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Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers

Neal Parikh and Eric Chu

Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic ...

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Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers (Foundations and Trends(r) in Machine Learning)

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Length: 138 pages

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