Journal of the Operations Research Society of China ›› 2025, Vol. 13 ›› Issue (1): 161-183.doi: 10.1007/s40305-023-00459-3

• • 上一篇    下一篇

  

  • 收稿日期:2022-04-07 修回日期:2023-01-14 出版日期:2025-03-30 发布日期:2025-03-20
  • 通讯作者: Xue Zhang,Xiao-Qun Zhang E-mail:zhangxue2100442@163.com;xqzhang@sjtu.edu.cn
  • 基金资助:
    This work was supported by the National Natural Science Foundation of China (No. 11901368).

A Variable Metric Extrapolation Proximal Iterative Hard Thresholding Method

Xue Zhang1, Xiao-Qun Zhang2,3   

  1. 1 School of Mathematics and Computer Science, Shanxi Normal University, Taiyuan 030002, Shanxi, China;
    2 Department of Mathematics, Shanghai Jiao Tong University, Shanghai 200240, China;
    3 Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2022-04-07 Revised:2023-01-14 Online:2025-03-30 Published:2025-03-20
  • Contact: Xue Zhang,Xiao-Qun Zhang E-mail:zhangxue2100442@163.com;xqzhang@sjtu.edu.cn

Abstract: In this paper, we propose a variable metric extrapolation proximal iterative hard thresholding (VMEPIHT) method for nonconvex $\ell_0$-norm sparsity regularization problem which has wide applications in signal and image processing, machine learning and so on. The VMEPIHT method is based on the forward-backward splitting (FBS) method, and variable metric strategy is employed in the extrapolation step to speed up the algorithm. The proposed method’s convergence, linear convergence rate and superlinear convergence rate are shown under appropriate assumptions. Finally, we conduct numerical experiments on compressed sensing problem and CT image reconstruction problem to confirm the efficiency of the proposed method, compared with other state-of-the-art methods.

Key words: Variable metric, Iterative hard thresholding, Linear convergence rate, Superlinear convergence rate

中图分类号: