IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v164y2015i3d10.1007_s10957-014-0617-4.html
   My bibliography  Save this article

Derivative-Free Methods for Mixed-Integer Constrained Optimization Problems

Author

Listed:
  • Giampaolo Liuzzi

    (CNR)

  • Stefano Lucidi

    (“Sapienza” Università di Roma)

  • Francesco Rinaldi

    (Università di Padova)

Abstract

Methods which do not use any derivative information are becoming popular among researchers, since they allow to solve many real-world engineering problems. Such problems are frequently characterized by the presence of discrete variables, which can further complicate the optimization process. In this paper, we propose derivative-free algorithms for solving continuously differentiable Mixed Integer NonLinear Programming problems with general nonlinear constraints and explicit handling of bound constraints on the problem variables. We use an exterior penalty approach to handle the general nonlinear constraints and a local search approach to take into account the presence of discrete variables. We show that the proposed algorithms globally converge to points satisfying different necessary optimality conditions. We report a computational experience and a comparison with a well-known derivative-free optimization software package, i.e., NOMAD, on a set of test problems. Furthermore, we employ the proposed methods and NOMAD to solve a real problem concerning the optimal design of an industrial electric motor. This allows to show that the method converging to the better extended stationary points obtains the best solution also from an applicative point of view.

Suggested Citation

  • Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2015. "Derivative-Free Methods for Mixed-Integer Constrained Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 164(3), pages 933-965, March.
    • Handle: RePEc:spr:joptap:v:164:y:2015:i:3:d:10.1007_s10957-014-0617-4
    DOI: 10.1007/s10957-014-0617-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-014-0617-4
    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/s10957-014-0617-4?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. C. J. Lin & S. Lucidi & L. Palagi & A. Risi & M. Sciandrone, 2009. "Decomposition Algorithm Model for Singly Linearly-Constrained Problems Subject to Lower and Upper Bounds," Journal of Optimization Theory and Applications, Springer, vol. 141(1), pages 107-126, April.
    2. G. Liuzzi & S. Lucidi & F. Rinaldi, 2012. "Derivative-free methods for bound constrained mixed-integer optimization," Computational Optimization and Applications, Springer, vol. 53(2), pages 505-526, October.
    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. Ubaldo M. García Palomares, 2023. "Convergence of derivative-free nonmonotone Direct Search Methods for unconstrained and box-constrained mixed-integer optimization," Computational Optimization and Applications, Springer, vol. 85(3), pages 821-856, July.
    2. Juliane Müller & Joshua D. Woodbury, 2017. "GOSAC: global optimization with surrogate approximation of constraints," Journal of Global Optimization, Springer, vol. 69(1), pages 117-136, September.
    3. Jeffrey Larson & Sven Leyffer & Prashant Palkar & Stefan M. Wild, 2021. "A method for convex black-box integer global optimization," Journal of Global Optimization, Springer, vol. 80(2), pages 439-477, June.
    4. Nikolaos Ploskas & Nikolaos V. Sahinidis, 2022. "Review and comparison of algorithms and software for mixed-integer derivative-free optimization," Journal of Global Optimization, Springer, vol. 82(3), pages 433-462, March.
    5. Boukouvala, Fani & Misener, Ruth & Floudas, Christodoulos A., 2016. "Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO," European Journal of Operational Research, Elsevier, vol. 252(3), pages 701-727.

