IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v92y1997i3d10.1023_a1022655423083.html
   My bibliography  Save this article

Robust Recursive Quadratic Programming Algorithm Model with Global and Superlinear Convergence Properties

Author

Listed:
  • F. Facchinei

    (Università di Roma “La Sapienza,”)

Abstract

A new, robust recursive quadratic programming algorithm model based on a continuously differentiable merit function is introduced. The algorithm is globally and superlinearly convergent, uses automatic rules for choosing the penalty parameter, and can efficiently cope with the possible inconsistency of the quadratic search subproblem. The properties of the algorithm are studied under weak a priori assumptions; in particular, the superlinear convergence rate is established without requiring strict complementarity. The behavior of the algorithm is also investigated in the case where not all of the assumptions are met. The focus of the paper is on theoretical issues; nevertheless, the analysis carried out and the solutions proposed pave the way to new and more robust RQP codes than those presently available.

Suggested Citation

  • F. Facchinei, 1997. "Robust Recursive Quadratic Programming Algorithm Model with Global and Superlinear Convergence Properties," Journal of Optimization Theory and Applications, Springer, vol. 92(3), pages 543-579, March.
    • Handle: RePEc:spr:joptap:v:92:y:1997:i:3:d:10.1023_a:1022655423083
    DOI: 10.1023/A:1022655423083
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/A:1022655423083
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1023/A:1022655423083?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adam B. Levy, 2005. "Convergence of Successive Approximation Methods with Parameter Target Sets," Mathematics of Operations Research, INFORMS, vol. 30(3), pages 765-784, August.
    2. J.L. Zhang & X.S. Zhang, 2003. "Sequential Penalty Algorithm for Nonlinear Constrained Optimization," Journal of Optimization Theory and Applications, Springer, vol. 118(3), pages 635-655, September.
    3. Francisco Facchinei & Vyacheslav Kungurtsev & Lorenzo Lampariello & Gesualdo Scutari, 2021. "Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity," Mathematics of Operations Research, INFORMS, vol. 46(2), pages 595-627, May.
    4. L. Qi & Y.F. Yang, 2002. "Globally and Superlinearly Convergent QP-Free Algorithm for Nonlinear Constrained Optimization," Journal of Optimization Theory and Applications, Springer, vol. 113(2), pages 297-323, May.
    5. Chen, M. J. & Huang, G. H., 2001. "A derivative algorithm for inexact quadratic program - application to environmental decision-making under uncertainty," European Journal of Operational Research, Elsevier, vol. 128(3), pages 570-586, 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:joptap:v:92:y:1997:i:3:d:10.1023_a:1022655423083. 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: 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.