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Sparse estimation via lower-order penalty optimization methods in high-dimensional linear regression

Author

Listed:
  • Xin Li

    (Northwest University)

  • Yaohua Hu

    (Shenzhen University)

  • Chong Li

    (Zhejiang University)

  • Xiaoqi Yang

    (The Hong Kong Polytechnic University)

  • Tianzi Jiang

    (Chinese Academy of Sciences)

Abstract

The lower-order penalty optimization methods, including the $$\ell _q$$ ℓ q minimization method and the $$\ell _q$$ ℓ q regularization method $$(0

Suggested Citation

  • Xin Li & Yaohua Hu & Chong Li & Xiaoqi Yang & Tianzi Jiang, 2023. "Sparse estimation via lower-order penalty optimization methods in high-dimensional linear regression," Journal of Global Optimization, Springer, vol. 85(2), pages 315-349, February.
    • Handle: RePEc:spr:jglopt:v:85:y:2023:i:2:d:10.1007_s10898-022-01220-5
    DOI: 10.1007/s10898-022-01220-5
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    References listed on IDEAS

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    1. Pun, Chi Seng & Wong, Hoi Ying, 2019. "A linear programming model for selection of sparse high-dimensional multiperiod portfolios," European Journal of Operational Research, Elsevier, vol. 273(2), pages 754-771.
    2. Giuzio, Margherita & Ferrari, Davide & Paterlini, Sandra, 2016. "Sparse and robust normal and t- portfolios by penalized Lq-likelihood minimization," European Journal of Operational Research, Elsevier, vol. 250(1), pages 251-261.
    3. X. X. Huang & X. Q. Yang, 2003. "A Unified Augmented Lagrangian Approach to Duality and Exact Penalization," Mathematics of Operations Research, INFORMS, vol. 28(3), pages 533-552, August.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. Le Thi, H.A. & Pham Dinh, T. & Le, H.M. & Vo, X.T., 2015. "DC approximation approaches for sparse optimization," European Journal of Operational Research, Elsevier, vol. 244(1), pages 26-46.
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