Cloud-based centralized/decentralized multi-agent optimization with communication delays

Matthew T. Hale, Angelia Nedich, Magnus Egerstedt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations


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.

Original languageEnglish (US)
Title of host publication2015 54th IEEE Conference on Decision and Control, CDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479978861
StatePublished - Feb 8 2016
Externally publishedYes
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: Dec 15 2015Dec 18 2015


Other54th IEEE Conference on Decision and Control, CDC 2015

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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