Relax, descend and certify : optimization techniques for typically tractable data problems

Simple item page
dc.contributor.advisorWard, Rachel, 1983-
dc.contributor.committeeMemberBlumberg, Andrew J
dc.contributor.committeeMemberIsrael, Arie
dc.contributor.committeeMemberBandeira, Afonso S
dc.creatorVillar Lozano, Maria Soledad
dc.creator.orcid0000-0003-4968-3829
dc.date.accessioned2017-10-12T18:15:00Z
dc.date.available2017-10-12T18:15:00Z
dc.date.created2017-05
dc.date.issued2017-06-14
dc.date.submittedMay 2017
dc.date.updated2017-10-12T18:15:00Z
dc.description.abstractIn this thesis we explore different mathematical techniques for extracting information from data. In particular we focus in machine learning problems such as clustering and data cloud alignment. Both problems are intractable in the "worst case", but we show that convex relaxations can efficiently find the exact or almost exact solution for classes of "typical" instances. We study different roles that optimization techniques can play in understanding and processing data. These include efficient algorithms with mathematical guarantees, a posteriori methods for quality evaluation of solutions, and algorithmic relaxation of mathematical models. We develop probabilistic and data-driven techniques to model data and evaluate performance of algorithms for data problems.
dc.description.departmentMathematics
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T28S4K527
dc.identifier.urihttp://hdl.handle.net/2152/62104
dc.language.isoen
dc.subjectOptimization
dc.subjectData science
dc.titleRelax, descend and certify : optimization techniques for typically tractable data problems
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentMathematics
thesis.degree.disciplineMathematics
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
VILLARLOZANO-DISSERTATION-2017.pdf
Size:
3.88 MB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.46 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.85 KB
Format:
Plain Text
Description:
Download

Collections

UT Electronic Theses and Dissertations