Primal recovery from consensus-based dual decomposition for distributed convex optimization

A Simonetto, H Jamali Rad

Research output: Contribution to journalArticleScientificpeer-review

35 Citations (Scopus)


Dual decomposition has been successfully employed in a variety of distributed convex optimization problems solved by a network of computing and communicating nodes. Often, when the cost function is separable but the constraints are coupled, the dual decomposition scheme involves local parallel subgradient calculations and a global subgradient update performed by a master node. In this paper, we propose a consensus-based dual decomposition to remove the need for such a master node and still enable the computing nodes to generate an approximate dual solution for the underlying convex optimization problem. In addition, we provide a primal recovery mechanism to allow the nodes to have access to approximate near-optimal primal solutions. Our scheme is based on a constant stepsize choice, and the dual and primal objective convergence are achieved up to a bounded error floor dependent on the stepsize and on the number of consensus steps among the nodes.
Original languageEnglish
Pages (from-to)172-197
Number of pages26
JournalJournal of Optimization Theory and Applications
Issue number1
Publication statusPublished - 2016

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