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Mathematical Optimization for Machine Learning

Proceedings of the MATH+ Thematic Einstein Semester 2023

dc.contributor.editorWeiser, Martin
dc.contributor.editorKannan, Aswin
dc.contributor.editorPokutta, Sebastian
dc.contributor.editorSharma, Kartikey
dc.contributor.editorWalter, Daniel
dc.contributor.editorWalther, Andrea
dc.contributor.editorFackeldey, Konstantin
dc.date.accessioned2025-11-24T12:27:20Z
dc.date.available2025-11-24T12:27:20Z
dc.date.issued2025
dc.date.submitted2025-08-22T10:09:07Z
dc.identifierONIX_20250822T115951_9783111376776_51
dc.identifier2942-4801
dc.identifierhttps://library.oapen.org/handle/20.500.12657/105686
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/204839
dc.description.abstractMathematical optimization and machine learning are closely related. This proceedings volume of the Thematic Einstein Semester 2023 of the Berlin Mathematics Research Center MATH+ collects recent progress on their interplay in topics such as discrete optimization, nonlinear programming, optimal control, first-order methods, multilevel optimization, machine learning in optimization, physics-informed learning, and fairness in machine learning.
dc.languageEnglish
dc.relation.ispartofseriesDe Gruyter Proceedings in Mathematics
dc.rightsopen access
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PB Mathematics::PBU Optimization
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PH Physics::PHU Mathematical physics
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
dc.subject.otherMathematical optimization
dc.subject.otherMachine learning
dc.subject.otherNonlinear optimization
dc.subject.otherDiscrete optimization
dc.subject.otherPhysics informed learning
dc.titleMathematical Optimization for Machine Learning
dc.title.alternativeProceedings of the MATH+ Thematic Einstein Semester 2023
dc.typebook
oapen.identifier.doi10.1515/9783111376776
oapen.relation.isPublishedByaf2fbfcc-ee87-43d8-a035-afb9d7eef6a5
oapen.relation.isbn9783111376776
oapen.relation.isbn9783111375854
oapen.relation.isbn9783111377742
oapen.imprintDe Gruyter
oapen.pages202
oapen.place.publicationBerlin/Boston


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