Powers of a superdiagonal matrix

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In summary, you would raise each matrix element on the diagonal to the order of the power you are computing.
  • #1
protaktyn
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Hello,

while dealing with non-homogeneous equations with constant coefficients I met a following problem. I need an easy way to calculate powers of a superdiagonal matrix (every power up to n-1):

[tex]\mathbb N^{n}_{n} \ni \mathbb M_{n}:=\begin{bmatrix} 0&n-1&0&0&...&0&0&0&0\\0&0&n-2&0&...&0&0&0&0\\0&0&0&n-3&...&0&0&0&0\\...&...&...&...&...&...&...&...&...\\0&0&0&0&...&0&3&0&0\\0&0&0&0&...&0&0&2&0\\0&0&0&0&...&0&0&0&1\\0&0&0&0&...&0&0&0&0 \end{bmatrix}[/tex]

(zeros outside the superdiagonal, an arithmetic progression on the superdiagonal).

Thanks in advance.
 
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  • #2
I think this is quite simple. You would simply raise each matrix element on the diagonal to the order of the power you are computing.

If you are computing M^2 then the element in a_{11} position would be (n-1)^2, a_{12} = (n-2)^2 and so on down the diagonal.

The then the (n-1)th power would return (in the a_{11} position) (n-1)^(n-1) and so on down the diagonal.

I think this is the solution unless I have totally misunderstood your matrix structure definition.
 
  • #3
The diagonal elements are zeros here, so it won't work. It's not a diagonal matrix.
 
  • #4
protaktyn said:
The diagonal elements are zeros here, so it won't work. It's not a diagonal matrix.


Whoops! Thought it was too easy. didn't read your laTex code correctly. Nor did I fully understand the terminology of "Super"diagonal.

I'll have another think. My intial thought here is that you would be dealing with matrices in Jordan Normal form. Are you familiar? If not they are upper triangular matrices of which there is a plethora of material on them. Your matrix above is a special type of Jordan Normal form where the upper triangular block is a itself a lower triangular matrix.

Some thought required here though.
 
Last edited:
  • #5
I think it might be easiest to express in basis format. Define the matrix:
[itex]e^i\otimes e_j = \Lambda^i_j[/itex]
To be the matrix which is is zero everywhere except a 1 in the i-th row and j-th column.
Then your matrix is:
[tex]\mathbb{M}_n = (n-1)\Lambda^1_2 +(n-2)\Lambda^2_3 + \cdots + 1\Lambda^{(n-1)}_n=\sum_{k=1}^{n-1}(n-k)\Lambda^k_{k+1}[/tex]

In component form multiplication becomes [itex]\Lambda^i_j \Lambda^k_n = \delta^k_j \Lambda^i_n[/itex] where [itex]\delta[/itex] is the Kronecker delta.

Expanding the first power gives:
[tex](\mathbb{M}_n)^2 = \sum_{k=1}^{n-2}(n-k)(n-k-1)\Lambda^k_{k+2}[/tex]
I think you can see the pattern emerge.
 

Related to Powers of a superdiagonal matrix

1. What is a superdiagonal matrix?

A superdiagonal matrix is a square matrix where all the elements below the main diagonal are zero. The main diagonal is the line of elements that run from the top left corner to the bottom right corner of the matrix.

2. What are the powers of a superdiagonal matrix?

The powers of a superdiagonal matrix refer to the result of multiplying the matrix by itself a certain number of times. For example, the second power of a superdiagonal matrix is the result of multiplying the matrix by itself once, and the third power is the result of multiplying the matrix by itself twice.

3. How are the powers of a superdiagonal matrix calculated?

The powers of a superdiagonal matrix can be calculated by using the matrix multiplication rule, which involves multiplying each element in a row of the first matrix by the corresponding element in a column of the second matrix, and then adding the products together.

4. What are the applications of superdiagonal matrices?

Superdiagonal matrices are commonly used in linear algebra and can be applied in various fields such as computer graphics, physics, and economics. They are also used in solving systems of linear equations and in diagonalization of matrices.

5. How are superdiagonal matrices different from other types of matrices?

Unlike other types of matrices, superdiagonal matrices have a specific structure where all the elements below the main diagonal are zero. This makes them useful in certain mathematical operations and applications, such as diagonalization and solving systems of linear equations. Other types of matrices may have non-zero elements below the main diagonal, which can affect their properties and uses.

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