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

Hao Wang (), Hao Zeng () and Jiashan Wang ()
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Hao Wang: ShanghaiTech University
Hao Zeng: ShanghaiTech University
Jiashan Wang: University of Washington

Computational Optimization and Applications, 2022, vol. 83, issue 3, No 8, 967-997

Abstract: 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

Keywords: $$\ell _p$$ ℓ p regularization; Extrapolation techniques; Iteratively reweighted methods; Kurdyka-Łojasiewicz; Non-Lipschitz regularization (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10589-022-00416-5

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