Distributed constrained optimization over noisy networks

Kunal Srivastava, Angelia Nedić, Dušan M. Stipanović

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

21 Scopus citations


In this paper we deal with two problems which are of great interest in the field of distributed decision making and control. The first problem we tackle is the problem of achieving consensus on a vector of local decision variables in a network of computational agents when the decision variables of each node are constrained to lie in a subset of the Euclidean space. We assume that the constraint sets for the local variables are private information for each node. We provide a distributed algorithm for the case when there is communication noise present in the network. We show that we can achieve almost sure convergence under certain assumptions. The second problem we discuss is the problem of distributed constrained optimization when the constraint sets are distributed over the agents. Furthermore our model incorporates the presence of noisy communication links and the presence of stochastic errors in the evaluation of subgradients of the local objective function. We establish sufficient conditions and provide an analysis guaranteeing the convergence of the algorithm to the optimal set with probability one.

Original languageEnglish (US)
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781424477456
StatePublished - 2010
Externally publishedYes
Event49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, United States
Duration: Dec 15 2010Dec 17 2010

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference49th IEEE Conference on Decision and Control, CDC 2010
Country/TerritoryUnited States

ASJC Scopus subject areas

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


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