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Mathematical Optimization for Machine Learning: Proceedings of the MATHplus Thematic Einstein Semester 2023 [Kõva köide]

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  • Formaat: Hardback, 212 pages, kõrgus x laius: 240x170 mm, kaal: 485 g, 53 Illustrations, color; 27 Tables, black and white; 2 Illustrations, black and white
  • Sari: De Gruyter Proceedings in Mathematics
  • Ilmumisaeg: 06-May-2025
  • Kirjastus: De Gruyter
  • ISBN-10: 3111375854
  • ISBN-13: 9783111375854
Teised raamatud teemal:
Mathematical Optimization for Machine Learning: Proceedings of the MATHplus Thematic Einstein Semester 2023Suurem pilt
  • Formaat: Hardback, 212 pages, kõrgus x laius: 240x170 mm, kaal: 485 g, 53 Illustrations, color; 27 Tables, black and white; 2 Illustrations, black and white
  • Sari: De Gruyter Proceedings in Mathematics
  • Ilmumisaeg: 06-May-2025
  • Kirjastus: De Gruyter
  • ISBN-10: 3111375854
  • ISBN-13: 9783111375854
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783111375854.html
Märksõnad:

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.

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