A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints

In this paper we propose a variant of the random coordinate descent method for solving linearly constrained convex optimization problems with composite objective functions. If the smooth part of the objective function has Lipschitz continuous gradient, then we prove that our method obtains an ϵ -opt...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Computational optimization and applications Jg. 57; H. 2; S. 307 - 337
Hauptverfasser:	Necoara, Ion, Patrascu, Andrei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.03.2014 Springer Nature B.V
Schlagworte:	Algorithms Computer science Convergence Convex analysis Convex and Discrete Geometry Derivatives Descent Management Science Mathematical analysis Mathematical models Mathematics Mathematics and Statistics Methods Operations Research Operations Research/Decision Theory Optimization Statistics Studies Support vector machines Texts Convergence rate Composite objective function Linearly coupled constraints Randomized algorithms Coordinate descent
ISSN:	0926-6003, 1573-2894
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!