Alternating Direction Method of Multipliers for Machine Learning (Hardcover, 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 2022
First published:	2022
Authors:	Zhouchen Lin • Huan Li • Cong Fang
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	263
Edition:	1st ed. 2022
ISBN-13:	978-981-16-9839-2
Categories:	Books > Science & Mathematics > Mathematics > Numerical analysis Books > Science & Mathematics > Mathematics > Optimization > General Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	981-16-9839-2
Barcode:	9789811698392

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