What Is a Circulant Matrix?

An n\times n circulant matrix is defined by n parameters, the elements in the first row, and each subsequent row is a cyclic shift forward of the one above:

\notag    C = \begin{bmatrix} c_1     & c_2    & \dots   & c_n     \\                        c_n     & c_1    & \dots   & \vdots  \\                        \vdots  & \ddots & \ddots  & c_2     \\                        c_2     & \dots  & c_n     & c_1     \\      \end{bmatrix}.

Circulant matrices have the important property that they are diagonalized by the discrete Fourier transform matrix

\notag      F_n = \Bigl(\exp( -2\pi \mathrm{i} (r-1)(s-1) / n )\Bigr)_{r,s=1}^n,

which satisfies F_n^*F_n = nI, so that n^{-1/2}F_n is a unitary matrix. (F_n is a Vandermonde matrix with points the roots of unity.) Specifically,

\notag          F_n C F_n^{-1} = D = \mathrm{diag}(d_i).  \qquad(1)

Hence circulant matrices are normal (C^*C = CC^*). Moreover, the eigenvalues are given by d = F_n Ce_1, where e_1 is the first unit vector.

Note that one particular eigenpair is immediate, since Ce = \bigl(\sum_{i=1}^n c_i\bigr) e, where e is the vector of ones.

The factorization (1) enables a circulant linear system to be solved in O(n\log n) flops, since multiplication by F_n can be done using the fast Fourier transform.

A particular circulant matrix is the (up) shift matrix K_n, the 4\times 4 version of which is

\notag   K_4 = \begin{bmatrix}   0 & 1 & 0 & 0  \\   0 & 0 & 1 & 0  \\   0 & 0 & 0 & 1  \\   1 & 0 & 0 & 0 \end{bmatrix}.

It is not hard to see that

C =  c_1 I + c_2 K_n + c_3K_n^2 + \cdots + c_n K_n^{n-1}.

Since powers of K_n commute, it follows that any two circulant matrices commute (this is also clear from (1)). Furthermore, the sum and product of two circulant matrices is a circulant matrix and the inverse of a nonsingular circulant matrix is a circulant matrix.

One important use of circulant matrices is to act as preconditioners for Toeplitz linear systems. Several methods have been proposed for constructing a circulant matrix from a Toeplitz matrix, including copying the central diagonals and wrapping them around and finding the nearest circulant matrix to the Toeplitz matrix. See Chan and Ng (1996) or Chan and Jin (2017) for a summary of work on circulant preconditioners for Toeplitz systems.

An interesting circulant matrix is anymatrix('core/circul_binom',n) in the Anymatrix collection, which is the n\times n circulant matrix whose first row has ith element n \choose ,i-1. The eigenvalues are (1 + w^i)^n - 1, i=1:n, where w = \exp(2\pi\mathrm{i}/n). The matrix is singular when n is a multiple of 6, in which case the null space has dimension 2. Example:

>> n = 6; C = anymatrix('core/circul_binom',n), svd(C)
C =
     1     6    15    20    15     6
     6     1     6    15    20    15
    15     6     1     6    15    20
    20    15     6     1     6    15
    15    20    15     6     1     6
     6    15    20    15     6     1
ans =


A classic reference on circulant matrices is Davis (1994).


This article is part of the “What Is” series, available from https://nhigham.com/category/what-is and in PDF form from the GitHub repository https://github.com/higham/what-is.

Photos and Videos from NJH60 Conference

The conference Advances in Numerical Linear Algebra: Celebrating the 60th Birthday of Nick Higham was held at the University of Manchester, July 6–8, 2022.

Most of the talks are available on the NLA Group YouTube channel and links to them are available on the conference web page.

Here is the conference photo.


(Hires version)

Here is me with some of my current and former PhD students.

220707-1230-52_3021_1024px-1.jpg (Hires version)

And with some of my current and former postdocs.


