Mathematical Optimization for Machine Learning

Proceedings of the MATH+ Thematic Einstein Semester 2023

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https://oapen-dev.siscern.org/bitstream/20.500.12657/105686/1/9783111376776.pdf
Contributor(s)
Weiser, Martin (editor)
Kannan, Aswin (editor)
Pokutta, Sebastian (editor)
Sharma, Kartikey (editor)
Walter, Daniel (editor)
Walther, Andrea (editor)
Fackeldey, Konstantin (editor)
Language
English
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Résumé
Mathematical 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.
URI
https://doab-dev.siscern.org/handle/20.500.12854/204839
Keywords
Mathematical optimization; Machine learning; Nonlinear optimization; Discrete optimization; Physics informed learning
DOI
10.1515/9783111376776
ISBN
9783111376776, 9783111375854, 9783111377742
Publisher
De Gruyter
Publisher website
http://www.degruyter.com/
Publication date and place
Berlin/Boston, 2025
Imprint
De Gruyter
Series
De Gruyter Proceedings in Mathematics,
Classification
Optimization
Applied mathematics
Mathematical physics
Machine learning
Pages
202
Rights
https://creativecommons.org/licenses/by-nd/4.0/
open access
Excepté là où spécifié autrement, la license de ce document est décrite en tant que open access