TY - GEN
T1 - Cloud-based centralized/decentralized multi-agent optimization with communication delays
AU - Hale, Matthew T.
AU - Nedic, Angelia
AU - Egerstedt, Magnus
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/8
Y1 - 2015/2/8
N2 - We present and analyze a hybrid computational architecture for performing multi-agent optimization. The optimization problems under consideration have convex objective and constraint functions with mild smoothness conditions imposed on them. For such problems, we provide a primal-dual algorithm implemented in the hybrid architecture, which consists of a decentralized network of agents into which an updated dual vector is occasionally injected, and we establish its convergence properties. In this setting, a central cloud computer is responsible for aggregating information, computing dual variable updates, and distributing these updates to the agents. The agents update their (primal) state variables and also communicate among themselves with each agent sharing and receiving state information with some number of its neighbors. Throughout, communications with the cloud are not assumed to be synchronous or instantaneous, and communication delays are explicitly accounted for in the modeling and analysis of the system. Experimental results for a team of robots are presented to support the theoretical developments made.
AB - We present and analyze a hybrid computational architecture for performing multi-agent optimization. The optimization problems under consideration have convex objective and constraint functions with mild smoothness conditions imposed on them. For such problems, we provide a primal-dual algorithm implemented in the hybrid architecture, which consists of a decentralized network of agents into which an updated dual vector is occasionally injected, and we establish its convergence properties. In this setting, a central cloud computer is responsible for aggregating information, computing dual variable updates, and distributing these updates to the agents. The agents update their (primal) state variables and also communicate among themselves with each agent sharing and receiving state information with some number of its neighbors. Throughout, communications with the cloud are not assumed to be synchronous or instantaneous, and communication delays are explicitly accounted for in the modeling and analysis of the system. Experimental results for a team of robots are presented to support the theoretical developments made.
UR - http://www.scopus.com/inward/record.url?scp=84962007191&partnerID=8YFLogxK
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U2 - 10.1109/CDC.2015.7402311
DO - 10.1109/CDC.2015.7402311
M3 - Conference contribution
AN - SCOPUS:84962007191
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 700
EP - 705
BT - 54rd IEEE Conference on Decision and Control,CDC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th IEEE Conference on Decision and Control, CDC 2015
Y2 - 15 December 2015 through 18 December 2015
ER -