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dc.contributor.authorWei, Ermin
dc.contributor.authorZargham, Michael
dc.contributor.authorOzdaglar, Asuman E.
dc.contributor.authorJadbabaie, Ali
dc.date.accessioned2012-09-13T13:10:20Z
dc.date.available2012-09-13T13:10:20Z
dc.date.issued2011-12
dc.identifier.isbn978-1-61284-799-3
dc.identifier.isbn978-1-61284-800-6
dc.identifier.issn0743-1546
dc.identifier.urihttp://hdl.handle.net/1721.1/72677
dc.description.abstractThe existing distributed algorithms for Network Utility Maximization (NUM) problems mostly rely on dual decomposition and first-order (gradient or subgradient) methods, which suffer from slow rate of convergence. Recent works [17] and [18] proposed an alternative distributed Newton-type second-order algorithm for solving NUM problems with self-concordant utility functions. This algorithm is implemented in the primal space and involves for each primal iteration computing the dual variables using a finitely terminated iterative scheme obtained through novel matrix splitting techniques. These works presented a convergence rate analysis for the primal iterations and showed that if the error level in the Newton direction (resulting from finite termination of dual iterations) is below a certain threshold, then the algorithm achieves local quadratic convergence rate to an error neighborhood of the optimal solution. This paper builds on these works and presents a convergence rate analysis for the dual iterations that enables us to explicitly compute at each primal iteration the number of dual steps that can satisfy the error level. This yields for the first time a fully distributed second order method for NUM problems with local quadratic convergence guarantee. Simulation results demonstrate significant convergence rate improvement of our algorithm, even when only one dual update is implemented per primal iteration, relative to the existing first-order methods based on dual decomposition.en_US
dc.description.sponsorshipNational Science Foundation (U.S.). (Career) (Grant number DMI-0545910)en_US
dc.description.sponsorshipUnited States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative (R6756-G2)en_US
dc.description.sponsorshipUnited States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N0001408107474)en_US
dc.description.sponsorshipUnited States. Army Research Office. Multidisciplinary University Research Initiative. Scalableen_US
dc.description.sponsorshipUnited States. Air Force Office of Scientific Research. Complex Networks Programen_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/CDC.2011.6161134en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleOn Dual Convergence of the Distributed Newton Method for Network Utility Maximizationen_US
dc.typeArticleen_US
dc.identifier.citationWei, Ermin et al. “On Dual Convergence of the Distributed Newton Method for Network Utility Maximization.” 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), 2011. 6612–6617.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.approverOzdaglar, Asuman E.
dc.contributor.mitauthorWei, Ermin
dc.contributor.mitauthorOzdaglar, Asuman E.
dc.relation.journalProceedings on the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), 2011en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsWei, Ermin; Zargham, Michael; Ozdaglar, Asuman; Jadbabaie, Alien
dc.identifier.orcidhttps://orcid.org/0000-0002-1827-1285
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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