• Anglický jazyk

Alternating Direction Method of Multipliers for Machine Learning

Autor: Zhouchen Lin

Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve... Viac o knihe

Na objednávku, dodanie 2-4 týždne

138.59 €

bežná cena: 153.99 €

O knihe

Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time.

  • Vydavateľstvo: Springer Nature Singapore
  • Rok vydania: 2023
  • Formát: Paperback
  • Rozmer: 235 x 155 mm
  • Jazyk: Anglický jazyk
  • ISBN: 9789811698422

Generuje redakčný systém BUXUS CMS spoločnosti ui42.