    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. Tommaso Giovannelli & Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2022. "Derivative-free methods for mixed-integer nonsmooth constrained optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 293-327, June.
    2. Andrea Manno & Laura Palagi & Simone Sagratella, 2018. "Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training," Computational Optimization and Applications, Springer, vol. 71(1), pages 115-145, September.
    3. Veronica Piccialli & Marco Sciandrone, 2022. "Nonlinear optimization and support vector machines," Annals of Operations Research, Springer, vol. 314(1), pages 15-47, July.
    4. P. Tseng & S. Yun, 2009. "Block-Coordinate Gradient Descent Method for Linearly Constrained Nonsmooth Separable Optimization," Journal of Optimization Theory and Applications, Springer, vol. 140(3), pages 513-535, March.
    5. Leonardo Galli & Alessandro Galligari & Marco Sciandrone, 2020. "A unified convergence framework for nonmonotone inexact decomposition methods," Computational Optimization and Applications, Springer, vol. 75(1), pages 113-144, January.
    6. L. Grippo & F. Rinaldi, 2015. "A class of derivative-free nonmonotone optimization algorithms employing coordinate rotations and gradient approximations," Computational Optimization and Applications, Springer, vol. 60(1), pages 1-33, January.
    7. Veronica Piccialli & Marco Sciandrone, 2018. "Nonlinear optimization and support vector machines," 4OR, Springer, vol. 16(2), pages 111-149, June.
    8. Giampaolo Liuzzi & Laura Palagi & Mauro Piacentini, 2010. "On the convergence of a Jacobi-type algorithm for Singly Linearly-Constrained Problems Subject to simple Bounds," DIS Technical Reports 2010-01, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    9. Dellepiane, Umberto & Palagi, Laura, 2015. "Using SVM to combine global heuristics for the Standard Quadratic Problem," European Journal of Operational Research, Elsevier, vol. 241(3), pages 596-605.
    10. Cassioli, A. & Di Lorenzo, D. & Sciandrone, M., 2013. "On the convergence of inexact block coordinate descent methods for constrained optimization," European Journal of Operational Research, Elsevier, vol. 231(2), pages 274-281.
    11. G. Liuzzi & S. Lucidi & F. Rinaldi, 2012. "Derivative-free methods for bound constrained mixed-integer optimization," Computational Optimization and Applications, Springer, vol. 53(2), pages 505-526, October.
    12. Boukouvala, Fani & Misener, Ruth & Floudas, Christodoulos A., 2016. "Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO," European Journal of Operational Research, Elsevier, vol. 252(3), pages 701-727.
    13. Nikolaos Ploskas & Nikolaos V. Sahinidis, 2022. "Review and comparison of algorithms and software for mixed-integer derivative-free optimization," Journal of Global Optimization, Springer, vol. 82(3), pages 433-462, March.
    14. Paul Tseng & Sangwoon Yun, 2010. "A coordinate gradient descent method for linearly constrained smooth optimization and support vector machines training," Computational Optimization and Applications, Springer, vol. 47(2), pages 179-206, October.
    15. Ion Necoara & Andrei Patrascu, 2014. "A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints," Computational Optimization and Applications, Springer, vol. 57(2), pages 307-337, March.
    16. Andrei Patrascu & Ion Necoara, 2015. "Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization," Journal of Global Optimization, Springer, vol. 61(1), pages 19-46, January.
    17. G. Cocchi & G. Liuzzi & A. Papini & M. Sciandrone, 2018. "An implicit filtering algorithm for derivative-free multiobjective optimization with box constraints," Computational Optimization and Applications, Springer, vol. 69(2), pages 267-296, March.
    18. Stefano Lucidi & Massimo Maurici & Luca Paulon & Francesco Rinaldi & Massimo Roma, 2014. "A derivative-free approach for a simulation-based optimization problem in healthcare," DIAG Technical Reports 2014-15, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    19. Angelo Ciccazzo & Vittorio Latorre & Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2015. "Derivative-Free Robust Optimization for Circuit Design," Journal of Optimization Theory and Applications, Springer, vol. 164(3), pages 842-861, March.
    20. Angelo Ciccazzo & Gianni Di Pillo & Vittorio Latorre, 2015. "A SVM Surrogate Model Based Method for Yield Optimization in Electronic Circuit Design," DIAG Technical Reports 2015-03, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

    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:164:y:2015:i:3:d:10.1007_s10957-014-0617-4. 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.