The Annals of Applied Statistics

Smoothing proximal gradient method for general structured sparse regression

Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, and Eric P. Xing

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We study the problem of estimating high-dimensional regression models regularized by a structured sparsity-inducing penalty that encodes prior structural information on either the input or output variables. We consider two widely adopted types of penalties of this kind as motivating examples: (1) the general overlapping-group-lasso penalty, generalized from the group-lasso penalty; and (2) the graph-guided-fused-lasso penalty, generalized from the fused-lasso penalty. For both types of penalties, due to their nonseparability and nonsmoothness, developing an efficient optimization method remains a challenging problem. In this paper we propose a general optimization approach, the smoothing proximal gradient (SPG) method, which can solve structured sparse regression problems with any smooth convex loss under a wide spectrum of structured sparsity-inducing penalties. Our approach combines a smoothing technique with an effective proximal gradient method. It achieves a convergence rate significantly faster than the standard first-order methods, subgradient methods, and is much more scalable than the most widely used interior-point methods. The efficiency and scalability of our method are demonstrated on both simulation experiments and real genetic data sets.

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Ann. Appl. Stat. Volume 6, Number 2 (2012), 719-752.

First available in Project Euclid: 11 June 2012

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Chen, Xi; Lin, Qihang; Kim, Seyoung; Carbonell, Jaime G.; Xing, Eric P. Smoothing proximal gradient method for general structured sparse regression. Ann. Appl. Stat. 6 (2012), no. 2, 719--752. doi:10.1214/11-AOAS514.

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