Skip to main content
Log in

A self-adaptive three-term conjugate gradient method for monotone nonlinear equations with convex constraints

  • Published:
Calcolo Aims and scope Submit manuscript

A Correction to this article was published on 14 May 2022

This article has been updated

Abstract

In this paper, a self-adaptive three-term conjugate gradient method is proposed for solving monotone nonlinear equations with convex constraints. Under milder conditions, the global convergence of the method is proved. Numerical experiments reported in this paper illustrate that the method is stable and efficient for monotone nonlinear equations, especially for the large-scale problems with convex constraints.

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
Fig. 2

Similar content being viewed by others

Change history

References

  1. Al-Baali, M., Narushima, Y., Yabe, H.: A family of three-term conjugate gradient methods with sufficient descent property for unconstrained optimization. Comput. Optim. Appl. (2014). doi:10.1007/s10589-014-9662-z

    MathSciNet  MATH  Google Scholar 

  2. Bellavia, S., Macconi, M., Morini, B.: STRSCNE: a scaled trust-region solver for constrained nonlinear equations. Comput. Optim. Appl. 28, 31–50 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bongartz, K.E., Conn, A.R., Gould, N.I.M., Toint, P.L.: CUTE: constrained and unconstrained testing environments. ACM Trans. Math. Softw. 21, 123–160 (1995)

    Article  MATH  Google Scholar 

  4. Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91, 201–213 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  5. Hager, W.W., Zhang, H.C.: A survey of nonlinear conjugate gradient method. Pac. J. Optim. 2, 35–58 (2006)

    MathSciNet  MATH  Google Scholar 

  6. Hager, W.W., Zhang, H.C.: A new conjugate gradient method with guaranteed descent and an efficient line search. SIAM J. Optim. 16, 170–192 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  7. Han, D.R., Sun, W.Y.: A new modified Goldstein–Levitin–Polyak projection method for variational inequality problems. Comput. Math. Appl. 47, 1817–1825 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  8. He, B.S., Liao, L.Z., Wang, X.: Proximal-like contraction methods for monotone variational inequalities in a unified framework II: general methods and numerical experiments. Comput. Optim. Appl. 51, 681–708 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  9. He, B.S., Yang, H., Wang, S.L.: Alternationg direction method with self-adaptive penalty parameters for monotone variational inequalites. J. Optim. Theory Appl. 106, 337–356 (2000)

    Article  MathSciNet  Google Scholar 

  10. Kanzow, C., Yamashita, N., Fukushima, M.: Levenberg–Marquardt methods with strong local convergence properties for solving nonlinear equations with convex constraints. J. Comput. Appl. Math. 172, 375–397 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  11. Narushima, Y.: A smoothing conjugate gradient method for solving systems of nonsmooth equations. Appl. Math. Comput. 219, 8646–8655 (2013)

    MathSciNet  MATH  Google Scholar 

  12. Narushima, Y., Yabe, H., Ford, J.A.: A three-term conjugate gradient method with sufficient descent property for unconstrained optimization. SIAM J. Optim. 21, 212–230 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  13. Sugiki, K., Narushima, Y., Yabe, H.: Globally convergent three-term conjugate gradient methods that use secant conditions and generate descent search directions for unconstrained optimization. J. Optim. Theory Appl. 153, 733–757 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  14. Solodov, M.V., Svaiter, B.F.: A globally convergent inexact Newton method for systems of monotone equations. In: Fukushima, M., Qi, L. (eds.) Reformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods, pp. 355–369. Kluwer Academic Publishers, Norwell (1998)

    Chapter  Google Scholar 

  15. Solodov, M.V., Svaiter, B.F.: A new projection method for variational inequality problems. SIAM J. Control. Optim. 37, 765–776 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  16. Wang, Y.J., Xiu, N.H., Zhang, J.Z.: Modified extragradient method for variational inequalities and verification of solution existence. J. Optim. Theory Appl. 119, 167–183 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  17. Wang, C.Y., Wang, Y.J.: A superlinearly convergent projection method for constrained systems of nonlinear equations. J. Glob. Optim. 44, 283–296 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  18. Wang, C.W., Wang, Y.J., Xu, C.L.: A projection method for a system of nonlinear monotone equations with convex constraints. Math. Meth. Oper. Res. 66, 33–46 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  19. Xiao, Y., Zhu, H.: A conjugate gradient method to solve convex constrained monotone equations with applications in compressive sensing. J. Math. Anal. Appl. 405, 310–319 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  20. Yu, Z.S., Sun, J., Qin, Y.: A multivariate spectral projected gradient method for bound constrained optimization. J. Comput. Appl. Math. 235, 2263–2269 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhang, L., Zhou, W.J., Li, D.H.: Some descent three-term conjugate gradient methods and their global convergence. Optim. Methods Softw. 22, 697–711 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhang, L., Zhou, W.J., Li, D.H.: A descent modified Polak–Ribière–Polyak conjugate gradient method and its global convergence. IMA J. Numer. Anal. 26, 629–640 (2006)

  23. 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 (2009). doi:10.1088/0266-5611/25/11/115001

Download references

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant Numbers: 11171362, 11301567 and 11401058), Specialized Research Fund for the Doctoral Program of Higher Education (Grant Number: 20120191110031), the Technology Project of Chongqing Education Committee (Grant Number: KJ130732), and the Research Start Project of Chongqing Technology and Business University (Grant Number: 2012-56-04). The authors thank the two anonymous reviewers for their valuable comments and suggestions, which helped to improve the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to X. Y. Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X.Y., Li, S.J. & Kou, X.P. A self-adaptive three-term conjugate gradient method for monotone nonlinear equations with convex constraints. Calcolo 53, 133–145 (2016). https://doi.org/10.1007/s10092-015-0140-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10092-015-0140-5

Keywords

Mathematics Subject Classfication

Navigation