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Mathematical Optimization for Machine Learning:Proceedings of the MATH+ Thematic Einstein Semester 2023 (De Gruyter Proceedings in Mathematics)
by: Konstantin Fackeldey (Editor), Aswin Kannan (Editor), Sebastian Pokutta (Editor), Kartikey Sharma (Editor), Daniel Walter (Editor), Andrea Walther (Editor), Martin Weiser (Editor)
Publisher: De Gruyter
Edition: 1st
Publication Date: 2025-05-06
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
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.
