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Main Authors: Ye, Yuge, Li, Qingna
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2507.03566
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author Ye, Yuge
Li, Qingna
author_facet Ye, Yuge
Li, Qingna
contents In this paper, we propose a globally convergent Newton type method to solve $\ell_0$ regularized sparse optimization problem. In fact, a line search strategy is applied to the Newton method to obtain global convergence. The Jacobian matrix of the original problem is a block upper triangular matrix. To reduce the computational burden, our method only requires the calculation of the block diagonal. We also introduced regularization to overcome matrix singularity. Although we only use the block-diagonal part of the Jacobian matrix, our algorithm still maintains global convergence and achieves a local quadratic convergence rate. Numerical results demonstrate the efficiency of our method.
format Preprint
id arxiv_https___arxiv_org_abs_2507_03566
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Modified Block Newton Algorithm for $\ell_0$-Regularized Optimization
Ye, Yuge
Li, Qingna
Optimization and Control
In this paper, we propose a globally convergent Newton type method to solve $\ell_0$ regularized sparse optimization problem. In fact, a line search strategy is applied to the Newton method to obtain global convergence. The Jacobian matrix of the original problem is a block upper triangular matrix. To reduce the computational burden, our method only requires the calculation of the block diagonal. We also introduced regularization to overcome matrix singularity. Although we only use the block-diagonal part of the Jacobian matrix, our algorithm still maintains global convergence and achieves a local quadratic convergence rate. Numerical results demonstrate the efficiency of our method.
title Modified Block Newton Algorithm for $\ell_0$-Regularized Optimization
topic Optimization and Control
url https://arxiv.org/abs/2507.03566