An essentially decentralized interior point method for control

Publikationstyp
Conference Paper
Date Issued
2021
Sprache
English
Author(s)
Engelmann, Alexander
Stomberg, Gösta  
Faulwasser, Timm  
TORE-URI
https://hdl.handle.net/11420/46188
Volume
2021-December
Start Page
2414
End Page
2420
Citation
Proceedings of the 60th IEEE Conference on Decision and Control 2021: 2414-2420 (2021)
Contribution to Conference
60th IEEE Conference on Decision and Control, CDC 2021  
Publisher DOI
10.1109/CDC45484.2021.9683694
Scopus ID
2-s2.0-85126054954
Publisher
IEEE
ISBN
9781665436595
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 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.
Subjects
decentralized optimization
interior point methods
non-convex optimization
optimal power flow
DDC Class
004: Computer Sciences
333.7: Natural Resources, Energy and Environment
510: Mathematics