Unitary Matrices and Positive Definite Matrices: Exploring the Relationship

In summary, the question asks for which unitary matrices U the matrix A-UBU^{\dagger} is positive definite, given that matrices A and B are positive definite and their difference A-B is also positive definite. The solution involves finding a condition for the trace of A-UBU^{\dagger} to be positive, which can be done by considering the effect of a unitary transform on the eigenvalues of a matrix.
  • #1
DavidK
31
0
An Hermitian matrix [tex] H [/tex] is positive definite if all its eigenvalues are nonzero and positive. Assume that the matrices [tex] A,B [/tex] are positve definite, and that the difference [tex] A-B [/tex] is positve definite. Now, for which unitary matrices, [tex] U [/tex], is it true that the matrix [tex] A-UBU^{\dagger} [/tex] is positve definite.

I haven't been able to solve this problems, and I'm not sure if it is because it is to difficult (i.e. the only way to solve it is to check for all [tex] U [/tex]) or because I'm to incompetent. Any suggestions would be appreciated.

/David
 
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  • #2
I haven't bothered to try this out fully so I may be going up a blind alley, but how about the definition of positive definite involving the inner product, i.e. [tex](Ax,x) > 0[/tex] for all [tex]x[/tex] in the vector space [tex]V[/tex]? Then, [tex]((A-B)x,x) > 0[/tex] if [tex]A - B[/tex] is to be positive definite.
 
  • #3
I deleted, and then resubmitted this post:

Here is a start:

A = [tex]\left[ \begin {array}{cc} 2 &0 \\ 0 &3 \end {array} \right][/tex]
B = [tex]\left[ \begin {array}{cc} 1 &0 \\ 0 &2 \end {array} \right][/tex]
U = [tex]\left[ \begin {array}{cc} 0 &1 \\ 1 &0 \end {array} \right][/tex]

A is positive definite, B is positive definite, A - B is positive definite, but [tex]A - UBU^{-1}[/tex] is not positive definite.

This points to what can go wrong in the general case.
 
  • #4
I think I have solved the problem for the [tex]2\times2 [/tex] case. A positive matrix [tex]A [/tex] can in this case be expressed as:

[tex]

A=\frac{\mbox{Tr}(A)}{2}(I+r_x \sigma_x+r_y\sigma_y + r_z \sigma_z),

[/tex]

where [tex]\sigma_x, \sigma_y, \sigma_z[/tex] are the standard Pauli matrices, and [tex]\bar{r}_a=(r_x,r_y,r_z) [/tex] is a 3-vector of length less than one. This means that the difference between the matrices [tex]A [/tex] and [tex]UBU^{\dagger} [/tex] is positive iff

[tex]

\frac{\mbox{Tr}(A)-\mbox{Tr}(B)}{2} \geq \frac{|\mbox{Tr}(A)\bar{r}_a-
\mbox{Tr}(B)\bar{r}_b|}{2},

[/tex]

where the angle between the vectors [tex]\bar{r}_a,\bar{r}_b [/tex] is given by the unitary [tex]U[/tex]. I'm, however, not sure if it is possible to solve the general problem using this approach.

/David
 
Last edited:
  • #5
Davids got the right idea. For any unitary matrix, [itex] U^{\dagger} = U^-1 [/itex]. For any matrix A, [itex] Tr(A)= Tr(DAD^-1) [/itex].

If A - B is positive definite, then Tr(A - B) > 0 .

[tex] Tr(A - UBU^{\dagger}) = Tr(A - B) > 0 [/tex].

Tr( A - B) > 0 is necessary, but it isn't sufficient. I'd start looking at how a unitary transform affects the eigenvalues of a a matrix.
 

1. What is a positive definite matrix?

A positive definite matrix is a square matrix where all of its eigenvalues are positive. This means that the matrix has certain properties, such as all of its principal minors being positive, and that it is nonsingular (its determinant is non-zero).

2. What are some applications of positive definite matrices?

Positive definite matrices have various applications in fields such as linear algebra, optimization, statistics, and engineering. They are commonly used to solve systems of equations, find the minimum or maximum of a function, and in machine learning algorithms.

3. How do you determine if a matrix is positive definite?

To determine if a matrix is positive definite, you can use the Cholesky decomposition or the Sylvester's criterion. The Cholesky decomposition involves factoring the matrix into a lower triangular matrix and its transpose. If this factorization is possible, and all the diagonal elements of the lower triangular matrix are positive, then the original matrix is positive definite. Sylvester's criterion states that a symmetric matrix is positive definite if and only if all of its principal minors are positive.

4. Can a non-square matrix be positive definite?

No, a non-square matrix cannot be positive definite. Positive definiteness applies to square matrices only, as it is related to the eigenvalues of the matrix, which can only be calculated for square matrices.

5. How are positive definite matrices related to positive semi-definite matrices?

A positive definite matrix is a special case of a positive semi-definite matrix, where all of its eigenvalues are strictly positive. A positive semi-definite matrix has all of its eigenvalues being non-negative, but it can have zero eigenvalues. This means that a positive definite matrix is always positive semi-definite, but the opposite is not always true.

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