In mathematics, especially in linear algebra and matrix theory, the commutation matrix is used for transforming the vectorized form of a matrix into the vectorized form of its transpose. Specifically, the commutation matrix K(m,n) is the nm × mn matrix which, for any m × n matrix A, transforms vec(A) into vec(AT):
K(m,n) vec(A) = vec(AT) .
Here vec(A) is the mn × 1 column vector obtain by stacking the columns of A on top of one another:
where A = [Ai,j]. In other words, vec(A) is the vector obtained by vectorizing A in column-major order. Similarly, vec(AT) is the vector obtaining by vectorizing A in row-major order.
In the context of quantum information theory, the commutation matrix is sometimes referred to as the swap matrix or swap operator [1]
The commutation matrix is a special type of permutation matrix, and is therefore orthogonal. In particular, K(m,n) is equal to , where is the permutation over for which
Replacing A with AT in the definition of the commutation matrix shows that K(m,n) = (K(n,m))T. Therefore in the special case of m = n the commutation matrix is an involution and symmetric.
The main use of the commutation matrix, and the source of its name, is to commute the Kronecker product: for every m × n matrix A and every r × q matrix B,
This property is often used in developing the higher order statistics of Wishart covariance matrices.[2]
The case of n=q=1 for the above equation states that for any column vectors v,w of sizes m,r respectively,
This property is the reason that this matrix is referred to as the "swap operator" in the context of quantum information theory.
An explicit form for the commutation matrix is as follows: if er,j denotes the j-th canonical vector of dimension r (i.e. the vector with 1 in the j-th coordinate and 0 elsewhere) then
The commutation matrix may be expressed as the following block matrix:
Where the p,q entry of n x m block-matrix Ki,j is given by
For example,
Code[]
For both square and rectangular matrices of m rows and n columns, the commutation matrix can be generated by the code below.
Python[]
importnumpyasnpdefcomm_mat(m,n):# determine permutation applied by KA=np.reshape(np.arange(m*n),(m,n),order='F')w=np.reshape(A.T,m*n,order='F')# apply this permutation to the rows (i.e. to each column) of identity matrixM=np.eye(m*n)M=M[w,:]returnM
Alternatively, a version without imports:
# Kronecker deltadefdelta(i,j):returnint(i==j)defcomm_mat(m,n):# determine permutation applied by Kv=[m*j+iforiinrange(m)forjinrange(n)]# apply this permutation to the rows (i.e. to each column) of identity matrixM=[[delta(i,j)forjinrange(m*n)]foriinrange(m*n)]M=[M[i]foriinv]returnM
Matlab[]
functionP=com_mat(m,n)% determine permutation applied by KA=reshape(1:m*n,m,n);v=reshape(A',1,[]);% apply this permutation to the rows (i.e. to each column) of identity matrixP=eye(m*n);P=P(v,:);
Example[]
Let denote the following matrix:
has the following column-major and row-major vectorizations (respectively):
The associated commutation matrix is
(where each denotes a zero). As expected, the following holds:
References[]
^Watrous, John (2018). The Theory of Quantum Information. Cambridge University Press. p. 94.
^von Rosen, Dietrich (1988). "Moments for the Inverted Wishart Distribution". Scand. J. Stat. 15: 97–109.
Jan R. Magnus and Heinz Neudecker (1988), Matrix Differential Calculus with Applications in Statistics and Econometrics, Wiley.