IDEAS home Printed from https://ideas.repec.org/a/wsi/apjorx/v38y2021i05ns0217595921400108.html
   My bibliography  Save this article

Smoothing Approximation to the New Exact Penalty Function with Two Parameters

Author

Listed:
  • Jing Qiu

    (School of Mathematical Science, Qufu Normal University, Qufu, Shandong 273165, P. R. China)

  • Jiguo Yu

    (School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250353, P. R. China3Shandong Computer Science Center (National Supercomputer Center in Jinan), Jinan, Shandong 250014, P. R. China4Shandong Laboratory of Computer Networks, Jinan 250014, P. R. China)

  • Shujun Lian

    (School of Management, Qufu Normal University, Rizhao, Shandong Province, P. R. China)

Abstract

In this paper, we propose a new non-smooth penalty function with two parameters for nonlinear inequality constrained optimization problems. And we propose a twice continuously differentiable function which is smoothing approximation to the non-smooth penalty function and define the corresponding smoothed penalty problem. A global solution of the smoothed penalty problem is proved to be an approximation global solution of the non-smooth penalty problem. Based on the smoothed penalty function, we develop an algorithm and prove that the sequence generated by the algorithm can converge to the optimal solution of the original problem.

Suggested Citation

  • Jing Qiu & Jiguo Yu & Shujun Lian, 2021. "Smoothing Approximation to the New Exact Penalty Function with Two Parameters," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 38(05), pages 1-19, October.
  • Handle: RePEc:wsi:apjorx:v:38:y:2021:i:05:n:s0217595921400108
    DOI: 10.1142/S0217595921400108
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0217595921400108
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0217595921400108?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.

    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:wsi:apjorx:v:38:y:2021:i:05:n:s0217595921400108. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/apjor/apjor.shtml .

    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.