What is the Kronecker Product?

The Kronecker product of two matrices A\in\mathbb{C}^{m\times n} and B\in\mathbb{C}^{p\times q} (also called the tensor product) is the mp\times nq matrix1

A \otimes B =  \begin{bmatrix}     a_{11}B & a_{12}B & \dots & a_{1n}B \\     a_{21}B & a_{22}B & \dots & a_{2n}B \\     \vdots & \vdots   & \ddots & \vdots \\     a_{m1}B & a_{m2}B & \dots & a_{mn}B     \end{bmatrix}.

In other words, A\otimes B is the block m\times n matrix with (i,j) block a_{ij}B. For example,

\begin{bmatrix} a_{11} & a_{12} \\ a_{21} & a_{22} \end{bmatrix} \otimes \begin{bmatrix} b_{11} & b_{12} & b_{13} \\ b_{21} & b_{22} & b_{23} \end{bmatrix} = \left[\begin{array}{ccc|ccc} a_{11}b_{11} & a_{11}b_{12} & a_{11}b_{13} & a_{12}b_{11} & a_{12}b_{12} & a_{12}b_{13} \\ a_{11}b_{21} & a_{11}b_{22} & a_{11}b_{23} & a_{12}b_{21} & a_{12}b_{22} & a_{12}b_{23} \\\hline a_{21}b_{11} & a_{21}b_{12} & a_{21}b_{13} & a_{22}b_{11} & a_{22}b_{12} & a_{22}b_{13} \\ a_{21}b_{21} & a_{21}b_{22} & a_{21}b_{23} & a_{22}b_{21} & a_{22}b_{22} & a_{22}b_{23} \end{array}\right].

Notice that the entries of A\otimes B comprise every possible product a_{ij}b_{rs}, which is not the case for the usual matrix product AB when it is defined. Indeed if A and B are n\times n then

  • AB is n\times n and contains sums of n^3 of the products a_{ij}b_{rs},
  • A\otimes B is n^2\times n^2 and contains all n^4 products a_{ij}b_{rs}.

Two key properties of the Kronecker product are

\begin{aligned}       (A\otimes B)^* &= A^* \otimes B^*,   \label{KP1} \\       (A\otimes B)(C\otimes D)&= AC \otimes BD. \label{KP2} \end{aligned}

The second equality implies that when x_i is an eigenvector of A\in\mathbb{C}^{m\times m} with eigenvalue \lambda_i and y_j is an eigenvector of B\in\mathbb{C}^{n\times n} with eigenvalue \mu_j then

(A\otimes B)(x_i\otimes y_j) = (Ax_i \otimes By_j)                              = (\lambda_i x_i \otimes \mu_j y_j)                              = \lambda_i\mu_j ( x_i \otimes y_j),

so that \lambda_i\mu_j is an eigenvalue of A\otimes B with eigenvector x_i \otimes y_j. In fact, the mn eigenvalues of A\otimes B are precisely \lambda_i\mu_j for i = 1\colon m and j = 1\colon n.

Kronecker product structure arises in image deblurring models in which the blur is separable, that is, the blur in the horizontal direction can be separated from the blur in the vertical direction. Kronecker products also arise in the construction of Hadamard matrices. Recall that a Hadamard matrix is a matrix of \pm1s whose rows and columns are mutually orthogonal. If H_n is an n\times n Hadamard matrix then

\begin{bmatrix} \phantom{-} 1 & 1 \\ -1 & 1 \end{bmatrix} \otimes H_n = \begin{bmatrix} \phantom{-} H_n & H_n \\ -H_n & H_n \end{bmatrix}

is a 2n\times 2n Hadamard matrix.

The practical significance of Kronecker product structure is that it allows computations on a large matrix to be reduced to computations on smaller matrices. For example, suppose A and B are Hermitian positive definite matrices and C = A \otimes B, which can be shown to be Hermitian positive definite from the properties mentioned above. If A = R^*R and B = S^*S are Cholesky factorizations then

A \otimes B = (R^*R) \otimes (S^*S)               = (R^*\otimes S^*) (R \otimes S)               = (R \otimes S)^* (R \otimes S),

so R\otimes S, which is easily seen to be triangular with positive diagonal elements, is the Cholesky factor of A\otimes B. If A and B are n\times n then forming A\otimes B and computing its Cholesky factorization costs O(n^6) flops, whereas R and S can be computed in O(n^3) flops. If we want to solve a linear system (A \otimes B)x = b this can be done using R and S without explicitly form R\otimes S.

