IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v74y2019i1d10.1007_s10898-019-00739-4.html
   My bibliography  Save this article

The locally Chen–Harker–Kanzow–Smale smoothing functions for mixed complementarity problems

Author

Listed:
  • Zhengyong Zhou

    (Shanxi Normal University)

  • Yunchan Peng

    (Shanxi Normal University)

Abstract

According to the structure of the projection function onto the box set $$\varPi _X$$ Π X and the Chen–Harker–Kanzow–Smale (CHKS) smoothing function, a new class of smoothing projection functions onto the box set are proposed in this paper. The new smoothing projection functions only smooth $$\varPi _X$$ Π X in neighborhoods of nonsmooth points of $$\varPi _X$$ Π X , and keep unchanged with $$\varPi _X$$ Π X at other points, hence they are referred as the locally Chen–Harker–Kanzow–Smale (LCHKS) smoothing functions. Based on the Robinson’s normal equation and the LCHKS smoothing functions, a smoothing Newton method with its convergence results is proposed for solving mixed complementarity problems. Compared with smoothing Newton methods based on various smoothing projection functions, the computations of the LCHKS smoothing function, the function value and its Jacobian matrix of the Newton equation become cheaper, and the Newton direction can be found by solving a low dimensional linear equation, hence the smoothing Newton method based on the LCHKS smoothing functions shows more efficient for large-scale mixed complementarity problems. The LCHKS smoothing functions are proved to be feasible, continuously differentiable, uniform approximations of $$\varPi _X$$ Π X , globally Lipschitz continuous and strongly semismooth, which are important to establish the superlinear and quadratic convergence of the smoothing Newton method. The proposed smoothing Newton method is implemented in MATLAB and numerical tests are done on the MCPLIB test collection. Numerical results show that the smoothing Newton method based on the LCHKS smoothing functions is promising for mixed complementarity problems.

