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.

Info autore

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

Dettagli sul prodotto

• Con la collaborazione di: Konstantin Fackeldey (Editore), Aswin Kannan (Editore), Sebastian Pokutta (Editore), Sebastian Pokutta et al (Editore), Kartikey Sharma (Editore), Daniel Walter (Editore), Andrea Walther (Editore), Martin Weiser (Editore)
• Editore: De Gruyter
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 26.05.2025
• EAN: 9783111375854
• ISBN: 978-3-11-137585-4
• Pagine: 202
• Peso: 485 g
• Illustrazioni: 2 b/w and 53 col. ill., 27 b/w tbl.
• Serie: De Gruyter Proceedings in Mathematics
• Categorie: Scienze naturali, medicina, informatica, tecnica > Matematica; 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