Linear algebra matrix
In linear algebra, a circulant matrix is a square matrix in which all rows are composed of the same elements and each row is rotated one element to the right relative to the preceding row. It is a particular kind of Toeplitz matrix.
In numerical analysis, circulant matrices are important because they are diagonalized by a discrete Fourier transform, and hence linear equations that contain them may be quickly solved using a fast Fourier transform.[1] They can be interpreted analytically as the integral kernel of a convolution operator on the cyclic group
and hence frequently appear in formal descriptions of spatially invariant linear operations. This property is also critical in modern software defined radios, which utilize Orthogonal Frequency Division Multiplexing to spread the symbols (bits) using a cyclic prefix. This enables the channel to be represented by a circulant matrix, simplifying channel equalization in the frequency domain.
In cryptography, a circulant matrix is used in the MixColumns step of the Advanced Encryption Standard.
Properties
Eigenvectors and eigenvalues
The normalized eigenvectors of a circulant matrix are the Fourier modes, namely,
![{\displaystyle v_{j}={\frac {1}{\sqrt {n))}\left(1,\omega ^{j},\omega ^{2j},\ldots ,\omega ^{(n-1)j}\right),\quad j=0,1,\ldots ,n-1,}](https://wikimedia.org/api/rest_v1/media/math/render/svg/4747084e869587eaf28d5f8c9bcba90cffbfe4c9)
where
is a primitive
-th root of unity and
is the imaginary unit.
(This can be understood by realizing that multiplication with a circulant matrix implements a convolution. In Fourier space, convolutions become multiplication. Hence the product of a circulant matrix with a Fourier mode yields a multiple of that Fourier mode, i.e. it is an eigenvector.)
The corresponding eigenvalues are given by
![{\displaystyle \lambda _{j}=c_{0}+c_{1}\omega ^{j}+c_{2}\omega ^{2j}+\dots +c_{n-1}\omega ^{(n-1)j},\quad j=0,1,\dots ,n-1.}](https://wikimedia.org/api/rest_v1/media/math/render/svg/666637f22c3d0c0bc3eabf9ca052f01f63fbf77b)
Determinant
As a consequence of the explicit formula for the eigenvalues above,
the determinant of a circulant matrix can be computed as:
![{\displaystyle \det C=\prod _{j=0}^{n-1}(c_{0}+c_{n-1}\omega ^{j}+c_{n-2}\omega ^{2j}+\dots +c_{1}\omega ^{(n-1)j}).}](https://wikimedia.org/api/rest_v1/media/math/render/svg/90d4e945a0bb9ee5e5b59a39761d76baeea5d23f)
Since taking the transpose does not change the eigenvalues of a matrix, an equivalent formulation is
![{\displaystyle \det C=\prod _{j=0}^{n-1}(c_{0}+c_{1}\omega ^{j}+c_{2}\omega ^{2j}+\dots +c_{n-1}\omega ^{(n-1)j})=\prod _{j=0}^{n-1}f(\omega ^{j}).}](https://wikimedia.org/api/rest_v1/media/math/render/svg/adc27fe55fa5f4c93a88704ddbcf7be414e970a8)
Rank
The rank of a circulant matrix
is equal to
where
is the degree of the polynomial
.[2]
Other properties
- Any circulant is a matrix polynomial (namely, the associated polynomial) in the cyclic permutation matrix
: ![{\displaystyle C=c_{0}I+c_{1}P+c_{2}P^{2}+\dots +c_{n-1}P^{n-1}=f(P),}](https://wikimedia.org/api/rest_v1/media/math/render/svg/5d0ccbb9553e16f763e7f972b1d7c4db53ffb920)
where
is given by the companion matrix ![{\displaystyle P={\begin{bmatrix}0&0&\cdots &0&1\\1&0&\cdots &0&0\\0&\ddots &\ddots &\vdots &\vdots \\\vdots &\ddots &\ddots &0&0\\0&\cdots &0&1&0\end{bmatrix)).}](https://wikimedia.org/api/rest_v1/media/math/render/svg/cedef173fa6e44ded943ee769a5e1b4b4ee7950f)
- The set of
circulant matrices forms an
-dimensional vector space with respect to addition and scalar multiplication. This space can be interpreted as the space of functions on the cyclic group of order
,
, or equivalently as the group ring of
.
- Circulant matrices form a commutative algebra, since for any two given circulant matrices
and
, the sum
is circulant, the product
is circulant, and
.
- For a nonsingular circulant matrix
, its inverse
is also circulant. For a singular circulant matrix, its Moore–Penrose pseudoinverse
is circulant.
- The matrix
that is composed of the eigenvectors of a circulant matrix is related to the discrete Fourier transform and its inverse transform: ![{\displaystyle U_{n}^{*}={\frac {1}{\sqrt {n))}F_{n},\quad {\text{and))\quad U_{n}={\sqrt {n))F_{n}^{-1},{\text{ where ))F_{n}=(f_{jk}){\text{ with ))f_{jk}=e^{-2jk\pi i/n},\,{\text{for ))0\leq j,k<n.}](https://wikimedia.org/api/rest_v1/media/math/render/svg/95c8b379310c736e3cfb05d571c260bbf30a363b)
Consequently the matrix
diagonalizes
. In fact, we have ![{\displaystyle C=U_{n}\operatorname {diag} (F_{n}c)U_{n}^{*}=F_{n}^{-1}\operatorname {diag} (F_{n}c)F_{n},}](https://wikimedia.org/api/rest_v1/media/math/render/svg/2b36568dbc1d1b6a6dec02693c3047383255eaf8)
where
is the first column of
. The eigenvalues of
are given by the product
. This product can be readily calculated by a fast Fourier transform.[3] Conversely, for any diagonal matrix
, the product
is circulant.
