Distributed multi-agent convex optimization over random digraphs

S. Sh Alaviani, N. Elia

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

5 Scopus citations


In this paper, we consider an unconstrained collaborative optimization of a sum of convex functions where agents make decisions using local information in the presence of random communication topologies. We recast the problem as a minimization of the sum of convex functions over a constraint set defined as the set of fixed value points of a random operator derived from weighted matrix of a random graph. This formulation does not need the weighted matrix of the graph to be independent and identically distributed. We define a novel optimization problem which includes the formulated distributed optimization problem as a special case. We propose a discrete algorithm for converging in mean square to the solution of the optimization problem under suitable assumptions. Numerical examples illustrate the convergence of the proposed algorithms.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509059928
StatePublished - Jun 29 2017
Externally publishedYes
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2017 American Control Conference, ACC 2017
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This work was supported by National Science Foundation under Grant CCF-1320643 and Grant CNS-1239319.

Publisher Copyright:
© 2017 American Automatic Control Council (AACC).


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