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Alternating Direction Method of Multipliers for Machine Learning / by Zhouchen Lin, Huan Li, Cong Fang.
- Format:
- Book
- Author/Creator:
- Lin, Zhouchen, Author.
- Li, Huan, Author.
- Fang, Cong., Author.
- Series:
- Computer Science (SpringerNature-11645)
- Language:
- English
- Subjects (All):
- Machine learning.
- Mathematical optimization.
- Computer science-Mathematics.
- Mathematics-Data processing.
- Machine Learning.
- Optimization.
- Mathematical Applications in Computer Science.
- Computational Mathematics and Numerical Analysis.
- Local Subjects:
- Machine Learning.
- Optimization.
- Mathematical Applications in Computer Science.
- Computational Mathematics and Numerical Analysis.
- Physical Description:
- 1 online resource (XXIII, 263 pages) : 1 illustrations
- Edition:
- 1st ed. 2022.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Singapore : Springer Nature Singapore : Imprint: Springer, 2022.
- System Details:
- text file PDF
- 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.
- Contents:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-981-16-9840-8
- 9789811698408
- Access Restriction:
- Restricted for use by site license.
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