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A progressive barrier derivative-free trust-region algorithm for constrained optimization

Author

Listed:
  • Charles Audet

    (École Polytechnique de Montréal)

  • Andrew R. Conn

    (IBM T J Watson Research Center)

  • Sébastien Le Digabel

    (École Polytechnique de Montréal)

  • Mathilde Peyrega

    (École Polytechnique de Montréal)

Abstract

We study derivative-free constrained optimization problems and propose a trust-region method that builds linear or quadratic models around the best feasible and around the best infeasible solutions found so far. These models are optimized within a trust region, and the progressive barrier methodology handles the constraints by progressively pushing the infeasible solutions toward the feasible domain. Computational experiments on 40 smooth constrained problems indicate that the proposed method is competitive with COBYLA, and experiments on two nonsmooth multidisciplinary optimization problems from mechanical engineering show that it can be competitive with the NOMAD software.

Suggested Citation

  • Charles Audet & Andrew R. Conn & Sébastien Le Digabel & Mathilde Peyrega, 2018. "A progressive barrier derivative-free trust-region algorithm for constrained optimization," Computational Optimization and Applications, Springer, vol. 71(2), pages 307-329, November.
    • Handle: RePEc:spr:coopap:v:71:y:2018:i:2:d:10.1007_s10589-018-0020-4
    DOI: 10.1007/s10589-018-0020-4
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    References listed on IDEAS

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    1. Nicholas Gould & Dominique Orban & Philippe Toint, 2015. "CUTEst: a Constrained and Unconstrained Testing Environment with safe threads for mathematical optimization," Computational Optimization and Applications, Springer, vol. 60(3), pages 545-557, April.
    2. E. Gumma & M. Hashim & M. Ali, 2014. "A derivative-free algorithm for linearly constrained optimization problems," Computational Optimization and Applications, Springer, vol. 57(3), pages 599-621, April.
    3. Ph. Sampaio & Ph. Toint, 2015. "A derivative-free trust-funnel method for equality-constrained nonlinear optimization," Computational Optimization and Applications, Springer, vol. 61(1), pages 25-49, May.
    4. Charles Audet & J. Dennis & Sébastien Digabel, 2010. "Globalization strategies for Mesh Adaptive Direct Search," Computational Optimization and Applications, Springer, vol. 46(2), pages 193-215, June.
    5. Charles Audet & Sébastien Le Digabel & Mathilde Peyrega, 2015. "Linear equalities in blackbox optimization," Computational Optimization and Applications, Springer, vol. 61(1), pages 1-23, May.
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    Cited by:

    1. Pooriya Beyhaghi & Ryan Alimo & Thomas Bewley, 2020. "A derivative-free optimization algorithm for the efficient minimization of functions obtained via statistical averaging," Computational Optimization and Applications, Springer, vol. 76(1), pages 1-31, May.

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