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An extrapolated iteratively reweighted $$\ell _1$$ ℓ 1 method with complexity analysis

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  • Hao Wang

    (ShanghaiTech University)

  • Hao Zeng

    (ShanghaiTech University)

  • Jiashan Wang

    (University of Washington)

Abstract

The iteratively reweighted $$\ell _1$$ ℓ 1 algorithm is a widely used method for solving various regularization problems, which generally minimize a differentiable loss function combined with a convex/nonconvex regularizer to induce sparsity in the solution. However, the convergence and the complexity of iteratively reweighted $$\ell _1$$ ℓ 1 algorithms is generally difficult to analyze, especially for non-Lipschitz differentiable regularizers such as $$\ell _p$$ ℓ p norm regularization with $$0

Suggested Citation

  • Hao Wang & Hao Zeng & Jiashan Wang, 2022. "An extrapolated iteratively reweighted $$\ell _1$$ ℓ 1 method with complexity analysis," Computational Optimization and Applications, Springer, vol. 83(3), pages 967-997, December.
    • Handle: RePEc:spr:coopap:v:83:y:2022:i:3:d:10.1007_s10589-022-00416-5
    DOI: 10.1007/s10589-022-00416-5
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    References listed on IDEAS

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    1. Yaohua Hu & Chong Li & Kaiwen Meng & Xiaoqi Yang, 2021. "Linear convergence of inexact descent method and inexact proximal gradient algorithms for lower-order regularization problems," Journal of Global Optimization, Springer, vol. 79(4), pages 853-883, April.
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