Alternating Direction Method of Multipliers for Machine Learning [E-Book] / by Zhouchen Lin, Huan Li, Cong Fang.
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 co...
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Full text |
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Personal Name(s): | Lin, Zhouchen, author |
Fang, Cong, author / Li, Huan, author | |
Edition: |
1st edition 2022. |
Imprint: |
Singapore :
Springer,
2022
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Physical Description: |
XXIII, 263 pages 1 illustration (online resource) |
Note: |
englisch |
ISBN: |
9789811698408 |
DOI: |
10.1007/978-981-16-9840-8 |
Subject (LOC): |
- 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.