In case anyone wants to know where this comes from, the mass term for Majorana neutrinos is essentially such a quadratic form; on the other hand, the mass term for Dirac neutrinos involve complex conjugation and can be dealt with in the usual manner of diagonalization [tex]U^\dagger M U[/tex])
 
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  • #4
petergreat said:
Is every complex symmetric (NOT unitary) matrix [tex]M[/tex] diagonalizable in the form [tex]U^T M U[/tex], where [tex]U[/tex] is a unitary matrix? Why?

You probably meant to emphasize "symmetric (not hermitian)"?

My belief is that if [itex]M\in\mathbb{C}^{n\times n}[/itex] is symmetric so that [itex]M^T=M[/itex], then there exists a complex orthogonal matrix [itex]O\in\mathbb{C}^{n\times n}[/itex] so that [itex]O^T=O^{-1}[/itex], and so that [itex]O^TMO[/itex] is diagonal. (And I believe that the answer to your question is: No.)

Unfortunately I don't have a reference for this claim, and I also don't have energy to go through a proof right now, because this isn't my problem, so you shouldn't believe my belief :biggrin:

I would recommend finding out how to prove the well known diagonalizability results of real symmetric, and complex hermitian matrices, and see if you can modify the proofs.

This is a little bit paradoxical topic for me, because I never studied these proofs from any educational material, but at some point I felt like my understanding on linear algebra had grown to a point when I could prove these results on my own. According to my understanding the proofs can be carried out recursively by using the fact that any matrix will always have at least one eigenvalue, and then for the purpose of moving the smaller dimensional subspaces (dim)[itex]n\mapsto n-1[/itex] you use some invariance properties such as: Orthogonal transformation keeps symmetric matrix as symmetric, or unitary transformation keeps hermitian matrix as hermitian.
 
  • #5
jostpuur said:
You probably meant to emphasize "symmetric (not hermitian)"?
Yes, that was a typo.

jostpuur said:
My belief is that if [itex]M\in\mathbb{C}^{n\times n}[/itex] is symmetric so that [itex]M^T=M[/itex], then there exists a complex orthogonal matrix [itex]O\in\mathbb{C}^{n\times n}[/itex] so that [itex]O^T=O^{-1}[/itex], and so that [itex]O^TMO[/itex] is diagonal. (And I believe that the answer to your question is: No.)

Unfortunately I don't have a reference for this claim, and I also don't have energy to go through a proof right now, because this isn't my problem, so you shouldn't believe my belief :biggrin:

That was also what I thought. What you described is sufficient to diagonalize the quadratic form. But the authors wanted to diagonalize the quadratic form while stilling preserving orthonormality, so went for unitary matrices, unfortunately (to me) with success...
 
  • #6
Can you show explicitly your example matrices that you have been working on?
 
  • #7
jostpuur said:
Can you show explicitly your example matrices that you have been working on?

I've attached a snapshot of my write-up of what I read. Basically the matrix contains a first-order small parameter, and is diagonalized by a unitary matrix that is expanded around identity up to 2nd order. The unitary matrix block-diagonalizes the matrix, and it is assumed that each block can be separately further diagonalized by unitary matrices. (The block-diagonalization is performed first to prove that some eigenvalues are an order smaller than the others. But this is not important to our discussion). If the assumption that each block can be separately further diagonalized is dubious, I've at least showed that 2 by 2 complex symmetric matrices of a particular form can be diagonalized in this manner.
 
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1. What is the purpose of diagonalizing a complex symmetric matrix?

Diagonalizing a complex symmetric matrix means transforming it into a diagonal matrix, where all the non-diagonal elements are zero. This simplifies the matrix and makes it easier to perform calculations and solve equations involving the matrix.

2. How is a complex symmetric matrix diagonalized?

A complex symmetric matrix can be diagonalized by finding its eigenvalues and eigenvectors. The eigenvectors form a basis for the matrix and can be used to transform it into a diagonal matrix.

3. Can all complex symmetric matrices be diagonalized?

Yes, all complex symmetric matrices can be diagonalized. This is because they have a full set of eigenvectors, which are necessary for diagonalization.

4. What are the benefits of diagonalizing a complex symmetric matrix?

Diagonalizing a complex symmetric matrix can make it easier to solve equations involving the matrix, as well as make it easier to identify patterns and relationships within the matrix. It can also simplify the matrix and make it more computationally efficient to work with.

5. Are there any applications of diagonalizing complex symmetric matrices?

Yes, diagonalization of complex symmetric matrices is commonly used in various fields of science and engineering, such as physics, chemistry, and electrical engineering. It is also used in computer algorithms for tasks such as data compression and image processing.

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