  System performance for networks composed of interconnected subsystems can be
increased if the traditionally separated subsystems are jointly optimized.
Recently, parallel and distributed optimization methods have emerged as a
powerful tool for solving estimation and control problems in large-scale
networked systems. In this paper we review and analyze the
optimization-theoretic concepts of parallel and distributed methods for solving
coupled optimization problems and demonstrate how several estimation and
control problems related to complex networked systems can be formulated in
these settings. The paper presents a systematic framework for exploiting the
potential of the decomposition structures as a way to obtain different parallel
algorithms, each with a different tradeoff among convergence speed, message
passing amount and distributed computation architecture. Several specific
applications from estimation and process control are included to demonstrate
the power of the approach.