Vec Operator

The vec operator stacks the columns of a matrix into one long vector: if A = [a_1,a_2,\dots,a_m] then \mathrm{vec}(A) = [a_1^T a_2^T \dots a_m^T]^T. The vec operator and the Kronecker product interact nicely: for any A, X, and B for which the product AXB is defined,

\mathrm{vec}(AXB) = (B^T \otimes A) \mathrm{vec}(X).

This relation allows us to express a linear system AXB = C in the usual form “Ax=b”.

Kronecker Sum

The Kronecker sum of A\in\mathbb{C}^{m\times m} and B\in\mathbb{C}^{n\times n} is defined by A\oplus B = A \otimes I_n + I_m \otimes B. The eigenvalues of A\oplus B are \lambda_{ij} = \lambda_i(A) + \lambda_j(B), i=1\colon m, j=1\colon n, where the \lambda_i(A) are the eigenvalues of A and the \lambda_j(B) are those of B.

The Kronecker sum arises when we apply the vec operator to the matrix AX + XB:

\mathrm{vec}(AX + XB) = (I_m \otimes A + B^T \otimes I_n) \mathrm{vec}(X)                         = (B^T \oplus A) \mathrm{vec}(X).

Kronecker sum structure also arises in finite difference discretizations of partial differential equations, such as when Poisson’s equation is discretized on a square by the usual five-point operator.

Vec-Permutation Matrix

Since for A\in\mathbb{C}^{m\times n} the vectors \mathrm{vec}(A) and \mathrm{vec}(A^T) contain the same mn elements in different orders, we must have

\mathrm{vec}(A^T) = \Pi_{m,n} \mathrm{vec}(A),

for some mn\times mn permutation matrix \Pi_{m,n}. This matrix is called the the vec-permutation matrix, and is also known as the commutation matrix.

Kronecker multiplication is not commutative, that is, A \otimes B \ne B \otimes A in general, but A \otimes B and B \otimes A do contain the same elements in different orders. In fact, the two matrices are related by row and column permutations: if A\in\mathbb{C}^{m\times n} and B\in\mathbb{C}^{p\times q} then

(A\otimes B)\Pi_{n,q} = \Pi_{m,p} (B\otimes A).

This relation can be obtained as follows: for X\in\mathbb{C}^{n\times q},

\begin{aligned} (B \otimes A)\mathrm{vec}(X) &= \mathrm{vec}(AXB^T)\\                              &= \Pi_{p,m} \mathrm{vec}(BX^TA^T)\\                              &= \Pi_{p,m} (A\otimes B)\mathrm{vec}(X^T)\\                              &= \Pi_{p,m} (A\otimes B) \Pi_{n,q}\mathrm{vec}(X). \end{aligned}

Since these equalities hold for all X, we have B\otimes A = \Pi_{p,m} (A\otimes B) \Pi_{n,q}, from which the relation follows on using \Pi_{m,n}\Pi_{n,m} = I, which can be obtained by replacing A by A^T in the definition of vec.

An explicit expression for the the vec-permutation matrix is

\Pi_{m,n} = \displaystyle\sum_{i=1}^m \sum_{j=1}^n                   (e_i^{} e_j^T) \otimes (e_j^{} e_i^T),

where e_i is the ith unit vector.

The following plot shows the sparsity patterns of all the vec permutation matrices \Pi_{m,n} with mn = 120, where the title of each subplot is (m,n).



In MATLAB the Kronecker product A\otimes B can be computed as kron(A,B) and \mathrm{vec}(A) is obtained by indexing with a colon: A(:). Be careful using kron as it can generate very large matrices!

Historical Note

The Kronecker product is named after Leopold Kronecker (1823–1891). Henderson et al. (1983) suggest that it should be called the Zehfuss product, after Johann Georg Zehfuss (1832–1891), who obtained the result \det(A\otimes B) = \det(A)^n \det(B)^m for A\in\mathbb{C}^{m\times m} and B\in\mathbb{C}^{n\times n} in 1858.


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

Related Blog Posts

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.



The \otimes symbol is typed in \LaTeX as \otimes.

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1 Response to What is the Kronecker Product?

  1. José-Javier Martínez says:

    Dear Professor Higham,
    I would like to add a classical source of the Kronecker product of two matrices, briefly commented in the paper by Van Loan: bivariate polynomial interpolation. In fact, in a natural way a “generalized Kronecker product” arises.
    See, for instance, a recent recall of these facts in our paper “Accurate polynomial interpolation by using the Bernstein basis” (Numerical Algorithms, 2017):


    Thank you very much for your work.

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