(Hires version)

After-dinner speaker Charlie Van Loan: 220707_20-24-34_1024px.jpg

Rob Corless kindly gave me a Bohemian matrix eigenvalue tie based on this image. 220717_14-46-08_1024px.jpg

Many thanks to Stefan Güttel, Sven Hammarling, Stephanie Lai, Françoise Tisseur and Marcus Webb for organizing the conference and the Royal Society, MathWorks and the University of Manchester for financial support.

What Is Fast Matrix Multiplication?

The definition of matrix multiplication says that for n\times n matrices A and B, the product C = AB is given by c_{ij} = \sum_{k=1}^n a_{ik}b_{kj}. Each element of C is an inner product of a row of A and a column of B, so if this formula is used then the cost of forming C is n^2(2n-1) additions and multiplications, that is, O(n^3) operations. For over a century after the development of matrix algebra in the 1850s by Cayley, Sylvester and others, all methods for matrix multiplication were based on this formula and required O(n^3) operations.

In 1969 Volker Strassen showed that when n=2 the product can be computed from the formulas

\notag \begin{gathered} p_1 =(a_{11}+a_{22})(b_{11}+b_{22}), \\ p_2=(a_{21}+a_{22})b_{11}, \qquad p_3=a_{11}(b_{12}-b_{22}), \\ p_4=a_{22}(b_{21}-b_{11}), \qquad p_5=(a_{11}+a_{12})b_{22}, \\ p_6=(a_{21}-a_{11})(b_{11}+b_{12}), \qquad p_7=(a_{12}-a_{22})(b_{21}+b_{22}), \\ \noalign{\smallskip} C = \begin{bmatrix} p_1+p_4-p_5+p_7 & p_3+p_5 \\ p_2+p_4 & p_1+p_3-p_2+p_6 \end{bmatrix}. \end{gathered}

The evaluation requires 7 multiplications and 18 additions instead of 8 multiplications and 4 additions for the usual formulas.

At first sight, Strassen’s formulas may appear simply to be a curiosity. However, the formulas do not rely on commutativity so are valid when the a_{ij} and b_{ij} are matrices, in which case for large dimensions the saving of one multiplication greatly outweighs the extra 14 additions. Assuming n is a power of 2, we can partition A and B into four blocks of size n/2, apply Strassen’s formulas for the multiplication, and then apply the same formulas recursively on the half-sized matrix products.

Let us examine the number of multiplications for the recursive Strassen algorithm. Denote by M(k) the number of scalar multiplications required to multiply two 2^k \times 2^k matrices. We have M(k) = 7M(k-1), so

M(k) = 7M(k-1) = 7^2M(k-2) = \cdots = 7^k M(0) = 7^k.

But 7^k = 2^{\log_2{7^k}} = (2^k)^{\log_2 7} = n^{\log_2 7} = n^{2.807\dots}. The number of additions can be shown to be of the same order of magnitude, so the algorithm requires O(n^{\log_27})=O(n^{2.807\dots}) operations.

Strassen’s work sparked interest in finding matrix multiplication algorithms of even lower complexity. Since there are O(n^2) elements of data, which must each participate in at least one operation, the exponent of n in the operation count must be at least 2.

The current record upper bound on the exponent is 2.37286, proved by Alman and Vassilevska Williams (2021) which improved on the previous record of 2.37287, proved by Le Gall (2014) The following figure plots the best upper bound for the exponent for matrix multiplication over time.


In the methods that achieve exponents lower than 2.775, various intricate techniques are used, based on representing matrix multiplication in terms of bilinear or trilinear forms and their representation as tensors having low rank. Laderman, Pan, and Sha (1993) explain that for these methods “very large overhead constants are hidden in the `O‘ notation”, and that the methods “improve on Strassen’s (and even the classical) algorithm only for immense numbers N.”

