Skip to main content
Log in

Modified inertial Mann algorithm and inertial CQ-algorithm for nonexpansive mappings

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

In this article, we first introduce a modified inertial Mann algorithm and an inertial CQ-algorithm by combining the accelerated Mann algorithm and the CQ-algorithm with the inertial extrapolation, respectively. This strategy is intended to speed up the convergence of the given algorithms. Then we established the convergence theorems for two provided algorithms. For the inertial CQ-algorithm, the conditions on the inertial parameters are very weak. Finally, the numerical experiments are presented to illustrate that the modified inertial Mann algorithm and inertial CQ-algorithm may have a number of advantages over other methods in computing for some cases.

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.

Fig. 1

Similar content being viewed by others

References

  1. Bauschke, H.H., Combettes, P.L.: Convex Analysis and Monotone Operator Theory in Hilbert Spaces. Springer, Berlin (2011)

    Book  MATH  Google Scholar 

  2. 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 

  3. Chen, P., Huang, J., Zhang, X.: A primal-dual fixed point algorithm for convex separable minimization with applications to image restoration. Inverse Probl. 29, 025011, 33 (2013)

  4. Picard, E.: Memoire sur la theorie des equations aux derivees partielles et la methode des approximations successives. J. Math. Pures et Appl. 6, 145–210 (1890)

    MATH  Google Scholar 

  5. Halpern, B.: Fixed points of nonexpanding maps. Bull. Am. Math. Soc. 73, 957–961 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  6. He, S., Yang, C.: Boundary point algorithms for minimum norm fixed points of nonexpansive mappings. Fixed Point Theroy Appl. 2014, 56 (2014)

    Article  MATH  Google Scholar 

  7. Yang, C., He, S.: General alternative regularization methods for nonexpansive mappings in Hilbert spaces. Fixed Point Theroy Appl. 2014, 203 (2014)

    Article  MATH  Google Scholar 

  8. Krasnoselskii, M.A.: Two remarks on the method of successive approximations. Usp. Mat. Nauk 10, 123–127 (1955)

    MathSciNet  Google Scholar 

  9. Mann, W.R.: Mean value methods in iteration. Proc. Am. Math. Soc. 4, 506–510 (1953)

    Article  MathSciNet  MATH  Google Scholar 

  10. Bauschke, H.H., Combettes, P.L.: A weak-to-strong convergence principle for fejer-monotone methods in Hilbert spaces. Math. Oper. Res. 26, 248–264 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  11. Nakajo, K., Takahashi, W.: Strong convergence theorems for nonexpansive mappings and nonexpansive semigroups. J. Math. Anal. Appl. 279, 372–379 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  12. Dong, Q.L., Lu, Y.Y.: A new hybrid algorithm for a nonexpansive mapping. Fixed Point Theroy Appl. 2015, 37 (2015)

    Article  MATH  Google Scholar 

  13. Dong, Q.L., He, S., Cho, Y.J.: A new hybrid algorithm and its numerical realization for two nonexpansive mappings. Fixed Point Theroy Appl. 2015, 150 (2015)

    Article  MATH  Google Scholar 

  14. Iiduka, H.: Iterative algorithm for triple-hierarchical constrained nonconvex optimization problem and its application to network bandwidth allocation. SIAM J. Optim. 22, 862–878 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  15. Iiduka, H.: Fixed point optimization algorithms for distributed optimization in networked systems. SIAM J. Optim. 23, 1–26 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  16. Micchelli, C.A., Shen, L., Xu, Y.: Proximity algorithms for image models: denoising. Inverse Probl. 27(4), 045009 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  17. Polyak, B.T: Some methods of speeding up the convergence of iteration methods, U.S.S.R. Comput. Math. Math. Phys. 4(5), 1–17 (1964)

