Title:
Faster Conditional Gradient Algorithms for Machine Learning

dc.contributor.advisor Pokutta, Sebastian
dc.contributor.author Carderera De Diego, Alejandro Agustin
dc.contributor.committeeMember Gupta, Swati
dc.contributor.committeeMember Lan, Guanghui
dc.contributor.committeeMember d'Aspremont, Alexandre
dc.contributor.committeeMember Diakonikolas, Jelena
dc.contributor.department Industrial and Systems Engineering
dc.date.accessioned 2022-01-14T16:09:59Z
dc.date.available 2022-01-14T16:09:59Z
dc.date.created 2021-12
dc.date.issued 2021-12-09
dc.date.submitted December 2021
dc.date.updated 2022-01-14T16:09:59Z
dc.description.abstract In this thesis, we focus on Frank-Wolfe (a.k.a. Conditional Gradient) algorithms, a family of iterative algorithms for convex optimization, that work under the assumption that projections onto the feasible region are prohibitive, but linear optimization problems can be efficiently solved over the feasible region. We present several algorithms that either locally or globally improve upon existing convergence guarantees. In Chapters 2-4 we focus on the case where the objective function is smooth and strongly convex and the feasible region is a polytope, and in Chapter 5 we focus on the case where the function is generalized self-concordant and the feasible region is a compact convex set.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/66117
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Convex optimization
dc.subject Optimization
dc.subject Machine learning
dc.subject
dc.title Faster Conditional Gradient Algorithms for Machine Learning
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename College of Engineering
relation.isOrgUnitOfPublication 29ad75f0-242d-49a7-9b3d-0ac88893323c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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