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. Mathematical optimization often focuses on accuracy, computational efficiency, and robustness while Machine Learning (ML) aims to achieve effective results on real data sets, in particular concentrating on generalization, robustness, and resilience (to, e.g., perturbations of the inputs).

Mathematical Optimization for Machine Learning: Proceedings of the MATH+ Thematic Einstein Semester 2023 (De Gruyter Proceedings in Mathematics) PDF

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