  As distributed systems increase in size, the need for scalable algorithms
becomes more and more important. We argue that in the context of system
identification, an essential building block of any scalable algorithm is the
ability to estimate local dynamics within a large interconnected system. We
show that in what we term the "full interconnection measurement" setting, this
task is easily solved using existing system identification methods. We also
propose a promising heuristic for the "hidden interconnection measurement"
case, in which contributions to local measurements from both local and global
dynamics need to be separated. Inspired by the machine learning literature, and
in particular by convex approaches to rank minimization and matrix
decomposition, we exploit the fact that the transfer function of the local
dynamics is low-order, but full-rank, while the transfer function of the global
dynamics is high-order, but low-rank, to formulate this separation task as a
nuclear norm minimization.
