On proximal gradient method for the convex problems regularized with the group reproducing kernel norm

Haibin Zhang (), Juan Wei, Meixia Li, Jie Zhou and Miantao Chao

Journal of Global Optimization, 2014, vol. 58, issue 1, 169-188

Abstract: We consider a class of nonsmooth convex optimization problems where the objective function is the composition of a strongly convex differentiable function with a linear mapping, regularized by the group reproducing kernel norm. This class of problems arise naturally from applications in group Lasso, which is a popular technique for variable selection. An effective approach to solve such problems is by the proximal gradient method. In this paper we derive and study theoretically the efficient algorithms for the class of the convex problems, analyze the convergence of the algorithm and its subalgorithm. Copyright Springer Science+Business Media New York 2014

Keywords: Nonsmooth convex optimization; Proximal gradient method; Linearly convergence; Quadratically convergence; Group Lasso (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s10898-013-0034-5

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