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Alternating direction method of multipliers for machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Alternating direction method of multipliers for machine learningby Zhouchen Lin, Huan Li, Cong Fang.
Author:	Lin, Zhouchen.
other author:	Li, Huan.
Published:	Singapore :Springer Nature Singapore :2022.
Description:	xxiii, 263 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learningMathematics.
Online resource:	https://doi.org/10.1007/978-981-16-9840-8
ISBN:	9789811698408$q(electronic bk.)

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. - Singapore :Springer Nature Singapore :2022. - xxiii, 263 p. :ill., digital ;24 cm.

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$q(electronic bk.)

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

857106
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .L55 2022

Dewey Class. No.: 006.310151

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505 0 $a 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.
520 $a 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.
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