Fully Distributed Algorithms for Convex Optimization Problems

Authors:
Damon Mosk-Aoyama;Tim Roughgarden;Devavrat Shah
Affiliations:
damonma@cs.stanford.edu and tim@cs.stanford.edu;-;devavrat@mit.edu
Venue:
SIAM Journal on Optimization
Year:
2010

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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.