Primal Recovery from Consensus-Based Dual Decomposition for Distributed Convex Optimization
Abstract
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.
Cite
@article{arxiv.1503.07678,
title = {Primal Recovery from Consensus-Based Dual Decomposition for Distributed Convex Optimization},
author = {Andrea Simonetto and Hadi Jamali-Rad},
journal= {arXiv preprint arXiv:1503.07678},
year = {2017}
}