Kronecker tensor product
Block Diagonal Matrix
Create a block diagonal matrix.
Create a 4-by-4 identity matrix and a 2-by-2 matrix that you want to be repeated along the diagonal.
A = eye(4); B = [1 -1;-1 1];
kron to find the Kronecker tensor product.
K = kron(A,B)
K = 8×8 1 -1 0 0 0 0 0 0 -1 1 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 -1 1 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 -1 1 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 -1 1
The result is an 8-by-8 block diagonal matrix.
Repeat Matrix Elements
Expand the size of a matrix by repeating elements.
Create a 2-by-2 matrix of ones and a 2-by-3 matrix whose elements you want to repeat.
A = [1 2 3; 4 5 6]; B = ones(2);
Calculate the Kronecker tensor product using
K = kron(A,B)
K = 4×6 1 1 2 2 3 3 1 1 2 2 3 3 4 4 5 5 6 6 4 4 5 5 6 6
The result is a 4-by-6 block matrix.
Sparse Laplacian Operator Matrix
This example visualizes a sparse Laplacian operator matrix.
The matrix representation of the discrete Laplacian operator on a two-dimensional,
n grid is a
n*n sparse matrix. There are at most five nonzero elements in each row or column. You can generate the matrix as the Kronecker product of one-dimensional difference operators. In this example
n = 5.
n = 5; I = speye(n,n); E = sparse(2:n,1:n-1,1,n,n); D = E+E'-2*I; A = kron(D,I)+kron(I,D);
Visualize the sparsity pattern with
A,B — Input matrices
scalars | vectors | matrices
Input matrices, specified as scalars, vectors, or matrices.
B is sparse,
kron multiplies only nonzero elements and
the result is also sparse.
Complex Number Support: Yes
Kronecker Tensor Product
A is an
B is a
then the Kronecker tensor product of
a large matrix formed by multiplying
B by each
For example, two simple 2-by-2 matrices produce
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Usage notes and limitations:
Code generation does not support sparse matrix inputs for this function.
Run code in the background using MATLAB®
backgroundPool or accelerate code with Parallel Computing Toolbox™
This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.
This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).
Introduced before R2006a