Alternating Direction Method of Multipliers for Machine Learning

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 con...

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Bibliographic Details
Main Authors Lin, Zhouchen, Li, Huan, Fang, Cong
Format eBook
LanguageEnglish
Published Singapore Springer 2022
Springer Nature Singapore
Edition1
Subjects
Computational Mathematics and Numerical Analysis
Computer algorithms
Computer Science
Machine Learning
Machine learning-Statistical methods
Mathematical Applications in Computer Science
Mathematical optimization
Multipliers (Mathematical analysis)
Optimization
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ISBN9811698392
9789811698392
DOI10.1007/978-981-16-9840-8

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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.
ISBN:9811698392
9789811698392
DOI:10.1007/978-981-16-9840-8