  The regression problem associated with finding a matrix approximation of the
Koopman operator from data is considered. The regression problem is formulated
as a convex optimization problem subject to linear matrix inequality (LMI)
constraints. Doing so allows for additional LMI constraints to be incorporated
into the regression problem. In particular, asymptotic stability constraints,
regularization using matrix norms, and even regularization using system norms
can be easily incorporated into the regression problem.
