Mathematical Optimization for Machine Learning : Proceedings of the MATH+ Thematic Einstein Semester 2023
(eBook)
Contributors
Baumgärtner, Lukas, Contributor
Beecroft, Damien, Contributor
Bethke, Franz, Contributor
Caboussat, Alexandre, Contributor
Carlucci, Antonio, Contributor
Beecroft, Damien, Contributor
Bethke, Franz, Contributor
Caboussat, Alexandre, Contributor
Carlucci, Antonio, Contributor
Published
Berlin ; De Gruyter,, [2025].
Format
eBook
ISBN
9783111376776
Status
Description
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Language
English
Notes
Restrictions on Access
Open Access https://purl.org/coar/access_right/c_abf2 d star
Description
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.
Additional Physical Form
Issued also in print.
System Details
Mode of access: Internet via World Wide Web.
Terms Governing Use and Reproduction
This eBook is made available Open Access under a CC BY-ND 4.0 license: https://creativecommons.org/licenses/by-nd/4.0
Language
In English.