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Alternating Direction Method of Multipliers for Machine Learning

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Alternating Direction Method of Multipliers for Machine Learning/ by Zhouchen Lin, Huan Li, Cong Fang.
作者:	Lin, Zhouchen.
其他作者:	Fang, Cong.
面頁冊數:	XXIII, 263 p. 1 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational Mathematics and Numerical Analysis. -
電子資源:	https://doi.org/10.1007/978-981-16-9840-8
ISBN:	9789811698408

Alternating Direction Method of Multipliers for Machine Learning
Lin, Zhouchen.

Alternating Direction Method of Multipliers for Machine Learning[electronic resource] /by Zhouchen Lin, Huan Li, Cong Fang. - 1st ed. 2022. - XXIII, 263 p. 1 illus.online resource.

Chapter 1. Introduction -- Chapter 2. Derivations of ADMM -- Chapter 3. ADMM for Deterministic and Convex Optimization -- Chapter 4. ADMM for Nonconvex Optimization -- Chapter 5. ADMM for Stochastic Optimization -- Chapter 6. ADMM for Distributed Optimization -- Chapter 7. Practical Issues and Conclusions.

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.

ISBN: 9789811698408

Standard No.: 10.1007/978-981-16-9840-8doiSubjects--Topical Terms:

669338
Computational Mathematics and Numerical Analysis.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Alternating Direction Method of Multipliers for Machine Learning
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