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dc.contributor.advisorAleksander Ma̧dryen_US
dc.contributor.authorCohen, Michael Benjaminen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-12-11T21:07:58Z
dc.date.available2018-12-11T21:07:58Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/119599
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 435-460).en_US
dc.description.abstractThis thesis aims to advance our algorithmic understanding of some of the most fundamental objects in computer science: graphs and matrices. Specifically, on one hand, we develop a broad set of sampling techniques that yield better (sparser) approximations of these objects and do so more efficiently. On the other hand, we provide faster algorithms for a host of core problems in numerical linear algebra and graph algorithms. The resulting insights often lead to first in decades progress on the studied problems.en_US
dc.description.statementofresponsibilityby Michael Benjamin Cohen.en_US
dc.format.extent460 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleSparse approximations, iterative methods, and faster algorithms for matrices and graphsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc1076272705en_US


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