# A regularized Newton method for ℓq-norm composite optimization problems

arXiv: https://arxiv.org/abs/2203.02957 · HTML: https://mlsanity.com/paper/2203.02957/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2022.

## Built on by (most cited first, 1 of 1)

- [Alternating Iteratively Reweighted ℓ1 and Subspace Newton Algorithms for Nonconvex Sparse Optimization](https://mlsanity.com/paper/2407.17216/) (2024)

## Abstract (arXiv)

This paper is concerned with ℓq,(0<q<1)-norm regularized minimization problems with a twice continuously differentiable loss function. For this class of nonconvex and nonsmooth composite problems, many algorithms have been proposed to solve them and most of which are of the first-order type. In this work, we propose a hybrid of proximal gradient method and subspace regularized Newton method, named HpgSRN. The whole iterate sequence produced by HpgSRN is proved to have a finite length and converge to an L-type stationary point under a mild curve-ratio condition and the Kurdyka-Lojasiewicz property of the cost function, which does linearly if further a Kurdyka-Lojasiewicz property of exponent 1/2 holds. Moreover, a superlinear convergence rate for the iterate sequence is also achieved under an additional local error bound condition. Our convergence results do not require the isolatedness and strict local minimality properties of the L-stationary point. Numerical comparisons with ZeroFPR, a hybrid of proximal gradient method and quasi-Newton method for the forward-backward envelope of the cost function, proposed in [A. Themelis, L. Stella, and P. Patrinos, em SIAM J. Optim.,  28(2018), pp. 2274-2303] for the ℓq-norm regularized linear and logistic regressions on real data indicate that HpgSRN not only requires much less computing time but also yields comparable even better sparsities and objective function values.