Mathematical Optimization for Machine Learning - Proceedings of the MATH+ Thematic Einstein Semester 2023

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.

About the author

M. Weiser, S. Pokutta, K. Sharma, ZIB, Germany; K. Fackeldey, TU Berlin; A. Kannan, D. Walter, A. Walther, Humboldt-Univ. Germany.

Product details

• Assisted by: Konstantin Fackeldey (Editor), Aswin Kannan (Editor), Sebastian Pokutta (Editor), Sebastian Pokutta et al (Editor), Kartikey Sharma (Editor), Daniel Walter (Editor), Andrea Walther (Editor), Martin Weiser (Editor)
• Publisher: De Gruyter
• Languages: English
• Product format: Hardback
• Released: 26.05.2025
• EAN: 9783111375854
• ISBN: 978-3-11-137585-4
• No. of pages: 202
• Weight: 485 g
• Illustrations: 2 b/w and 53 col. ill., 27 b/w tbl.
• Series: De Gruyter Proceedings in Mathematics
• Subjects: Natural sciences, medicine, IT, technology > Mathematics; machine learning, Maschinelles Lernen, Optimization, Artificial Intelligence, Mathematische Physik, Angewandte Mathematik, Mathematical physics, Applied mathematics, Mathematical optimization, Discrete Optimization, nichtlineare Optimierung, Mathematische Optimierung, MAT003000 MATHEMATICS / Applied, COM004000 COMPUTERS / Intelligence (AI) & Semantics, COM094000 COMPUTERS / Data Science / Machine Learning, nonlinear optimization, Physics informed learning, Diskrete Optimierung, Physik-informiertes Lernen