Alternating Direction Method of Multipliers for Machine Learning

Lin, Zhouchen; Li, Huan; Fang, Cong

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.

Details

Published by: Springer

Publication Date: 2022-06-16

Format: Hardcover

ISBN-13: 9789811698392

DOI: 10.1007/978-981-16-9840-8

Dimensions: 235.0cm x155.0cm

Pages: 263.0