@inproceedings{b19eb1b27482456784778b980e354734,
title = "An approximate dual subgradient algorithm for multi-agent non-convex optimization",
abstract = "We consider a multi-agent optimization problem where agents aim to cooperatively minimize a sum of local objective functions subject to a global inequality constraint and a global state constraint set. In contrast to existing papers, we do not require the objective, constraint functions, and state constraint sets to be convex. We propose a distributed approximate dual subgradient algorithm to enable agents to asymptotically converge to a pair of approximate primaldual solutions over dynamically changing network topologies. Convergence can be guaranteed provided that the Slater's condition and strong duality property are satisfied.",
author = "Minghui Zhu and Sonia Mart{\'i}nez",
year = "2010",
doi = "10.1109/CDC.2010.5717220",
language = "English (US)",
isbn = "9781424477456",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7487--7492",
booktitle = "2010 49th IEEE Conference on Decision and Control, CDC 2010",
address = "United States",
note = "49th IEEE Conference on Decision and Control, CDC 2010 ; Conference date: 15-12-2010 Through 17-12-2010",
}