Alternating Direction Method Of Multipliers For Machine Learning

Springer
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9789811698392
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ISBN13:
9789811698392
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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.


  • | Author: Zhouchen Lin|Huan Li|Cong Fang
  • | Publisher: Springer
  • | Publication Date: Jun 29, 2022
  • | Number of Pages: 286 pages
  • | Language: English
  • | Binding: Hardcover/Computers
  • | ISBN-10: 9811698392
  • | ISBN-13: 9789811698392
Author:
Zhouchen Lin, Huan Li, Cong Fang
Publisher:
Springer
Publication Date:
Jun 29, 2022
Number of pages:
286 pages
Language:
English
Binding:
Hardcover/Computers
ISBN-10:
9811698392
ISBN-13:
9789811698392