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Sparse approximations, iterative methods, and faster algorithms for matrices and graphs

Author(s)
Cohen, Michael Benjamin
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Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Aleksander Ma̧dry
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MIT 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. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
This 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.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 435-460).
 
Date issued
2018
URI
http://hdl.handle.net/1721.1/119599
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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