  Distributed and decentralized optimization are key for the control of
networked systems. Application examples include distributed model predictive
control and distributed sensing or estimation. Non-linear systems, however,
lead to problems with non-convex constraints for which classical decentralized
optimization algorithms lack convergence guarantees. Moreover, classical
decentralized algorithms usually exhibit only linear convergence. This paper
presents an essentially decentralized primal-dual interior point method with
convergence guarantees for non-convex problems at a superlinear rate. We show
that the proposed method works reliably on a numerical example from power
systems. Our results indicate that the proposed method outperforms ADMM in
terms of computation time and computational complexity of the subproblems.
