Any implications of this diagonal element inequality?

In summary, the given relationship between two positive definite matrices A and B implies that the sum of eigenvalues of A is greater than or equal to the sum of eigenvalues of B. This holds even after any unitary change of coordinates. Furthermore, it can be deduced that the eigenvalues of A dominate the eigenvalues of B in a specific manner, where the sum of the first k eigenvalues of A is greater than or equal to the sum of the first k eigenvalues of B for any k. This result follows from the fact that the sum of eigenvalues of the sum of two Hermitian matrices is greater than or equal to the sum of the individual eigenvalues.
  • #1
winterfors
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Given two definite positive definite matrices A and B of identical size with the following relationship of their diagonal elements:

[tex] A_{ii} \geq B_{ii}[/tex] (no summation)

which also holds after any unitary change of coordinates

[tex] \textbf{A}'=\textbf{U}^T\textbf{A}\textbf{U}[/tex]

where [tex]\textbf{U}[/tex] complete set of orthonormal vectors.


Question: What does this imply in terms of inequalities on the eigenvalues of the matrices?

The sum of eigenvalues of A is of course equal of greater than the sum of eigenvalues B:s eigenvalues, but this is true even without allowing for change of coordinates. I'm sure you must be able to deduce something stronger when the inequality holds under any unitary coordinate change...
 
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  • #2
My linear algebra is very rusty so be careful of the argument below.

A-B is p.s.d .

I am assuming the matrices are real.

Now from the given condition we have for any orthogonal matrix U, the diagonal elements of [itex] U^T (A-B) U [/itex] are non negative.

In particular choose [itex] U [/itex] to be the orthogonal matrix corresponding to the spectral decomposition of [itex]A-B[/itex] and we get all its eigenvalues as non negative.
 
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  • #3
I think this also implies that the eigenvalues of A dominate the eigenvalues of B in the following way: if [itex] \lambda_i(A)[/itex] is the [itex]i^{th}[/itex] largest eigenvalue of A and [itex] \lambda_i(B)[/itex] is similarly defined. Then [itex] \sum_{i=1}^{k} \lambda_i(A) \geq \sum_{i=1}^{k} \lambda_i(B)[/itex] for [itex]k=1,\dots,n[/itex].

This follows from the fact that for two hermitian matrices A and B of the same order we have (4.3.27 Horn and Johnson)
[itex]\sum_{i=1}^{k} \lambda_i(A+B) \geq \sum_{i=1}^{k} \lambda_i(A) + \sum_{i=1}^{k} \lambda_i(B)[/itex], in particular substituting the pair [itex]A-B,B[/itex] and noting [itex]\lambda_i(A-B) \geq 0[/itex] we get the result.
 
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  • #4
This was exactly what I was looking for, many thanks!
 

FAQ: Any implications of this diagonal element inequality?

What does the diagonal element inequality imply?

The diagonal element inequality implies that the elements on the main diagonal of a matrix are greater than or equal to the elements in the rest of the matrix. This can be represented mathematically as A[i][i] ≥ A[i][j], where i is the row index and j is the column index.

Why is the diagonal element inequality important?

The diagonal element inequality is important because it can provide valuable information about the properties of a matrix. For example, it can help determine if a matrix is positive definite or if it has a unique solution in a system of linear equations.

How can the diagonal element inequality be used in matrix operations?

The diagonal element inequality can be used in various matrix operations, such as matrix multiplication and finding eigenvalues and eigenvectors. It can also be used to determine the rank and determinant of a matrix.

Are there any exceptions to the diagonal element inequality?

Yes, there are exceptions to the diagonal element inequality. For example, in a diagonal matrix, all elements on the main diagonal are equal, so the inequality does not hold. Additionally, in certain cases, the inequality may hold for some elements but not for others.

How does the diagonal element inequality relate to the properties of a matrix?

The diagonal element inequality is closely related to the properties of a matrix. For example, if a matrix satisfies the diagonal element inequality, it is symmetric and positive semi-definite. On the other hand, if the inequality does not hold, the matrix may be asymmetric or have complex eigenvalues.

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