Fully Distributed Algorithms for Convex Optimization Problems
Abstract
We design and analyze a fully distributed algorithm for convex constrained optimization in networks without any consistent naming infrastructure. The algorithm produces an approximately feasible and near-optimal solution in time polynomial in the network size, the inverse of the permitted error, and a measure of curvature variation in the dual optimization problem. It blends, in a novel way, gossip-based information spreading, iterative gradient ascent, and the barrier method from the design of interior-point algorithms.
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Copyright © 2010 Society for Industrial and Applied Mathematics.
History
Submitted: 15 December 2008
Accepted: 5 July 2010
Published online: 28 October 2010
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