Inversion of this Vandermonde matrix

In summary: I'm still trying to understand the connection between the discrete Fourier transform and the equation Wx=b.In summary, you were trying to expand a three and more parameter functions similarly to the two-parameter case f(x,y)=(f(x,y)+f(y,x))/2+(f(x,y)-f(y,x))/2. You need to solve \begin{pmatrix}1 & 1 & 1 & \dotsb & 1\\1 & \omega & \omega^2 & \dotsb & \omega^{n-1}\\1 & \omega^2 & \omega^4 & \
  • #1
Gerenuk
1,034
5
I was trying to expand a three and more parameter functions similarly to the two-parameter case f(x,y)=(f(x,y)+f(y,x))/2+(f(x,y)-f(y,x))/2.

Anyway, to do the same for more parameters I need to solve
[tex]
\begin{pmatrix}
1 & 1 & 1 & \dotsb & 1\\
1 & \omega & \omega^2 & \dotsb & \omega^{n-1}\\
1 & \omega^2 & \omega^4 & \dotsb & \omega^{2(n-1)}\\
1 & \omega^3 & \omega^6 & \dotsb & \omega^{3(n-1)}\\
\vdots & &&& \vdots \\
1 & \omega^{n-1} & \omega^{2(n-1)} & \dotsb & \omega^{(n-1)(n-1)}
\end{pmatrix}\mathbf{x}=
\begin{pmatrix}
1 \\ 0 \\ 0 \\ 0 \\ \vdots \\ 0
\end{pmatrix}
[/tex]
with [itex]\omega=\exp(2\pi\mathrm{i}/n)[/itex]
Is there a closed form expression for x?

EDIT: Oh, silly me. I realized it's a discrete Fourier transform. So is this the correct way to expand then? In the 3 parameter case the solution would be
[tex]
f(x,y,z)=\frac13(f(x,y,z)+f(y,z,x)+f(z,x,y))+\frac13\left(f(x,y,z)+\omega f(y,z,x)+\omega^*f(z,x,y)\right)+\frac13\left(f(x,y,z)+\omega^*f(y,z,x)+\omega f(z,x,y)\right)[/tex]
with [itex]\omega=\exp(2\pi\mathrm{i}/3)[/itex]?

Now I'm just wondering why I get linear dependent terms when I consider the real part only?
 
Last edited:
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  • #2
You're on the right track. It is convenient to write this equation in matrix notation as

[tex]Wx=b[/tex]

where b is the vector you wrote on the RHS. Since W is full-rank and non-singular, it is invertible and the solution to your problem is

[tex]x=W^{-1}b[/tex] .

You need the inverse of W, but this is easy since we know from the properties of the discrete Fourier transform (DFT) that the individual vector columns of W are independent. Each is an orthonormal basis vector of the DFT. In words, this follows because the component of signal at one frequency (cosine or sine at w_m) is independent to that at every other frequency w_n. In fact,

[tex]W^{-1} = W^{\dagger} [/tex]

that is, W is Hermitian (the dagger is the conjugate transpose operation) and unitary (you get the identity matrix in the next equation)

[tex]WW^{\dagger}=I [/tex] .

You can perform this multiplication explicitly it to see that this is so. Accordingly

[tex]x=W^{\dagger}b[/tex]

and you can write out the components of x explicitly.
 
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  • #3
I just wonder if that way to decompose a multiparameter function makes sense.

It's nicely symmetrical and generalizes to higher dimensions. However it introduces complex number where the initial function might actually be real only. And also I haven't included odd parity permutations of the function arguments...
 
  • #4
Sorry, I'm not following your comments. I provided the closed solution to Wx=b, where W is complex, which was the question asked in your first post. Are you looking for something else?
 
  • #5
I did that exercise to find a way to extend the rule f(x,y)=(f(x,y)+f(y,x))/2+(f(x,y)-f(y,x))/2 to higher dimensions (I didn't explain the connection; just mentioned it in the intro). I assumed some cyclic symmetry for the final form of f(x,y,z)=... and with the help of the discrete Fourier transform (which I didnt recognise at first), I can find some "decomposition".

Now I wasn't sure if the decomposition is useful this way.
 

What is a Vandermonde matrix?

A Vandermonde matrix is a special type of matrix with the elements of each row increasing geometrically, while the elements of each column increase arithmetically.

What is inversion of a Vandermonde matrix?

Inversion of a Vandermonde matrix refers to the process of finding the inverse of the matrix, which is a matrix that when multiplied by the original matrix gives the identity matrix.

Why is inverting a Vandermonde matrix important?

Inverting a Vandermonde matrix is important in various applications such as signal processing, interpolation, and curve fitting. It allows for efficient computation and simplifies various mathematical operations.

What are the methods for inverting a Vandermonde matrix?

There are several methods for inverting a Vandermonde matrix, including Gaussian elimination, LU decomposition, and Cholesky decomposition. Each method has its own advantages and disadvantages, and the choice of method depends on the specific application.

What are the challenges of inverting a Vandermonde matrix?

One of the main challenges of inverting a Vandermonde matrix is the possibility of encountering ill-conditioned matrices, which may result in inaccurate or unstable solutions. Another challenge is the high computational complexity, especially for larger matrices.

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