{"product_id":"mathematical-optimization-for-machine-learning-proceedings-of-the-math-thematic-einstein-semester-2023-9783111375854","title":"Mathematical Optimization for Machine Learning: Proceedings of the Math+ Thematic Einstein Semester 2023","description":"\u003cp\u003e • Author(s): Konstantin Fackeldey | Aswin Kannan | Sebastian Pokutta\u003cbr\u003e • Publisher: de Gruyter\u003cbr\u003e • Publisher Imprint: de Gruyter\u003cbr\u003e • BISAC: Optimization\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eMathematical 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. \u003c\/p\u003e","brand":"de Gruyter","offers":[{"title":"Hardcover","offer_id":47779315515543,"sku":"9783111375854","price":19016.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9783111375854.webp?v=1778035368","url":"https:\/\/atlanticbooks.com\/products\/mathematical-optimization-for-machine-learning-proceedings-of-the-math-thematic-einstein-semester-2023-9783111375854","provider":"Atlantic Books","version":"1.0","type":"link"}