I have a special type of companion matrix, where the "special" part is that each element in the matrix are matrices. For instance, the diagonal with "1":s is instead a diagonal with identity matrices, and so on... My question is if anyone knows of a solid algorithm for finding the eigen-decomposition of such a matrix? If you don't know the exact algorithm, perhaps you know of some work that has been done in the area?
If anyone is interested, I can clarify what my eigendecomposition will be used; AND this poses another problem, which is really the core problem I am facing.
I want to simulate a companion matrix (p x p), with each element being a matrix (n x n). The only restriction I have is that all eigenvalues must satisfy |λ|<1 . Ultimately, I would like to simulate the eigenvalues from (−1,1) , and then produce a companion matrix from that. I realize there might be (most likely are) multiple solutions, but that does not matter. I would settle for any solution. :)
My idea of solving this is to look at the general eigendecomposition of a companion matrix, simulate the eigenvalues, possible simulate the eigenvectors, and then reproduce the companion matrix.
Am I making any sense with this? I hope you understand the issue I have, and if not, please feel free to ask. As I said above, this area is fairly new to me...