Engelmann, AlexanderAlexanderEngelmannStomberg, GöstaGöstaStombergFaulwasser, TimmTimmFaulwasser2024-03-052024-03-052021Proceedings of the 60th IEEE Conference on Decision and Control 2021: 2414-2420 (2021)9781665436595https://hdl.handle.net/11420/46188Distributed 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 de-centralized 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.endecentralized optimizationinterior point methodsnon-convex optimizationoptimal power flowComputer SciencesNatural Resources, Energy and EnvironmentMathematicsConference Paper10.1109/CDC45484.2021.9683694Conference Paper