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{ "item_title" : "Mathematical Optimization for Machine Learning", "item_author" : [" Konstantin Fackeldey", "Aswin Kannan", "Sebastian Pokutta "], "item_description" : "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. ", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/11/137/585/3111375854_b.jpg", "price_data" : { "retail_price" : "175.99", "online_price" : "175.99", "our_price" : "175.99", "club_price" : "175.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Mathematical Optimization for Machine Learning|Konstantin Fackeldey

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

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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.

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Details

  • ISBN-13: 9783111375854
  • ISBN-10: 3111375854
  • Publisher: de Gruyter
  • Publish Date: May 2025
  • Dimensions: 9.61 x 6.69 x 0.5 inches
  • Shipping Weight: 1.19 pounds
  • Page Count: 212

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