Mathematical Optimization for Machine Learning
Proceedings of the MATH+ Thematic Einstein Semester 2023, De Gruyter Proceedings in Mathematics
Konstantin Fackeldey/Aswin Kannan/Sebastian Pokutta et al
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Zusatztext
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.
Autorenportrait
M. Weiser, S. Pokutta, K. Sharma, ZIB, Germany; K. Fackeldey, TU Berlin; A. Kannan, D. Walter, A. Walther, Humboldt-Univ. Germany.
Weitere Details
Erschienen: 06.05.2025
Umfang: X, 202 S., 2 s/w Illustr., 53 farbige Illustr., 27
Sprache: ENG
Einband: GEB
ISBN/EAN: 9783111375854
Umbreit-Nr.: 5113609
