Cloud-Based Centralized/Decentralized Multi-Agent Optimization with Communication Delays

View/ Open
Date
2015-12Author
Hale, Matthew T.
Nedić, Angelia
Egerstedt, Magnus B.
Metadata
Show full item recordAbstract
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 primaldual
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.