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. © 2010 Society for Industrial and Applied Mathematics.
CITATION STYLE
Mosk-Aoyama, D., Roughgarden, T., & Shah, D. (2010). Fully distributed algorithms for convex optimization problems. SIAM Journal on Optimization, 20(6), 3260–3279. https://doi.org/10.1137/080743706
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