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...

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Bibliographic Details
Published in:	Computational optimization and applications Vol. 57; no. 2; pp. 307 - 337
Main Authors:	Necoara, Ion, Patrascu, Andrei
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.03.2014 Springer Nature B.V
Subjects:	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 Access:	Get full text
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