2026-09-19T05:55:19Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1203762026-06-17T14:44:31Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Piotr Indyk.
Backurs, Arturs
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-02-14T15:22:29Z
2019-02-14T15:22:29Z
2018
2018
http://hdl.handle.net/1721.1/120376
1084284848
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 145-156).
The theory of NP-hardness has been remarkably successful in identifying problems that are unlikely to be solvable in polynomial time. However, many other important problems do have polynomial-time algorithms, but large exponents in their runtime bounds can make them inefficient in practice. For example, quadratic-time algorithms, although practical on moderately sized inputs, can become inefficient on big data problems that involve gigabytes or more of data. Although for many data analysis problems no sub-quadratic time algorithms are known, any evidence of quadratic-time hardness has remained elusive. In this thesis we present hardness results for several text analysis and machine learning tasks: ** Lower bounds for edit distance, regular expression matching and other pattern matching and string processing problems. ** Lower bounds for empirical risk minimization such as kernel support vectors machines and other kernel machine learning problems. All of these problems have polynomial time algorithms, but despite extensive amount of research, no near-linear time algorithms have been found. We show that, under a natural complexity-theoretic conjecture, such algorithms do not exist. We also show how these lower bounds have inspired the development of efficient algorithms for some variants of these problems.
by Arturs Backurs.
Ph.D.
156 pages
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
Below P vs NP : fine-grained hardness for big data problems
Fine-grained hardness for big data problems
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="90b164ae-fe05-48b1-bc3a-a34638415ecb">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Below P vs NP : fine-grained hardness for big data problems</Title>
<Subtitle>Fine-grained hardness for big data problems</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2018</PublicationDate>
<Authors>
<Author>
<DisplayName>Backurs, Arturs</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>The theory of NP-hardness has been remarkably successful in identifying problems that are unlikely to be solvable in polynomial time. However, many other important problems do have polynomial-time algorithms, but large exponents in their runtime bounds can make them inefficient in practice. For example, quadratic-time algorithms, although practical on moderately sized inputs, can become inefficient on big data problems that involve gigabytes or more of data. Although for many data analysis problems no sub-quadratic time algorithms are known, any evidence of quadratic-time hardness has remained elusive. In this thesis we present hardness results for several text analysis and machine learning tasks: ** Lower bounds for edit distance, regular expression matching and other pattern matching and string processing problems. ** Lower bounds for empirical risk minimization such as kernel support vectors machines and other kernel machine learning problems. All of these problems have polynomial time algorithms, but despite extensive amount of research, no near-linear time algorithms have been found. We show that, under a natural complexity-theoretic conjecture, such algorithms do not exist. We also show how these lower bounds have inspired the development of efficient algorithms for some variants of these problems.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>