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Mathematical Optimization for Machine Learning: Proceedings of the MATH+ Thematic Einstein Semester 2023
Author(s): Konstantin Fackeldey (Editor), Aswin Kannan (Editor), Sebastian Pokutta (Editor), Kartikey Sharma (Editor), Daniel Walter (Editor), Andrea Walther (Editor), Martin Weiser (Editor)
- Publisher: De Gruyter
- Publication Date: May 6, 2025
- Edition: 1st
- Language: English
- Print length: 212 pages
- ISBN-10: 3111375854
- ISBN-13: 9783111375854
Book 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.
Editorial Reviews
About the Author
M. Weiser, S. Pokutta, K. Sharma, ZIB, Germany; K. Fackeldey, TU Berlin; A. Kannan, D. Walter, A. Walther, Humboldt-Univ. Germany.
