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Large-Scale Optimization With Machine Learning Applications
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0002-2450-5367
2019 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

This thesis aims at developing efficient algorithms for solving some fundamental engineering problems in data science and machine learning. We investigate a variety of acceleration techniques for improving the convergence times of optimization algorithms.  First, we investigate how problem structure can be exploited to accelerate the solution of highly structured problems such as generalized eigenvalue and elastic net regression. We then consider Anderson acceleration, a generic and parameter-free extrapolation scheme, and show how it can be adapted to accelerate practical convergence of proximal gradient methods for a broad class of non-smooth problems. For all the methods developed in this thesis, we design novel algorithms, perform mathematical analysis of convergence rates, and conduct practical experiments on real-world data sets.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. , p. 122
Series
TRITA-EECS-AVL ; 2019:80
Keywords [en]
Optimization algorithms, Anderson acceleration, finite-sum, first-order methods
National Category
Control Engineering
Research subject
Applied and Computational Mathematics, Optimization and Systems Theory; Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-263147ISBN: 978-91-7873-358-3 (print)OAI: oai:DiVA.org:kth-263147DiVA, id: diva2:1367354
Presentation
2019-11-26, D2, Lindstedtsvägen 5, D-huset, Kungliga Tekniska högskolan, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20191105

Available from: 2019-11-05 Created: 2019-11-03 Last updated: 2019-11-05Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf