Distributed nonconvex optimization: Algorithms and convergence analysis

dc.contributor.advisor Mingyi Hong
dc.contributor.advisor Gary Mirka
dc.contributor.author Hajinezhad, Davood
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2018-08-11T10:09:34.000
dc.date.accessioned 2020-06-30T03:08:59Z
dc.date.available 2020-06-30T03:08:59Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2017
dc.date.embargo 2001-01-01
dc.date.issued 2017-01-01
dc.description.abstract <p>This thesis addresses the problem of distributed optimization and learning over multi-agent networks. Our main focus is to design efficient algorithms for a class of nonconvex problems, defined over networks in which each agent/node only has partial knowledge about the entire problem. Multi-agent nonconvex optimization has gained much attention recently due to its wide applications in big data analysis, sensor networks, signal processing, multi-agent network, resource allocation, communication networks, just to name a few. In this work, we develop a general class of primal-dual algorithms for distributed optimization problems in challenging setups, such as nonconvexity in loss functions, nonsmooth regularizations, and coupling constraints. Further, we consider different setup where each agent can only access the zeroth-order information (i.e., the functional values) of its local functions. Rigorous convergence and rate of convergence analysis is provided for the proposed algorithms. Our work represents one of the first attempts to address nonconvex optimization and learning over networks.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/16140/
dc.identifier.articleid 7147
dc.identifier.contextkey 11456957
dc.identifier.doi https://doi.org/10.31274/etd-180810-5769
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/16140
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/30323
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/16140/Hajinezhad_iastate_0097E_17081.pdf|||Fri Jan 14 20:55:41 UTC 2022
dc.subject.disciplines Operational Research
dc.subject.keywords Convergence analysis
dc.subject.keywords Distributed optimization
dc.subject.keywords Nonconvex optimization
dc.subject.keywords Rate of convergence
dc.subject.keywords Zeroth-order optimization
dc.title Distributed nonconvex optimization: Algorithms and convergence analysis
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
thesis.degree.discipline Industrial and Manufacturing Systems Engineering
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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