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Alternating Direction Method of Multipliers for Machine Learning

Alternating Direction Method of Multipliers for Machine Learning

Cong Fang / Huan Li / Zhouchen Lin

171,79 €
IVA incluido
Disponible
Editorial:
Springer Nature B.V.
Año de edición:
2022
Materia
Inteligencia artificial
ISBN:
9789811698392
171,79 €
IVA incluido
Disponible

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Sobre IBD

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.

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