Properties of a special block matrix

In summary, the matrix R is symmetric and has eigenvalues that are related to the eigenvalues of the matrix R above.
  • #1
ekkilop
29
0
Hi folks!

I've encountered the matrix below and I'm curious about its properties;

[tex] R=
\begin{pmatrix}
0 & N-S\\
N+S & 0
\end{pmatrix}
[/tex]

where R, N and S are real matrices, R is 2n by 2n, N is n by n symmetric and S is n by n skew-symmetric.

Clearly R is symmetric so the eigenvalues are real, but what else can be said about a matrix of this type? I checked through some literature but didn't really know what to look for. Surely the form is simple enough that it should have been studied.

In a special case, the elements of the rows of the matrix N+S sum to zero. Could this affect the properties somehow?

Any ideas would be much appreciated!
 
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  • #2
Any matrix can be written as a symmetric plus a skew symmetric, so all you really have is a matrix of the form

[tex] R=
\begin{pmatrix}
0 & A\\
A^t & 0
\end{pmatrix}
[/tex]

The N and S aren't adding anything.

What are you doing with this matrix? Is there a specific type of problem you are trying to solve for example?
 
  • #3
That's a fair point.
I was playing around with a different matrix - Hermitian and also symmetric about the anti-diagonal. Turns out that the eigenvectors are closely related to the eigenvectors of the matrix R above so I was curious about their structure. It seems reasonable that the upper and lower half of the eigenvectors should be closely related but the form largely depends on A I suppose.
But the form of R seems particularly neat so I thought perhaps it had some other interesting properties. Perhaps one could say what the determinant should be? Is it generally true that
[itex] det(R)=det(-A^{T}A) [/itex]
 
  • #4
ekkilop said:
Is it generally true that
[tex] det(R)=det(A^{T}A) [\tex]
That should be true for all block matrices (maybe with a different sign).
As determinants are multiplicative, this can be simplified to det(R)=det(A)^2.
 
  • #5
Thank you!
I think I shall have to return to the drawing board for a closer investigation :)
 

1. What is a special block matrix?

A special block matrix is a matrix that is composed of smaller submatrices, also known as blocks. These blocks can be arranged in a specific pattern or structure to form the special block matrix, and each block can contain any type of elements, such as numbers or variables.

2. What are the properties of a special block matrix?

The properties of a special block matrix include the ability to perform operations on individual blocks as well as the entire matrix, such as addition, multiplication, and inversion. It also allows for easier manipulation and analysis of large matrices, as well as efficient storage of data.

3. How is a special block matrix different from a regular matrix?

A regular matrix is composed of a single block of elements, while a special block matrix is composed of multiple blocks arranged in a specific structure. This allows for more flexibility in terms of operations and analysis, as well as improved efficiency in storage and computation.

4. What are some applications of special block matrices?

Special block matrices are commonly used in various fields of mathematics and science, such as linear algebra, statistics, and data analysis. They are also used in computer graphics and engineering, particularly in the design and analysis of complex systems.

5. How can I determine the structure of a special block matrix?

The structure of a special block matrix can be determined by looking at the arrangement of the blocks within the matrix. The number of rows and columns of each block, as well as the size and type of elements within each block, can also provide information about the structure of the matrix.

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