IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v98y2000i1p215-23410.1023-a1019260508329.html
   My bibliography  Save this article

Computational Discretization Algorithms for Functional Inequality Constrained Optimization

Author

Listed:
  • K.L. Teo
  • X.Q. Yang
  • L.S. Jennings

Abstract

In this paper, a functional inequality constrained optimization problem is studied using a discretization method and an adaptive scheme. The problem is discretized by partitioning the interval of the independent parameter. Two methods are investigated as to how to treat the discretized optimization problem. The discretization problem is firstly converted into an optimization problem with a single nonsmooth equality constraint. Since the obtained equality constraint is nonsmooth and does not satisfy the usual constraint qualification condition, relaxation and smoothing techniques are used to approximate the equality constraint via a smooth inequality constraint. This leads to a sequence of approximate smooth optimization problems with one constraint. An adaptive scheme is incorporated into the method to facilitate the computation of the sum in the inequality constraint. The second method is to apply an adaptive scheme directly to the discretization problem. Thus a sequence of optimization problems with a small number of inequality constraints are obtained. Convergence analysis for both methods is established. Numerical examples show that each of the two proposed methods has its own advantages and disadvantages over the other. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • K.L. Teo & X.Q. Yang & L.S. Jennings, 2000. "Computational Discretization Algorithms for Functional Inequality Constrained Optimization," Annals of Operations Research, Springer, vol. 98(1), pages 215-234, December.
    • Handle: RePEc:spr:annopr:v:98:y:2000:i:1:p:215-234:10.1023/a:1019260508329
    DOI: 10.1023/A:1019260508329
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1019260508329
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1019260508329?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. 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.
    2. Ping Jin & Chen Ling & Huifei Shen, 2015. "A smoothing Levenberg–Marquardt algorithm for semi-infinite programming," Computational Optimization and Applications, Springer, vol. 60(3), pages 675-695, April.
    3. Li-Ping Pang & Jian Lv & Jin-He Wang, 2016. "Constrained incremental bundle method with partial inexact oracle for nonsmooth convex semi-infinite programming problems," Computational Optimization and Applications, Springer, vol. 64(2), pages 433-465, June.
    4. Mengwei Xu & Soon-Yi Wu & Jane Ye, 2014. "Solving semi-infinite programs by smoothing projected gradient method," Computational Optimization and Applications, Springer, vol. 59(3), pages 591-616, December.
    5. C. Ling & L. Q. Qi & G. L. Zhou & S. Y. Wu, 2006. "Global Convergence of a Robust Smoothing SQP Method for Semi-Infinite Programming," Journal of Optimization Theory and Applications, Springer, vol. 129(1), pages 147-164, April.
    6. Liping Zhang & Soon-Yi Wu, 2011. "A new approach to the weighted peak-constrained least-square error FIR digital filter optimal design problem," Computational Optimization and Applications, Springer, vol. 50(2), pages 445-461, October.
    7. Ting-Jang Shiu & Soon-Yi Wu, 2012. "Relaxed cutting plane method with convexification for solving nonlinear semi-infinite programming problems," Computational Optimization and Applications, Springer, vol. 53(1), pages 91-113, September.
    8. Chen Ling & Qin Ni & Liqun Qi & Soon-Yi Wu, 2010. "A new smoothing Newton-type algorithm for semi-infinite programming," Journal of Global Optimization, Springer, vol. 47(1), pages 133-159, May.
    9. Bo Wei & William B. Haskell & Sixiang Zhao, 2020. "The CoMirror algorithm with random constraint sampling for convex semi-infinite programming," Annals of Operations Research, Springer, vol. 295(2), pages 809-841, December.
    10. Xiaojiao Tong & Soon-Yi Wu & Renjun Zhou, 2010. "New approach for the nonlinear programming with transient stability constraints arising from power systems," Computational Optimization and Applications, Springer, vol. 45(3), pages 495-520, April.

    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:annopr:v:98:y:2000:i:1:p:215-234:10.1023/a:1019260508329. 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.