Alternating Direction Method of Multipliers for Machine Learning

Machine learning heavily relies on optimization algorithms, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems. This book provides a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization, and is an excellent reference book for users seeking a universal algorithm for constrained problems.

Format: Paperback / softback
Length: 263 pages
Publication date: 17 June 2023
Publisher: Springer Verlag, Singapore

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.

Dimension: 235 x 155 (mm)
ISBN-13: 9789811698422
Edition number: 1st ed. 2022