Wow! eBook
Alternating Direction Method of Multipliers for Machine Learning 1st ed. 2022 Edition
Author(s): Zhouchen Lin (Author), Huan Li (Author), Cong Fang (Author)
- Publisher: Springer
- Publication Date: 17 Jun. 2023
- Edition: 1st ed. 2022
- Language: English
- Print length: 286 pages
- ISBN-10: 9811698422
- ISBN-13: 9789811698422
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 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.