Strassen’s method, when carefully implemented, can be faster than conventional matrix multiplication for reasonable dimensions. In practice, one does not recur down to 1\times 1 matrices, but rather uses conventional multiplication once n_0\times n_0 matrices are reached, where the parameter n_0 is tuned for the best performance.

Strassen’s method has the drawback that it satisfies a weaker form of rounding error bound that conventional multiplication. For conventional multiplication C = AB of A\in\mathbb{R}^{m\times n} and B\in\mathbb{R}^{n\times p} we have the componentwise bound

\notag        |C - \widehat{C}| \le \gamma^{}_n |A|\, |B|, \qquad(1)

where \gamma^{}_n = nu/(1-nu) and u is the unit roundoff. For Strassen’s method we have only a normwise error bound. The following result uses the norm \|A\| = \max_{i,j} |a_{ij}|, which is not a consistent norm.

Theorem 1 (Brent). Let A,B \in \mathbb{R}^{n\times n}, where n=2^k. Suppose that C=AB is computed by Strassen’s method and that n_0=2^r is the threshold at which conventional multiplication is used. The computed product \widehat{C} satisfies

\notag    \|C - \widehat{C}\| \le \left[ \Bigl( \displaystyle\frac{n}{n_0} \Bigr)^{\log_2{12}}                       (n_0^2+5n_0) - 5n \right] u \|A\|\, \|B\|                       + O(u^2). \qquad(2)

With full recursion (n_0=1) the constant in (2) is 6 n^{\log_2 12}-5n \approx 6 n^{3.585}-5n, whereas with just one level of recursion (n_0 = n/2) it is 3n^2+25n. These compare with n^2u + O(u^2) for conventional multiplication (obtained by taking norms in (1)). So the constant for Strassen’s method grows at a faster rate than that for conventional multiplication no matter what the choice of n_0.

The fact that Strassen’s method does not satisfy a componentwise error bound is a significant weakness of the method. Indeed Strassen’s method cannot even accurately multiply by the identity matrix. The product

\notag       C = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix}           \begin{bmatrix} 1 & \epsilon \\ \epsilon & \epsilon^2 \end{bmatrix},             \quad 0 < \epsilon \ll 1

is evaluated exactly in floating-point arithmetic by conventional multiplication, but Strassen’s method computes

c_{22} = 2(1+\epsilon^2) + (\epsilon-\epsilon^2) - 1 - (1+\epsilon).

Because c_{22} involves subterms of order unity, the error c_{22} - \widehat c_{22} will be of order u. Thus the relative error |c_{22} - \widehat c_{22}| / |c_{22}| = O(u/\epsilon^2) \gg O(u),

Another weakness of Strassen’s method is that while the scaling AB \to (AD) (D^{-1}B), where D is diagonal, leaves (1) unchanged, it can alter (2) by an arbitrary amount. Dumitrescu (1998) suggests computing D_1(D_1^{-1}A\cdot B D_2^{-1})D_2, where the diagonal matrices D_1 and D_2 are chosen to equilibrate the rows of A and the columns of B in the \infty-norm; he shows that this scaling can improve the accuracy of the result. Further investigations along these lines are made by Ballard et al. (2016).

Should one use Strassen’s method in practice, assuming that an implementation is available that is faster than conventional multiplication? Not if one needs a componentwise error bound, which ensures accurate products of matrices with nonnegative entries and ensures that the column scaling of A and row scaling of B has no effect on the error. But if a normwise error bound with a faster growing constant than for conventional multiplication is acceptable then the method is worth considering.


For recent work on high-performance implementation of Strassen’s method see Huang et al. (2016, 2020).

Theorem 1 is from an unpublished technical report of Brent (1970). A proof can be found in Higham (2002, §23.2.2).

For more on fast matrix multiplication see Bini (2014) and Higham (2002, Chapter 23).


This is a minimal set of references, which contain further useful references within.

This article is part of the “What Is” series, available from https://nhigham.com/category/what-is and in PDF form from the GitHub repository https://github.com/higham/what-is.