Sparse approximations, iterative methods, and faster algorithms for matrices and graphs
Name
1076272705-MIT.pdf
Description
Full printable version
Size
29.62 MB
Format
Adobe PDF
Checksum (MD5)
ba6ef6c7a68b00845f7996219c505e1f
Author(s)
Cohen, Michael Benjamin
Advisor(s)
Aleksander Ma̧dry
Date Issued
2018
Publisher
Massachusetts Institute of Technology
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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