Sparse approximations, iterative methods, and faster algorithms for matrices and graphs
Author(s)
Cohen, Michael Benjamin
DownloadFull printable version (29.62Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Aleksander Ma̧dry
Terms of use
Metadata
Show full item recordAbstract
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
2018Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.