- Let
be the (monic) characteristic polynomial of an
circulant matrix
. Then the scaled derivative
is the characteristic polynomial of the following
submatrix of
: ![{\displaystyle C_{n-1}={\begin{bmatrix}c_{0}&c_{n-1}&\cdots &c_{3}&c_{2}\\c_{1}&c_{0}&c_{n-1}&&c_{3}\\\vdots &c_{1}&c_{0}&\ddots &\vdots \\c_{n-3}&&\ddots &\ddots &c_{n-1}\\c_{n-2}&c_{n-3}&\cdots &c_{1}&c_{0}\\\end{bmatrix))}](https://wikimedia.org/api/rest_v1/media/math/render/svg/833f33c0f39a445fcddaff348cd7b054204cbaca)
(see [4] for the proof).
Analytic interpretation
Circulant matrices can be interpreted geometrically, which explains the connection with the discrete Fourier transform.
Consider vectors in
as functions on the integers with period
, (i.e., as periodic bi-infinite sequences:
) or equivalently, as functions on the cyclic group of order
(denoted
or
) geometrically, on (the vertices of) the regular
-gon: this is a discrete analog to periodic functions on the real line or circle.
Then, from the perspective of operator theory, a circulant matrix is the kernel of a discrete integral transform, namely the convolution operator for the function
; this is a discrete circular convolution. The formula for the convolution of the functions
is
![{\displaystyle b_{k}=\sum _{i=0}^{n-1}a_{i}c_{k-i))](https://wikimedia.org/api/rest_v1/media/math/render/svg/d573c7aada47d1f2a3096b32f7908ac9e7be9541)
(recall that the sequences are periodic)
which is the product of the vector
by the circulant matrix for
.
The discrete Fourier transform then converts convolution into multiplication, which in the matrix setting corresponds to diagonalization.
The
-algebra of all circulant matrices with complex entries is isomorphic to the group
-algebra of
Hermitian circulant matrices
The complex version of the circulant matrix, ubiquitous in communications theory, is usually Hermitian. In this case
and its determinant and all eigenvalues are real.
If n is even the first two rows necessarily takes the form
![{\displaystyle {\begin{bmatrix}r_{0}&z_{1}&z_{2}&r_{3}&z_{2}^{*}&z_{1}^{*}\\z_{1}^{*}&r_{0}&z_{1}&z_{2}&r_{3}&z_{2}^{*}\\\dots \\\end{bmatrix)).}](https://wikimedia.org/api/rest_v1/media/math/render/svg/56850afc9f41e3c76f6d8841da68f6e7e924a1a7)
in which the first element
in the top second half-row is real.
If n is odd we get
![{\displaystyle {\begin{bmatrix}r_{0}&z_{1}&z_{2}&z_{2}^{*}&z_{1}^{*}\\z_{1}^{*}&r_{0}&z_{1}&z_{2}&z_{2}^{*}\\\dots \\\end{bmatrix)).}](https://wikimedia.org/api/rest_v1/media/math/render/svg/35d73e3c5acf0589b9ed66ce86ef34b911402bed)
Tee[5] has discussed constraints on the eigenvalues for the Hermitian condition.
Applications
In linear equations
Given a matrix equation
![{\displaystyle C\mathbf {x} =\mathbf {b} ,}](https://wikimedia.org/api/rest_v1/media/math/render/svg/643582c953182921d8e3ecc506e056dc5a674ddc)
where
is a circulant matrix of size
, we can write the equation as the circular convolution
![{\displaystyle \mathbf {c} \star \mathbf {x} =\mathbf {b} ,}](https://wikimedia.org/api/rest_v1/media/math/render/svg/8e575badff76b06bd57fd1d2052fbabc0199a09c)
where
is the first column of
, and the vectors
,
and
are cyclically extended in each direction. Using the circular convolution theorem, we can use the discrete Fourier transform to transform the cyclic convolution into component-wise multiplication
![{\displaystyle {\mathcal {F))_{n}(\mathbf {c} \star \mathbf {x} )={\mathcal {F))_{n}(\mathbf {c} ){\mathcal {F))_{n}(\mathbf {x} )={\mathcal {F))_{n}(\mathbf {b} )}](https://wikimedia.org/api/rest_v1/media/math/render/svg/f4edcc9bcb6de5891c9c86a21b4bd06ab9f3e198)
so that
![{\displaystyle \mathbf {x} ={\mathcal {F))_{n}^{-1}\left[\left({\frac {({\mathcal {F))_{n}(\mathbf {b} ))_{\nu )){({\mathcal {F))_{n}(\mathbf {c} ))_{\nu ))}\right)_{\!\nu \in \mathbb {Z} }\,\right]^{\rm {T)).}](https://wikimedia.org/api/rest_v1/media/math/render/svg/8428523f914059eb0f48e7787b39987f630d1c16)
This algorithm is much faster than the standard Gaussian elimination, especially if a fast Fourier transform is used.
In graph theory
In graph theory, a graph or digraph whose adjacency matrix is circulant is called a circulant graph/digraph. Equivalently, a graph is circulant if its automorphism group contains a full-length cycle. The Möbius ladders are examples of circulant graphs, as are the Paley graphs for fields of prime order.