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dc.contributor.authorAkbari Varnousfaderani, Mohammad Jr
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2015-08-31 09:59:41.261en
dc.date.accessioned2015-09-01T20:19:44Z
dc.date.available2015-09-01T20:19:44Z
dc.date.issued2015-09-01
dc.identifier.urihttp://hdl.handle.net/1974/13551
dc.descriptionThesis (Master, Mathematics & Statistics) -- Queen's University, 2015-08-31 09:59:41.261en
dc.description.abstractThis thesis introduces two classes of discrete-time distributed online optimization algorithms, with a group of agents which communicate over a network. At each time, a private convex objective function is revealed to each agent. In the next time step, each agent updates its state using its own objective function and the information gathered from its immediate in-neighbours at that time. We design algorithms distributed over the network topologies, which guarantee that the individual regret, the difference between the network cost incurred by the agent’s states estimation and the cost incurred by the best fixed choice, grows only sublinearly. One algorithm is based on gradient-flow, which provably works for a sequence of time-varying uniformly strongly connected graphs. The other one is based on Alternating Direction Method of Multipliers, which works on fixed undirected graphs and gives an explicit regret bound in terms of the size of the network. We implement the proposed algorithms on a sensor network and the results demonstrate the good performance for both algorithms.en_US
dc.languageenen
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectMulti-agent Systemsen_US
dc.subjectSensor Networksen_US
dc.subjectDistributed Optimizationen_US
dc.subjectSubgradient Algorithmen_US
dc.subjectMethod of Multipliersen_US
dc.subjectOnline Optimizationen_US
dc.titleDistributed Online Optimization on time-varying networksen_US
dc.typeThesisen_US
dc.description.degreeMasteren
dc.contributor.supervisorGharesifard, Bahmanen
dc.contributor.supervisorLinder, Tamásen
dc.contributor.departmentMathematics and Statisticsen


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