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dc.contributor.advisorDimitri P. Bertsekas.en_US
dc.contributor.authorNediÄ , Angeliaen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2005-05-19T14:59:52Z
dc.date.available2005-05-19T14:59:52Z
dc.date.copyright2002en_US
dc.date.issued2002en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/16843
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.en_US
dc.descriptionIncludes bibliographical references (p. 169-174).en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.description.abstractMany optimization problems arising in various applications require minimization of an objective cost function that is convex but not differentiable. Such a minimization arises, for example, in model construction, system identification, neural networks, pattern classification, and various assignment, scheduling, and allocation problems. To solve convex but not differentiable problems, we have to employ special methods that can work in the absence of differentiability, while taking the advantage of convexity and possibly other special structures that our minimization problem may possess. In this thesis, we propose and analyze some new methods that can solve convex (not necessarily differentiable) problems. In particular, we consider two classes of methods: incremental and variable metric.en_US
dc.description.statementofresponsibilityby Angelia Nedić.en_US
dc.format.extent174 p.en_US
dc.format.extent1040674 bytes
dc.format.extent1040074 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleSubgradient methods for convex minimizationen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc51441857en_US


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