Skip to main content
Log in

An active-set Levenberg–Marquardt method for degenerate nonlinear complementarity problem under local error bound conditions

  • Original Research
  • Published:
Journal of Applied Mathematics and Computing Aims and scope Submit manuscript

Abstract

In this paper, we focus on the identification of the degenerate indices of the nonlinear complementarity problem under the local error bound condition. Under this condition, the complementarity problems may fail to have non-singularity or local uniqueness at its solutions. We present an active-set Levenberg–Marquardt method, which introduces an estimated set to approximate the degenerate indices of the solutions. When near the solution, the degenerate indices will be correctly identified and the original problem will be transformed to a reduced problem. This method has global convergence. Under the local error bound condition, local superlinear convergence is obtained as well.

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. Facchinei, F., Fischer, A., Kanzow, C.: On the accurate identification of active constraints. SIAM. J. Optim. 9, 14–32 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  2. Kanzow, C., Qi, H.D.: A QP-free constrained Newton-type method for variational inequality problems. Math. Prog. 85, 81–106 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  3. Yu, H.D., Pu, D.G.: Smoothing Newton method for NCP with the identification of degenerate indices. J. Comput. Appl. Math. 234, 3424–3435 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  4. Yamashita, N., Dan, H., Fukushima, F.: On the identification of degenerate indices in the nonlinear complementarity problem with the proximal point algorithm. Math. Prog. 99, 377–397 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  5. Yamashita, N., Fukushima, F.: The proximal point algorithm with genuine superlinear convergence for the monotone complementarity problem. SIAM J. Optim. 11, 364–379 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  6. HäuBler, W.M.: A local convergence analysis for the gauss-newton and Levenberg–Morrison–Marquardt algorithms. Computing 31, 231–244 (1983)

    Article  MathSciNet  Google Scholar 

  7. Ueda, K., Yamashita, N.: Global complexity bound analysis of the Levenberg–Marquardt method for nonsmooth equations and its application to the nonlinear complementarity problem. J. Optim. Theory Appl. 152, 450–467 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  8. Behling, R., Fischer, A.: A unified local convergence analysis of inexact constrained Levenberg–Marquardt methods. Optim. Lett. 6, 927–940 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  9. Zhou, W., Chen, X.: On the convergence of a modified regularized Newton method for convex optimization with singular solutions. J. Comput. Appl. Math. 239, 179–188 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  10. Zhu, D.: Affine scaling interior Levenberg–Marquardt method for bound-constrained semismooth equations under local error bound conditions. J. Comput. Appl. Math. 219, 198–215 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  11. Zhang, J., Zhang, X.: A smoothing Levenberg–Marquardt method for NCP. Appl. Math. Comput. 178, 212–228 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  12. Yu, H.D., Pu, D.G.: Smoothing Levenberg–Marquardt method for general nonlinear complementarity problems under local error bound. Appl. Math. Model. 35, 1337–1348 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  13. Yamashita, N., Fukushima, M.: On the rate of convergence of the Levenberg–Marquardt method. Computing 15, 239–249 (2001)

    MathSciNet  MATH  Google Scholar 

  14. Dan, H., Yamashita, N., Fukushima, M.: Convergence properties of the inexact Levenberg–Marquardt method under local error bound conditions. Optim. Methods Softw. 17, 605–626 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  15. Fan, J.Y., Yuan, Y.X.: On the quadratic convergence of the Levenberg–Marquardt method without nonsingularity assumption. Computing 74, 23–39 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  16. Clarke, F.H.: Optimization and Nonsmooth Analysis. Wiley, New York (1983)

    MATH  Google Scholar 

  17. Facchinei, F., Fischer, A., Kanzow, C.: Regularity properties of a semismooth reformulation of variational inequalities. SIAM. J. Optim. 8, 850–869 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  18. Qi, L.: C-differentiability, C-differential operators and generalized newton methods. Applied Mathematics Report AMR96/5, University of New South Wales, Sydney, Australia (1996)

  19. Kanzow, C., Pieper, H.: Jacobian smoothing methods for nonlinear complementarity problems. SIAM. J. Optim. 9, 342–373 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  20. Mathiesen, L.: An algorithm based on a sequence of a linear complementarity problems applied to a Walrasion equilibrium model: An example. Math. Prog. 37, 1–18 (1987)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by National Natural Science Foundation of China (11401384), the Scientific Research Foundation for youth teachers of Lixin University of Commerce (2014QNYB17).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haodong Yu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, H. An active-set Levenberg–Marquardt method for degenerate nonlinear complementarity problem under local error bound conditions. J. Appl. Math. Comput. 52, 191–213 (2016). https://doi.org/10.1007/s12190-015-0937-z

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12190-015-0937-z

Keywords

Mathematics Subject Classification

Navigation