Alternating Direction Method Of Multipliers For Machine Learning
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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 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.
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
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 regression, basis pursuit, covariance selection, support vector machines, and many others.
Theory and Applications of Models of Computation
This book constitutes the refereed proceedings of the 17th Annual Conference on Theory and Applications of Models of Computation, TAMC 2022, held as a virtual event, in September 2022. The 33 full papers were carefully reviewed and selected from 75 submissions. The main themes of the selected papers are computability, complexity, algorithms, information theory and their extensions to machine learning theory, and foundations of artificial intelligence.