IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v74y2019i1d10.1007_s10589-019-00105-w.html
   My bibliography  Save this article

A conjugate gradient-based algorithm for large-scale quadratic programming problem with one quadratic constraint

Author

Listed:
  • A. Taati

    (University of Guilan)

  • M. Salahi

    (University of Guilan)

Abstract

In this paper, we consider the nonconvex quadratically constrained quadratic programming (QCQP) with one quadratic constraint. By employing the conjugate gradient method, an efficient algorithm is proposed to solve QCQP that exploits the sparsity of the involved matrices and solves the problem via solving a sequence of positive definite system of linear equations after identifying suitable generalized eigenvalues. Specifically, we analyze how to recognize hard case (case 2) in a preprocessing step, fixing an error in Sect. 2.2.2 of Pong and Wolkowicz (Comput Optim Appl 58(2):273–322, 2014) which studies the same problem with the two-sided constraint. Some numerical experiments are given to show the effectiveness of the proposed method and to compare it with some recent algorithms in the literature.

Suggested Citation

  • A. Taati & M. Salahi, 2019. "A conjugate gradient-based algorithm for large-scale quadratic programming problem with one quadratic constraint," Computational Optimization and Applications, Springer, vol. 74(1), pages 195-223, September.
    • Handle: RePEc:spr:coopap:v:74:y:2019:i:1:d:10.1007_s10589-019-00105-w
    DOI: 10.1007/s10589-019-00105-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10589-019-00105-w
    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/s10589-019-00105-w?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. Ting Pong & Henry Wolkowicz, 2014. "The generalized trust region subproblem," Computational Optimization and Applications, Springer, vol. 58(2), pages 273-322, June.
    2. Joe-Mei Feng & Gang-Xuan Lin & Reuy-Lin Sheu & Yong Xia, 2012. "Duality and solutions for quadratic programming over single non-homogeneous quadratic constraint," Journal of Global Optimization, Springer, vol. 54(2), pages 275-293, October.
    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. Yong Xia & Longfei Wang & Meijia Yang, 2019. "A fast algorithm for globally solving Tikhonov regularized total least squares problem," Journal of Global Optimization, Springer, vol. 73(2), pages 311-330, February.
    2. Stefan Sremac & Fei Wang & Henry Wolkowicz & Lucas Pettersson, 2019. "Noisy Euclidean distance matrix completion with a single missing node," Journal of Global Optimization, Springer, vol. 75(4), pages 973-1002, December.
    3. Liaoyuan Zeng & Ting Kei Pong, 2022. "$$\rho$$ ρ -regularization subproblems: strong duality and an eigensolver-based algorithm," Computational Optimization and Applications, Springer, vol. 81(2), pages 337-368, March.
    4. Duy-Van Nguyen, 2022. "Strong Duality for General Quadratic Programs with Quadratic Equality Constraints," Journal of Optimization Theory and Applications, Springer, vol. 195(1), pages 297-313, October.
    5. Mengmeng Song & Yong Xia, 2023. "Calabi-Polyak convexity theorem, Yuan’s lemma and S-lemma: extensions and applications," Journal of Global Optimization, Springer, vol. 85(3), pages 743-756, March.
    6. Amaioua, Nadir & Audet, Charles & Conn, Andrew R. & Le Digabel, Sébastien, 2018. "Efficient solution of quadratically constrained quadratic subproblems within the mesh adaptive direct search algorithm," European Journal of Operational Research, Elsevier, vol. 268(1), pages 13-24.
    7. Matamala, Yolanda & Feijoo, Felipe, 2021. "A two-stage stochastic Stackelberg model for microgrid operation with chance constraints for renewable energy generation uncertainty," Applied Energy, Elsevier, vol. 303(C).
    8. Van-Bong Nguyen & Thi Ngan Nguyen & Ruey-Lin Sheu, 2020. "Strong duality in minimizing a quadratic form subject to two homogeneous quadratic inequalities over the unit sphere," Journal of Global Optimization, Springer, vol. 76(1), pages 121-135, 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:coopap:v:74:y:2019:i:1:d:10.1007_s10589-019-00105-w. 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.