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Optimizing radial basis functions by d.c. programming and its use in direct search for global derivative-free optimization

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  • H. Le Thi
  • A. Vaz
  • L. Vicente

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  • H. Le Thi & A. Vaz & L. Vicente, 2012. "Optimizing radial basis functions by d.c. programming and its use in direct search for global derivative-free optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 190-214, April.
    • Handle: RePEc:spr:topjnl:v:20:y:2012:i:1:p:190-214
    DOI: 10.1007/s11750-011-0193-9
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    References listed on IDEAS

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    1. L. Ingber & B. Rosen, 1992. "Genetic algorithms and very fast simulated reannealing: A comparison," Lester Ingber Papers 92ga, Lester Ingber.
    2. Le An & Pham Tao, 2005. "The DC (Difference of Convex Functions) Programming and DCA Revisited with DC Models of Real World Nonconvex Optimization Problems," Annals of Operations Research, Springer, vol. 133(1), pages 23-46, January.
    3. Rommel Regis & Christine Shoemaker, 2005. "Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions," Journal of Global Optimization, Springer, vol. 31(1), pages 153-171, January.
    4. Robert Fourer & David M. Gay & Brian W. Kernighan, 1990. "A Modeling Language for Mathematical Programming," Management Science, INFORMS, vol. 36(5), pages 519-554, May.
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    Cited by:

    1. Charles Audet & Sébastien Le Digabel & Renaud Saltet, 2022. "Quantifying uncertainty with ensembles of surrogates for blackbox optimization," Computational Optimization and Applications, Springer, vol. 83(1), pages 29-66, September.
    2. Chris A. Kieslich & Fani Boukouvala & Christodoulos A. Floudas, 2018. "Optimization of black-box problems using Smolyak grids and polynomial approximations," Journal of Global Optimization, Springer, vol. 71(4), pages 845-869, August.
    3. 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.
    4. Charles Audet & Michael Kokkolaras & Sébastien Le Digabel & Bastien Talgorn, 2018. "Order-based error for managing ensembles of surrogates in mesh adaptive direct search," Journal of Global Optimization, Springer, vol. 70(3), pages 645-675, March.
    5. Davide Previtali & Mirko Mazzoleni & Antonio Ferramosca & Fabio Previdi, 2023. "GLISp-r: a preference-based optimization algorithm with convergence guarantees," Computational Optimization and Applications, Springer, vol. 86(1), pages 383-420, September.

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