I’m working on a project these days that uses four different kinds of matrix product, which made me wonder if there’s another kind of product out there that I could find some use for.

In the process of looking around for other matrix products, I ran across the **Kronecker sum**. I’ve seen Kronecker *products* many times, but I’d never heard of Kronecker *sums.*

The Kronecker sum is defined in terms of the Kronecker product, so if you’re not familiar with the latter, you can find a definition and examples here. Essentially, you multiply each scalar element of the first matrix by the second matrix *as a block matrix*.

The Kronecker product of an *m* × *n* matrix *A* and a *p* × *q* matrix *B* is a *mp* × *nq* matrix *K* = *A *⊗* B*. You could think of *K* as an *m* × *n* matrix whose entries are *p* × *q* blocks.

So, what is the Kronecker sum? It is defined for two square matrices, an *n* × *n* matrix *A* and an *m* × *m* matrix *B*. The sizes of the two matrices need not match, but the matrices do need to be square. The Kronecker sum of *A* and *B* is

*A* ⊕ *B* = *A* ⊗ *I*_{m} + *I*_{n} ⊗ *B*

where *I*_{m} and *I*_{n} are identity matrices of size *m* and *n* respectively.

Does this make sense dimensionally? The left side of the (ordinary) matrix addition is *nm* × *nm*, and so is the right side, so the addition makes sense.

However, the Kronecker sum is not commutative, and usually things called “sums” are commutative. Products are not always commutative, but it goes against convention to call a non-commutative operation a sum. Still, the Kronecker sum is kinda like a sum, so it’s not a bad name.

I don’t have any application in mind (*yet*) for the Kronecker sum, but presumably it was defined for a good reason, and maybe I’ll run an application, maybe even on the project alluded to at the beginning.

There are several identities involving Kronecker sums, and here’s one I found interesting:

exp( *A* ) ⊗ exp( *B* ) = exp( *A *⊕ *B* ).

If you haven’t seen the exponential of a matrix before, basically you stick your matrix into the power series for the exponential function.

## Examples

First, let’s define a couple matrices *A* and *B*.

We can compute the Kronecker sums

*S* = *A* ⊕ *B*

and

*T* = *B* ⊕ *A*

with Mathematica to show they are different.

A = {{1, 2}, {3, 4}}
B = {{1, 0, 1}, {1, 2, 0}, {2, 0, 3}}
S = KroneckerProduct[A, IdentityMatrix[3]] +
KroneckerProduct[IdentityMatrix[2], B]
T = KroneckerProduct[B, IdentityMatrix[2]] +
KroneckerProduct[IdentityMatrix[3], A]

This shows

and so the two matrices are not equal.

We can compute the matrix exponentials of *A* and *B* with the Mathematica function `MatrixExp`

to see that

(I actually used `MatrixExp[N[A]]`

and similarly for *B* so Mathematica would compute the exponentials numerically rather than symbolically. The latter takes forever and it’s hard to read the result.)

Now we have

and so the two matrices are equal.

Even though the identity

exp( *A* ) ⊗ exp( *B* ) = exp( *A *⊕ *B* )

may look symmetrical, it’s not. The matrices on the left do not commute in general. And not only are *A* ⊕ *B* and *B* ⊕ *A* different in general, their exponentials are also different. For example

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