TY - GEN
T1 - An approximate dual subgradient algorithm for multi-agent non-convex optimization
AU - Zhu, Minghui
AU - Martínez, Sonia
PY - 2010/12/1
Y1 - 2010/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79953149292&partnerID=8YFLogxK
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U2 - 10.1109/CDC.2010.5717220
DO - 10.1109/CDC.2010.5717220
M3 - Conference contribution
AN - SCOPUS:79953149292
SN - 9781424477456
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 7487
EP - 7492
BT - 2010 49th IEEE Conference on Decision and Control, CDC 2010
T2 - 2010 49th IEEE Conference on Decision and Control, CDC 2010
Y2 - 15 December 2010 through 17 December 2010
ER -