  18. Alvarez, F., Attouch, H.: An inertial proximal method for maximal monotone operators via discretization of a nonlinear oscillator with damping. Set Val. Anal. 9(1–2), 3–11 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  19. Moudafi, A., Oliny, M.: Convergence of a splitting inertial proximal method formonotone operators. J. Comput. Appl. Math. 155, 447–454 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  20. Lorenz, D.A., Pock, T.: An inertial forward-backward algorithm for monotone inclusions. J. Math. Imaging Vis. 51, 311–325 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  21. Chan, R.H., Ma, S., Yang, J.F.: Inertial proximal ADMM for linearly constrained separable convex optimization. SIAM J. Imaging Sci. 8(4), 2239–2267 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  22. Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  23. Chambolle, A., Dossal, Ch.: On the convergence of the iterates of the “fast iterative shrinkage/ thresholding algorithm”. J. Optim. Theory. Appl. 166, 968–982 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  24. Mainge, P.E: Convergence theorems for inertial KM-type algorithms. J. Comput. Appl. Math. 219, 223–236 (2008)

  25. Bot, R.I., Csetnek, E.R., Hendrich, C.: Inertial Douglas-Rachford splitting for monotone inclusion problems. Appl. Math. Comput. 256, 472–487 (2015)

    MathSciNet  MATH  Google Scholar 

  26. Nocedal, J., Wright, S.J.: Numerical Optimization. Springer Series in Operations Research and Financial Engineering, vol. 2, 2nd edn. Springer, Berlin (2006)

    Google Scholar 

  27. Dong, Q.L., Yuan, H.B.: Accelerated Mann and CQ algorithms for finding a fixed point of a nonexpansive mapping. Fixed Point Theory Appl. 2015, 125 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  28. Alvarez, F.: Weak convergence of a relaxed and inertial hybrid projection-proximal point algorithm for maximal monotone operators in Hilbert space. SIAM J. Optim. 14(3), 773–782 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  29. Matinez-Yanes, C., Xu, H.K.: Strong convergence of the CQ method for fixed point processes. Nonlinear Anal. 64, 2400–2411 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  30. Alvarez, F., Attouch, H.: An inertial proximal method for maximal monotone operators via discretization of a nonlinear oscillator with damping. Set Val. Anal. 9, 3–11 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  31. Sakurai, K., Liduka, H.: Acceleration of the Halpern algorithm to search for a fixed poin of a nonexpansive mapping. Fixed Point Theory Appl. 2014, 202 (2014)

    Article  Google Scholar 

  32. Dong, Q.L., Cho, Y.J., Zhong, L.L., Rassias, Th. M.: Inertial projection and contraction algorithms for variational inequalities, completed

  33. Xu, H.X.: Averaged mappings and the gradient-projection algorithm. J. Optim. Theory Appl. 150, 360–378 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  34. Zhou, H., Zhou, Y., Feng, G.: Iterative methods for solving a class of monotone variational inequality problems with applications. J. Inequal. Appl. 2015, 68 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  35. Ishikawa, S.: Fixed points by a new iteration method. Proc. Am. Math. Soc. 44, 147–150 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  36. Noor, M.A.: New approximation schemes for general variational inequalities. J. Math. Anal. Appl. 251, 217–229 (2000)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

Supported by National Natural Science Foundation of China (No. 61379102) and Open Fund of Tianjin Key Lab for Advanced Signal Processing (No. 2016ASP-TJ01). Also, the third author was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (2014R1A2A2A01002100). The authors would like to thank three anonymous referees for their careful reading of an earlier version of this paper and constructive suggestions. In particular, one referee gave us very valuable comments on the numerical experiments, which enabled us to improve the paper greatly.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Y. J. Cho.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, Q.L., Yuan, H.B., Cho, Y.J. et al. Modified inertial Mann algorithm and inertial CQ-algorithm for nonexpansive mappings. Optim Lett 12, 87–102 (2018). https://doi.org/10.1007/s11590-016-1102-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-016-1102-9

Keywords

Navigation