Alternating Direction Method of Multipliers for Machine Learning (1st ed. 2022)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Release date:	June 2023
First published:	2022
Authors:	Zhouchen Lin • Huan Li • Cong Fang
Dimensions:	235 x 155mm (L x W)
Pages:	263
Edition:	1st ed. 2022
ISBN-13:	978-981-16-9842-2
Categories:	Books
LSN:	981-16-9842-2
Barcode:	9789811698422

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