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Journal of Scientific Computing

, Volume 79, Issue 2, pp 809–826 | Cite as

A New Proximal Iterative Hard Thresholding Method with Extrapolation for \(\ell _0\) Minimization

  • Xue Zhang
  • Xiaoqun ZhangEmail author
Article
  • 148 Downloads

Abstract

In this paper, we consider a non-convex problem which is the sum of \(\ell _0\)-norm and a convex smooth function under a box constraint. We propose one proximal iterative hard thresholding type method with an extrapolation step for acceleration and establish its global convergence results. In detail, the sequence generated by the proposed method globally converges to a local minimizer of the objective function. Finally, we conduct numerical experiments to show the proposed method’s effectiveness on comparison with some other efficient methods.

Keywords

\(\ell _0\) regularization Proximal operator Hard threshholding Extrapolation Local minimizer Global convergence 

Notes

Acknowledgements

This work was partially supported by NSFC (No. 11771288), National key research and development program (No. 2017YFB0202902), the Young Top-notch Talent program of China, and 973 program (No. 2015CB856004).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematics and Computer ScienceShanxi Normal UniversityShanxiChina
  2. 2.Department of Mathematics and Institute of Natural SciencesShanghai Jiao Tong UniversityShanghaiChina

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