Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2501.08719 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909457055744000 |
|---|---|
| author | Wu, Fan Wei, Jiazhen Bian, Wei |
| author_facet | Wu, Fan Wei, Jiazhen Bian, Wei |
| contents | For a class of sparse optimization problems with the penalty function of $\|(\cdot)_+\|_0$, we first characterize its local minimizers and then propose an extrapolated hard thresholding algorithm to solve such problems. We show that the iterates generated by the proposed algorithm with $ε>0$ (where $ε$ is the dry friction coefficient) have finite length, without relying on the Kurdyka-Łojasiewicz inequality. Furthermore, we demonstrate that the algorithm converges to an $ε$-local minimizer of this problem. For the special case that $ε=0$, we establish that any accumulation point of the iterates is a local minimizer of the problem. Additionally, we analyze the convergence when an error term is present in the algorithm, showing that the algorithm still converges in the same manner as before, provided that the errors asymptotically approach zero. Finally, we conduct numerical experiments to verify the theoretical results of the proposed algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08719 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extrapolated Hard Thresholding Algorithms with Finite Length for Composite $\ell_0$ Penalized Problems Wu, Fan Wei, Jiazhen Bian, Wei Optimization and Control For a class of sparse optimization problems with the penalty function of $\|(\cdot)_+\|_0$, we first characterize its local minimizers and then propose an extrapolated hard thresholding algorithm to solve such problems. We show that the iterates generated by the proposed algorithm with $ε>0$ (where $ε$ is the dry friction coefficient) have finite length, without relying on the Kurdyka-Łojasiewicz inequality. Furthermore, we demonstrate that the algorithm converges to an $ε$-local minimizer of this problem. For the special case that $ε=0$, we establish that any accumulation point of the iterates is a local minimizer of the problem. Additionally, we analyze the convergence when an error term is present in the algorithm, showing that the algorithm still converges in the same manner as before, provided that the errors asymptotically approach zero. Finally, we conduct numerical experiments to verify the theoretical results of the proposed algorithm. |
| title | Extrapolated Hard Thresholding Algorithms with Finite Length for Composite $\ell_0$ Penalized Problems |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2501.08719 |