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

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Listed:
  • Haibin Zhang
  • Juan Wei
  • Meixia Li
  • Jie Zhou
  • Miantao Chao

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

Suggested Citation

  • Haibin Zhang & Juan Wei & Meixia Li & Jie Zhou & Miantao Chao, 2014. "On proximal gradient method for the convex problems regularized with the group reproducing kernel norm," Journal of Global Optimization, Springer, vol. 58(1), pages 169-188, January.
    • Handle: RePEc:spr:jglopt:v:58:y:2014:i:1:p:169-188
    DOI: 10.1007/s10898-013-0034-5
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    References listed on IDEAS

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    1. O. Boikanyo & G. Moroşanu, 2011. "Inexact Halpern-type proximal point algorithm," Journal of Global Optimization, Springer, vol. 51(1), pages 11-26, September.
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    4. Lukas Meier & Sara Van De Geer & Peter Bühlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71, February.
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    6. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
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    Cited by:

    1. Hai-Bin Zhang & Jiao-Jiao Jiang & Yun-Bin Zhao, 2015. "On the proximal Landweber Newton method for a class of nonsmooth convex problems," Computational Optimization and Applications, Springer, vol. 61(1), pages 79-99, May.

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