Asynchronous Multiagent Primal-Dual Optimization

Matthew T. Hale, Angelia Nedich, Magnus Egerstedt

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


We present a framework for asynchronously solving convex optimization problems over networks of agents which are augmented by the presence of a centralized cloud computer. This framework uses a Tikhonov-regularized primal-dual approach in which the agents update the system's primal variables and the cloud updates its dual variables. To minimize coordination requirements placed upon the system, the times of communications and computations among the agents are allowed to be arbitrary, provided they satisfy mild conditions. Communications from the agents to the cloud are likewise carried out without any coordination in their timing. However, we require that the cloud keeps the dual variable's value synchronized across the agents, and a counterexample is provided that demonstrates that this level of synchrony is indeed necessary for convergence. Convergence rate estimates are provided in both the primal and dual spaces, and simulation results are presented that demonstrate the operation and convergence of the proposed algorithm.

Original languageEnglish (US)
Article number7837612
Pages (from-to)4421-4435
Number of pages15
JournalIEEE Transactions on Automatic Control
Issue number9
StatePublished - Sep 2017


  • Distributed algorithms
  • Optimization
  • multi-agent systems
  • networked control systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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