Alternating Direction Method of Multipliers for Machine Learning - Softcover

Lin, Zhouchen; Li, Huan; Fang, Cong

Softcover

ISBN 10: 9811698414 ISBN 13: 9789811698415

Verlag: Springer, 2022

Inhaltsangabe

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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2022
• ISBN 10: 9811698414
• ISBN 13: 9789811698415
• Einband: Tapa blanda
• Sprache: Englisch
• Anzahl der Seiten: 288
• Kontakt zum Hersteller: Nicht verfügbar