Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 7-14 Tagen versandfertig
Veröffentlichung:	Juni 2022
Genre:	EDV / Informatik
	AlternatingDirectionMethodofMultipliers / constrainedoptimization / First-OrderOptimization / LagrangeMultiplierMethod / Maschinelles Lernen / OperatorSplitting / Optimierung / Theoretische Informatik
ISBN:	9789811698392
EAN-Code:	9789811698392
Verlag:	Springer
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 241 mm / B 160 mm / D 22 mm
Gewicht:	600 gr
Seiten:	288
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.