Decentralized non-convex optimization via bi-level SQP and ADMM

Publikationstyp
Conference Paper
Date Issued
2022-01-01
Sprache
English
Author(s)
Stomberg, Gösta  
Engelmann, Alexander
Faulwasser, Timm  
TORE-URI
https://hdl.handle.net/11420/46019
Volume
2022-December
Start Page
273
End Page
278
Citation
61st IEEE Conference on Decision and Control (CDC 2022)
Contribution to Conference
IEEE 61st Conference on Decision and Control, CDC 2022  
Publisher DOI
10.1109/CDC51059.2022.9992379
Scopus ID
2-s2.0-85144977515
Publisher
IEEE
ISBN
9781665467612
Decentralized non-convex optimization is important in many problems of practical relevance. Existing decentralized methods, however, typically either lack convergence guarantees for general non-convex problems, or they suffer from a high subproblem complexity. We present a novel bilevel SQP method, where the inner quadratic problems are solved via ADMM. A decentralized stopping criterion from inexact Newton methods allows the early termination of ADMM as an inner algorithm to improve computational efficiency. The method has local convergence guarantees for non-convex problems. Moreover, it only solves sequences of Quadratic Programs, whereas many existing algorithms solve sequences of Nonlinear Programs. The method shows competitive numerical performance for an optimal power flow problem.
DDC Class
004: Computer Sciences
510: Mathematics