Alternating Direction Method of Multipliers for Machine Learning

Alternating Direction Method of Multipliers for Machine Learning 1st ed. 2022 Edition
Author: Zhouchen Lin,Huan Li,Cong Fang (Author)
Publisher Finelybook 出版社:Springer; 1st ed. 2022 edition (June 16, 2022)
Language 语言:English
pages 页数:286 pages
ISBN-10 书号:9811698392
ISBN-13 书号:9789811698392

Book Description
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 Author: 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.

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