FULLY DISTRIBUTED ALGORITHMS FOR CONVEX OPTIMIZATION PROBLEMS
Name
Mosk-Aoyama-2010-FULLY DISTRIBUTED ALGORITHMS FOR CONVEX OPTIMIZATION PROBLEMS.pdf
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247.08 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Mosk-Aoyama, Damon
Roughgarden, Tim
Shah, Devavrat
Date Issued
October 2010
Journal
SIAM Journal on Optimization
Publisher
Society for Industrial and Applied Mathematics (SIAM)
Citation
Mosk-Aoyama, Damon, Tim Roughgarden, and Devavrat Shah. “Fully Distributed Algorithms for Convex Optimization Problems.” SIAM Journal on Optimization 20.6 (2010) : 3260. © 2010 Society for Industrial and Applied Mathematics
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1137/080743706
