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

by Konstantin Fackeldey (Editor), Aswin Kannan (Editor), Sebastian Pokutta (Editor), Kartikey Sharma (Editor), Daniel Walter (Editor)

Overview

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.

Product Details

ISBN-13:	9783111375854
Publisher:	De Gruyter
Publication date:	05/06/2025
Series:	De Gruyter Proceedings in Mathematics
Pages:	212
Product dimensions:	6.69(w) x 9.45(h) x (d)

About the Author

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