Eigenvalues of Circulant matrices

In summary, circulant matrices have eigenvalues that are combinations of roots of unity due to their relationship with permutation matrices, companion matrices, and their generation through discrete Fourier transforms.
  • #1
mr.tea
102
12
Hi,
I am studying about circulant matrices, and I have seen that one of the properties of such matrices is the eigenvalues which some combinations of roots of unity.

I am trying to understand why it is like that. In all the places I have searched they just show that it is true, but I would like to know how(obviously it's not a guess...).

Thank you.
 
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  • #2
It isn't clear what "how" means in this context but I'll give this a shot.

I assume all matrices are ##n## x## n##

note that any circulant matrix ##\mathbf S## can be written as follows

##\mathbf S = \sum_{k=0}^{n-1} s_k \mathbf P^k##
(you should check this for yourself)

where

##\mathbf P = \begin{bmatrix}
0 & 0 &0 & \cdots &0 & 1\\
1 & 0 &0 &\cdots &0 & 0\\
0 & 1 &0 & \cdots &0 &0 \\
0 & 0 &1& \cdots &0 & 0\\
0 & 0 &0& \ddots &0 & 0\\
0 & 0 &0& \cdots &1 & 0
\end{bmatrix}##

##\mathbf P## can be interpreted as a permutation matrix (that is associated with a connected graph). This means it is unitarily diagonalizable and all of its eigenvalues have magnitude 1.

##\mathbf P## can further be interpreted as a Companion matrix associated with ##p(x) = x^n - 1##, and hence its eigenvalues are nth roots of unity. Hence ##\mathbf P= \mathbf F \mathbf \Lambda \mathbf F^* ##, where ##\mathbf \Lambda## is a diagonal matrix that contains nth roots of unity, and ##\mathbf F## is the unitary version of the Vandermonde matrix, i.e. discrete Fourier transform matrix.

As an aside, it maybe worth pointing out that Companion matrices are a bit 'brittle' and are always defective unless all of their eigenvalues are unique (i.e. algebraic multiplicity of 1 for each eig). But we know that this matrix cannot be defective because it is a permutation matrix (which must be unitarily diagonalizable) hence we know it has n unique roots.

The fact that ##\mathbf P## is a companion matrix associated with ##p(x) = x^n - 1##, and a permutation (read: normal) matrix, and serves as a basis for generating circulant matrices -- those three things lead to the result you're asking about.

Using all of the above we can re-write things as:

##\mathbf S = \sum_{k=0}^{n-1} s_k \mathbf P^k = \sum_{k=0}^{n-1} s_k \big(\mathbf F \mathbf \Lambda \mathbf F^*\big)^k = \sum_{k=0}^{n-1} s_k \mathbf F \mathbf \Lambda^k \mathbf F^* = \sum_{k=0}^{n-1} \mathbf F \big(s_k \mathbf \Lambda^k\big) \mathbf F^* = \mathbf F\big(\sum_{k=0}^{n-1} s_k \mathbf \Lambda^k\big) \mathbf F^*##
 
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1. What are eigenvalues of circulant matrices?

The eigenvalues of a circulant matrix are the roots of its characteristic polynomial. They represent the directions and magnitudes of the matrix's principal axes.

2. How are circulant matrices related to eigenvalues?

Circulant matrices are known for having easily identifiable eigenvalues, as they can be obtained from the first row of the matrix. This property makes them useful in solving systems of linear equations and in signal processing.

3. How do you calculate the eigenvalues of a circulant matrix?

The eigenvalues of a circulant matrix can be calculated using the discrete Fourier transform. Alternatively, the eigenvalues can also be found by taking the powers of the first element of the first row of the matrix.

4. Can circulant matrices have complex eigenvalues?

Yes, circulant matrices can have complex eigenvalues. This is because the characteristic polynomial of a circulant matrix can have complex coefficients, resulting in complex roots.

5. What are some applications of circulant matrices and their eigenvalues?

Circulant matrices and their eigenvalues have various applications in fields such as signal processing, coding theory, and graph theory. They are also used in solving linear systems of differential equations and in image processing.

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