Exploiting Locality and Structure for Distributed Optimization in Multi-Agent Systems

Robin Brown, Federico Rossi, Kiril Solovey, Michael T. Wolf, Marco Pavone

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


A number of prototypical optimization problems in multi-agent systems (e.g. task allocation and network load-sharing) exhibit a highly local structure: that is, each agent's decision variables are only directly coupled to few other agent's variables through the objective function or the constraints. Nevertheless, existing algorithms for distributed optimization generally do not exploit the locality structure of the problem, requiring all agents to compute or exchange the full set of decision variables. In this paper, we develop a rigorous notion of "locality" that relates the structural properties of a linearly- constrained convex optimization problem (in particular, the sparsity structure of the constraint matrix and the objective function) to the amount of information that agents should exchange to compute an arbitrarily high-quality approximation to the problem from a cold-start. We leverage the notion of locality to develop a locality-aware distributed optimization algorithm, and we show that, for problems where individual agents only require to know a small portion of the optimal solution, the algorithm requires very limited inter-agent communication. Numerical results show that the convergence rate of our algorithm is directly explained by the locality metric proposed, and that the proposed theoretical bounds are remarkably tight; comparison to the projected sub-gradient algorithm shows that our locality-aware algorithm requires orders of magnitude fewer communication rounds to achieve similar solution quality.

Original languageEnglish
Title of host publicationEuropean Control Conference 2020, ECC 2020
Number of pages8
ISBN (Electronic)9783907144015
StatePublished - May 2020
Externally publishedYes
Event18th European Control Conference, ECC 2020 - Saint Petersburg, Russian Federation
Duration: 12 May 202015 May 2020

Publication series

NameEuropean Control Conference 2020, ECC 2020


Conference18th European Control Conference, ECC 2020
Country/TerritoryRussian Federation
CitySaint Petersburg

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Systems Engineering
  • Mechanical Engineering
  • Computational Mathematics
  • Control and Optimization


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