IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v150y2011i1d10.1007_s10957-011-9815-5.html
   My bibliography  Save this article

Outer Trust-Region Method for Constrained Optimization

Author

Listed:
  • Ernesto G. Birgin

    (University of São Paulo)

  • Emerson V. Castelani

    (University of Campinas)

  • André L. M. Martinez

    (University of Campinas)

  • J. M. Martínez

    (University of Campinas)

Abstract

Given an algorithm A for solving some mathematical problem based on the iterative solution of simpler subproblems, an outer trust-region (OTR) modification of A is the result of adding a trust-region constraint to each subproblem. The trust-region size is adaptively updated according to the behavior of crucial variables. The new subproblems should not be more complex than the original ones, and the convergence properties of the OTR algorithm should be the same as those of Algorithm A. In the present work, the OTR approach is exploited in connection with the “greediness phenomenon” of nonlinear programming. Convergence results for an OTR version of an augmented Lagrangian method for nonconvex constrained optimization are proved, and numerical experiments are presented.

Suggested Citation

  • Ernesto G. Birgin & Emerson V. Castelani & André L. M. Martinez & J. M. Martínez, 2011. "Outer Trust-Region Method for Constrained Optimization," Journal of Optimization Theory and Applications, Springer, vol. 150(1), pages 142-155, July.
  • Handle: RePEc:spr:joptap:v:150:y:2011:i:1:d:10.1007_s10957-011-9815-5
    DOI: 10.1007/s10957-011-9815-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-011-9815-5
    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/s10957-011-9815-5?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. J. M. Martínez & L. T. Santos, 1998. "New Theoretical Results on Recursive Quadratic Programming Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 97(2), pages 435-454, May.
    2. Emerson Castelani & André Martinez & J. Martínez & B. Svaiter, 2010. "Addressing the greediness phenomenon in Nonlinear Programming by means of Proximal Augmented Lagrangians," Computational Optimization and Applications, Springer, vol. 46(2), pages 229-245, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. E. Birgin & J. Martínez & L. Prudente, 2014. "Augmented Lagrangians with possible infeasibility and finite termination for global nonlinear programming," Journal of Global Optimization, Springer, vol. 58(2), pages 207-242, February.
    2. E. G. Birgin & G. Haeser & A. Ramos, 2018. "Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points," Computational Optimization and Applications, Springer, vol. 69(1), pages 51-75, January.
    3. E. G. Birgin & L. F. Bueno & J. M. Martínez, 2016. "Sequential equality-constrained optimization for nonlinear programming," Computational Optimization and Applications, Springer, vol. 65(3), pages 699-721, December.

    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. E. G. Birgin & L. F. Bueno & J. M. Martínez, 2016. "Sequential equality-constrained optimization for nonlinear programming," Computational Optimization and Applications, Springer, vol. 65(3), pages 699-721, December.
    2. Dominique Orban & Abel Soares Siqueira, 2020. "A regularization method for constrained nonlinear least squares," Computational Optimization and Applications, Springer, vol. 76(3), pages 961-989, July.
    3. E. Birgin & J. Martínez & L. Prudente, 2014. "Augmented Lagrangians with possible infeasibility and finite termination for global nonlinear programming," Journal of Global Optimization, Springer, vol. 58(2), pages 207-242, February.
    4. E. G. Birgin & G. Haeser & A. Ramos, 2018. "Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points," Computational Optimization and Applications, Springer, vol. 69(1), pages 51-75, January.

    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:150:y:2011:i:1:d:10.1007_s10957-011-9815-5. 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.