Eigenvalue separation of a Block Matrix with a special structure

In summary: Your Name]In summary, the conversation discusses constructing a new $n$ by $n$ matrix with only the positive eigenvalues of a given square matrix $J$. This can be achieved by using the Schur decomposition of $J$ and taking the top left $n$ by $n$ submatrix of the resulting upper triangular matrix. This new matrix will have the desired property of having all positive eigenvalues.
  • #1
Sam1984
1
0
Hi everyone,

I have a square matrix [tex]J \in \mathbb{C}^{2n \times 2n}[/tex] where,

$J=\left(\begin{array}{cc}A&B\\\bar{B}&\bar{A}\end{array}\right)$

[tex]A \in \mathbb{C}^{n \times n}[/tex] and its conjugate [tex]\bar{A}[/tex] are diagonal.Assume the submatrices [tex]A,B \in \mathbb{C}^{n \times n}[/tex] are constructed in a way that all $2n$ eigenvalues are either real with exactly n eigenvalues positive and the other n eigenvalues negative or if some eigenvalues are complex the real part of these $2n$ eigenvalues are half positive half negative. Notice that this property does not hold in general for every $J$ with the above structure. But suppose it holds for a set of [tex]A,B \in \mathbb{C}^{n \times n}[/tex], then how can we form a new $n$ by $n$ matrix based on the submatrices [tex]A,B \in \mathbb{C}^{n \times n}[/tex] which has only the positive eigenvalues of $J$ as its set of eigenvalues.

Any help would be greatly appreciated.
 
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  • #2


Hi there,

Thank you for sharing your interesting question. It seems like you are looking for a way to construct a new $n$ by $n$ matrix with only the positive eigenvalues of $J$ as its set of eigenvalues. This can be achieved by using the Schur decomposition of the matrix $J$, which allows us to transform it into an upper triangular matrix with its eigenvalues on the diagonal.

In this case, we can use the Schur decomposition to obtain a new matrix $T$ such that $J=UTU^*$, where $U$ is a unitary matrix and $T$ is an upper triangular matrix. The eigenvalues of $J$ will be the same as the diagonal elements of $T$. Since $T$ is upper triangular, the positive eigenvalues of $J$ will be located on the diagonal of $T$.

To construct a new $n$ by $n$ matrix with only the positive eigenvalues of $J$, we can simply take the top left $n$ by $n$ submatrix of $T$, which will contain only the positive eigenvalues. This new matrix will have the desired property that all its eigenvalues are positive.

I hope this helps. Let me know if you have any further questions. Good luck with your research!

 

FAQ: Eigenvalue separation of a Block Matrix with a special structure

What is eigenvalue separation of a block matrix with a special structure?

Eigenvalue separation is a technique used in linear algebra to analyze the eigenvalues of a block matrix that has a specific structure. This structure involves the matrix being divided into smaller blocks, and the eigenvalues of the entire matrix can be determined by looking at the eigenvalues of the individual blocks.

Why is eigenvalue separation useful?

Eigenvalue separation can simplify the process of finding the eigenvalues of a large block matrix by breaking it down into smaller, more manageable blocks. This can save time and computational resources, especially for matrices with a large number of dimensions.

What is the special structure required for eigenvalue separation?

The special structure for eigenvalue separation involves the block matrix being divided into smaller blocks that are either diagonal or upper triangular. This structure allows for the eigenvalues of the entire matrix to be determined by looking at the eigenvalues of the individual blocks.

How does eigenvalue separation work?

Eigenvalue separation works by using the properties of block matrices to simplify the process of finding eigenvalues. By dividing the matrix into smaller blocks, the eigenvalues of the entire matrix can be determined by looking at the eigenvalues of the individual blocks. This process can be repeated for each block until the eigenvalues of the entire matrix are obtained.

Can eigenvalue separation be applied to any block matrix?

No, eigenvalue separation can only be applied to block matrices with a specific structure. This structure involves the matrix being divided into smaller blocks that are either diagonal or upper triangular. If a block matrix does not have this structure, then eigenvalue separation cannot be used to find its eigenvalues.

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