Alternating direction method of multipliers for machine learning / Zhouchen Lin, Huan Li, Cong Fang.
Lin, Zhouchen.; Li, Huan.; Fang, Cong.
2022
Q325.5
Available Online

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Title Alternating direction method of multipliers for machine learning / Zhouchen Lin, Huan Li, Cong Fang.
Author Lin, Zhouchen.
ISBN 9789811698408 (electronic bk.)
9811698406 (electronic bk.)
9811698392
9789811698392
DOI https://doi.org/10.1007/978-981-16-9840-8
Publication Details Singapore : Springer, 2022.
Language English
Description 1 online resource (274 pages)
Item Number 10.1007/978-981-16-9840-8 doi
Call Number Q325.5
Dewey Decimal Classification 006.3/10151
Summary 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.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed June 29, 2022).
Added Author Li, Huan.
Fang, Cong.
Available in Other Form Alternating Direction Method of Multipliers for Machine Learning.
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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.

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