Skip to main content
Log in

A Family of Projection Gradient Methods for Solving the Multiple-Sets Split Feasibility Problem

  • Published:
Journal of Optimization Theory and Applications Aims and scope Submit manuscript

Abstract

In the present paper, we explore a family of projection gradient methods for solving the multiple-sets split feasibility problem, which include the cyclic/simultaneous iteration methods introduced in Wen et al. (J Optim Theory Appl 166:844–860, 2015) as special cases. For the general case, where the involved sets are given by level sets of convex functions, the calculation of the projection onto the level sets is complicated in general, and thus, the resulting projection gradient method cannot be implemented easily. To avoid this difficulty, we introduce a family of relaxed projection gradient methods, in which the projections onto the approximated halfspaces are adopted in place of the ones onto the level sets. They cover the relaxed cyclic/simultaneous iteration methods introduced in Wen et al. (J Optim Theory Appl 166:844–860, 2015) as special cases. Global weak convergence theorems are established for these methods. In particular, as direct applications of the established theorems, our results fill some gaps and deal with the imperfections that appeared in Wen et al. (J Optim Theory Appl 166:844–860, 2015) and hence improve and extend the corresponding results therein.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Censor, Y., Elfving, T.: A multiprojection algorithm using Bregman projections in a product space. Numer. Algorithms 8, 221–239 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  2. Byrne, C.: A unified treatment of some iterative algorithms in signal processing and image reconstruction. Inverse Probl. 20, 103–120 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  3. He, H., Ling, C., Xu, H.K.: An implementable splitting algorithm for the \(\ell _1\)-norm regularized split feasibility problem. J. Sci. Comput. 67, 1–18 (2015)

    Google Scholar 

  4. Censor, Y., Bortfeld, T., Martin, B., Trofimov, A.: A unified approach for inversion problems in intensity-modulated radiation therapy. Phys. Med. Biol. 51, 2353–2365 (2006)

    Article  Google Scholar 

  5. Wang, J.H., Hu, Y.H., Li, C., Yao, J.C.: Linear convergence of CQ algorithms and applications in gene regulatory network inference. Inverse Probl. 33, 055017 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  6. Hu, Y.H., Li, C., Meng, K.W., Qin, J., Yang, X.Q.: Group sparse optimization via \(\ell _{p, q}\) regularization. J. Mach. Learn. Res. 18, 1–52 (2017)

    MathSciNet  Google Scholar 

  7. Byrne, C.: Iterative oblique projection onto convex sets and the split feasibility problem. Inverse Probl. 18, 441–453 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  8. Bauschke, H.H., Borwein, J.M.: On projection algorithms for solving convex feasibility problems. SIAM Rev. 38, 367–426 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  9. Bauschke, H.H., Kruk, S.G.: Reflection-projection method for convex feasibility problems with an obtuse cone. J. Optim. Theory Appl. 120, 503–531 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  10. Hu, Y.H., Li, C., Yang, X.Q.: On convergence rates of linearized proximal algorithms for convex composite optimization with applications. SIAM J. Optim. 26, 1207–1235 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  11. Li, G.Y., Pong, T.K.: Douglas–Rachford splitting for nonconvex optimization with application to nonconvex feasibility problems. Math. Program. Ser. A 159, 371–401 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  12. Censor, Y., Elfving, T., Kopf, N., Bortfeld, T.: The multiple-sets split feasibility problem and its applications for inverse problems. Inverse Probl. 21, 2071–2084 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  13. Xu, H.K.: A variable Krasnosel’skiĭ-Mann algorithm and the multiple-set split feasibility problem. Inverse Probl. 22, 2021–2034 (2006)

    Article  MATH  Google Scholar 

  14. Zhang, W.X., Han, D.R., Li, Z.B.: A self-adaptive projection method for solving the multiple-sets split feasibility problem. Inverse Probl. 25, 115001 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  15. Zhao, J.L., Yang, Q.Z.: Self-adaptive projection methods for the multiple-sets split feasibility problem. Inverse Probl. 27, 035009 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  16. Zhao, J.L., Yang, Q.Z.: A simple projection method for solving the multiple-sets split feasibility problem. Inverse Probl. Sci. Eng. 21, 537–546 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Wen, M., Peng, J.G., Tang, Y.C.: A cyclic and simultaneous iterative method for solving the multiple-sets split feasibility problem. J. Optim. Theory Appl. 166, 844–860 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  18. Bauschke, H.H., Combettes, P.L.: Convex Analysis and Monotone Operator Theory in Hilbert Space. Springer, London (2011)

    Book  MATH  Google Scholar 

  19. Bregman, L.M.: The method of successive projection for finding a common point of convex sets. Sov. Math. Dokl. 6, 688–692 (1965)

    MATH  Google Scholar 

  20. Gubin, L.G., Polyak, B.T., Raik, E.V.: The method of projections for finding the common point of convex sets. USSR Comput. Math. Math. Phys. 7, 1–24 (1967)

    Article  Google Scholar 

  21. Combettes, P.L.: Hilbertian convex feasibility problem: convergence of projection methods. Appl. Math. Optim. 35, 311–330 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhao, X., Ng, K.F., Li, C., Yao, J.C.: Linear regularity and linear convergence of projection-based methods for solving convex feasibility problems. Appl. Math. Optim. 78, 613–641 (2018)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the anonymous referees and the editor for their valuable comments and suggestions that helped to improve the quality of this paper. Jinhua Wang was supported in part by the National Natural Science Foundation of China (Grant 11771397) and Zhejiang Provincial Natural Science Foundation of China (Grant LY17A010021). Yaohua Hu was supported in part by National Natural Science Foundation of China (11601343, 11601344, 11871347), Natural Science Foundation of Guangdong (2016A030310038), Natural Science Foundation of Shenzhen (JCYJ20170817100950436, JCYJ20170818091621856) and Interdisciplinary Innovation Team of Shenzhen University. Carisa Kwok Wai Yu is supported in part by the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS14/P02/15 and UGC/FDS14/P02/17).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yaohua Hu.

Additional information

Communicated by Julian P. Revalski.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Hu, Y., Yu, C.K.W. et al. A Family of Projection Gradient Methods for Solving the Multiple-Sets Split Feasibility Problem. J Optim Theory Appl 183, 520–534 (2019). https://doi.org/10.1007/s10957-019-01563-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10957-019-01563-2

Keywords

Mathematics Subject Classification

Navigation