If you’re doing a lot of simulations, optimizations or other muckity-muck, it’s easy to start writing down equations with matrices like A^{-1}B inside of them. And, depending on the application, it might be a really good idea to precompute this matrix. But there’s that pesky inverse operation, and generally inverting a matrix is a no-no since it can easily land you in trouble when that matrix isn’t really very stable.

So, here’s a trick you can use in these situations to avoid explicitly inverting the matrix A.  It turns out that instead we can just solve the system Ax=b a bunch of times in order to explicitly compute the product matrix A^{-1}B.

It’s pretty straightforward.  If we want to extract the ith column from A^{-1}B, we need to solve the equation x = A^{-1}B e_i, where e_i is the indicator vector with 0s everywhere except the ith entry, which is 1.  If we just massage this equation a bit, we get Ax = Be_i = b_i where b_i is the ith column of B.  Sweet!  If we have a good way to pre-factor A or otherwise prepare it for repeated solves, we can also keep this solution method pretty efficient.