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A fast splitting method tailored for Dantzig selector

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  • Hongjin He
  • Xingju Cai
  • Deren Han

Abstract

In this paper, we introduce a splitting method for solving Dantzig selector problem, a new linear regression model that was extensively studied in the literature in the past few years. The new method is very simple in the sense that, per iteration, it only performs a projection onto a box, and does some matrix-vector products. We prove the global convergence of the method and report some promising numerical results, which demonstrate that the new method is competitive with some state-of-the-art methods recently developed in the literature. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Hongjin He & Xingju Cai & Deren Han, 2015. "A fast splitting method tailored for Dantzig selector," Computational Optimization and Applications, Springer, vol. 62(2), pages 347-372, November.
    • Handle: RePEc:spr:coopap:v:62:y:2015:i:2:p:347-372
    DOI: 10.1007/s10589-015-9748-2
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    References listed on IDEAS

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    1. Lu, Zhaosong & Pong, Ting Kei & Zhang, Yong, 2012. "An alternating direction method for finding Dantzig selectors," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4037-4046.
    2. Gareth M. James & Peter Radchenko & Jinchi Lv, 2009. "DASSO: connections between the Dantzig selector and lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 127-142, January.
    3. 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.
    4. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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