Suggested Citation

  • Zhengyong Zhou & Yunchan Peng, 2019. "The locally Chen–Harker–Kanzow–Smale smoothing functions for mixed complementarity problems," Journal of Global Optimization, Springer, vol. 74(1), pages 169-193, May.
  • Handle: RePEc:spr:jglopt:v:74:y:2019:i:1:d:10.1007_s10898-019-00739-4
    DOI: 10.1007/s10898-019-00739-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10898-019-00739-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10898-019-00739-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stephen M. Robinson, 1992. "Normal Maps Induced by Linear Transformations," Mathematics of Operations Research, INFORMS, vol. 17(3), pages 691-714, August.
    2. Chen, Bintong & Harker, Patrick T. & Pinar, Mustafa C., 1999. "Continuation method for nonlinear complementarity problems via normal maps," European Journal of Operational Research, Elsevier, vol. 116(3), pages 591-606, August.
    3. L. Qi & D. Sun, 2002. "Smoothing Functions and Smoothing Newton Method for Complementarity and Variational Inequality Problems," Journal of Optimization Theory and Applications, Springer, vol. 113(1), pages 121-147, April.
    4. Liqun Qi, 1993. "Convergence Analysis of Some Algorithms for Solving Nonsmooth Equations," Mathematics of Operations Research, INFORMS, vol. 18(1), pages 227-244, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. J. Han & D. Sun, 1997. "Newton and Quasi-Newton Methods for Normal Maps with Polyhedral Sets," Journal of Optimization Theory and Applications, Springer, vol. 94(3), pages 659-676, September.
    2. Pin-Bo Chen & Gui-Hua Lin & Xide Zhu & Fusheng Bai, 2021. "Smoothing Newton method for nonsmooth second-order cone complementarity problems with application to electric power markets," Journal of Global Optimization, Springer, vol. 80(3), pages 635-659, July.
    3. Todd S. Munson & Francisco Facchinei & Michael C. Ferris & Andreas Fischer & Christian Kanzow, 2001. "The Semismooth Algorithm for Large Scale Complementarity Problems," INFORMS Journal on Computing, INFORMS, vol. 13(4), pages 294-311, November.
    4. S. H. Pan & Y. X. Jiang, 2008. "Smoothing Newton Method for Minimizing the Sum of p-Norms," Journal of Optimization Theory and Applications, Springer, vol. 137(2), pages 255-275, May.
    5. L. Qi & D. Sun, 2002. "Smoothing Functions and Smoothing Newton Method for Complementarity and Variational Inequality Problems," Journal of Optimization Theory and Applications, Springer, vol. 113(1), pages 121-147, April.
    6. Zhengyong Zhou & Bo Yu, 2014. "A smoothing homotopy method for variational inequality problems on polyhedral convex sets," Journal of Global Optimization, Springer, vol. 58(1), pages 151-168, January.
    7. Michael Patriksson & R. Tyrrell Rockafellar, 2002. "A Mathematical Model and Descent Algorithm for Bilevel Traffic Management," Transportation Science, INFORMS, vol. 36(3), pages 271-291, August.
    8. Chen Ling & Hongxia Yin & Guanglu Zhou, 2011. "A smoothing Newton-type method for solving the L 2 spectral estimation problem with lower and upper bounds," Computational Optimization and Applications, Springer, vol. 50(2), pages 351-378, October.
    9. Dong-Hui Li & Liqun Qi & Judy Tam & Soon-Yi Wu, 2004. "A Smoothing Newton Method for Semi-Infinite Programming," Journal of Global Optimization, Springer, vol. 30(2), pages 169-194, November.
    10. John Duggan & Tasos Kalandrakis, 2011. "A Newton collocation method for solving dynamic bargaining games," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 36(3), pages 611-650, April.
    11. Liang Chen & Anping Liao, 2020. "On the Convergence Properties of a Second-Order Augmented Lagrangian Method for Nonlinear Programming Problems with Inequality Constraints," Journal of Optimization Theory and Applications, Springer, vol. 187(1), pages 248-265, October.
    12. Baohua Huang & Wen Li, 2023. "A smoothing Newton method based on the modulus equation for a class of weakly nonlinear complementarity problems," Computational Optimization and Applications, Springer, vol. 86(1), pages 345-381, September.
    13. H. Xu & B. M. Glover, 1997. "New Version of the Newton Method for Nonsmooth Equations," Journal of Optimization Theory and Applications, Springer, vol. 93(2), pages 395-415, May.
    14. Ralf Münnich & Ekkehard Sachs & Matthias Wagner, 2012. "Calibration of estimator-weights via semismooth Newton method," Journal of Global Optimization, Springer, vol. 52(3), pages 471-485, March.
    15. Y. Gao, 2006. "Newton Methods for Quasidifferentiable Equations and Their Convergence," Journal of Optimization Theory and Applications, Springer, vol. 131(3), pages 417-428, December.
    16. Sanja Rapajić & Zoltan Papp, 2017. "A nonmonotone Jacobian smoothing inexact Newton method for NCP," Computational Optimization and Applications, Springer, vol. 66(3), pages 507-532, April.
    17. G. L. Zhou & L. Caccetta, 2008. "Feasible Semismooth Newton Method for a Class of Stochastic Linear Complementarity Problems," Journal of Optimization Theory and Applications, Springer, vol. 139(2), pages 379-392, November.
    18. Alexander Shapiro, 2005. "Sensitivity Analysis of Parameterized Variational Inequalities," Mathematics of Operations Research, INFORMS, vol. 30(1), pages 109-126, February.
    19. M. A. Tawhid & J. L. Goffin, 2008. "On Minimizing Some Merit Functions for Nonlinear Complementarity Problems under H-Differentiability," Journal of Optimization Theory and Applications, Springer, vol. 139(1), pages 127-140, October.
    20. C. Kanzow & H. Qi & L. Qi, 2003. "On the Minimum Norm Solution of Linear Programs," Journal of Optimization Theory and Applications, Springer, vol. 116(2), pages 333-345, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jglopt:v:74:y:2019:i:1:d:10.1007_s10898-019-00739-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.