BOBYQA

This Fortran software seeks the least value of a function of several variables without requiring any derivatives of the objective function. It was developed from my NEWUOA package for this calculation in the unconstrained case. The main new feature of BOBYQA, however, is that it allows lower and upper bounds on each variable. The name BOBYQA denotes Bound Optimization BY Quadratic Approximation. Please send an e-mail to me at mjdp-at-cam.ac.uk if you would like to receive a free copy of the Fortran software. As far as I know BOBYQA is the most powerful package available at present for minimizing functions of hundreds of variables without derivatives subject to simple bound constraints. There are no restrictions on its use. I would be delighted if it becomes valuable to much research and many applications


References in zbMATH (referenced in 60 articles )

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  1. Bajaj, Ishan; Faruque Hasan, M. M.: Deterministic global derivative-free optimization of black-box problems with bounded Hessian (2020)
  2. Gumma, E. A. E.; Ali, M. Montaz; Hashim, M. H. A.: A derivative-free algorithm for non-linear optimization with linear equality constraints (2020)
  3. Meyer, Knut Andreas; Ekh, Magnus; Ahlström, Johan: Anisotropic yield surfaces after large shear deformations in pearlitic steel (2020)
  4. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  5. Bittrich, Lars; Spickenheuer, Axel; Almeida, José Humberto S.; Müller, Sascha; Kroll, Lothar; Heinrich, Gert: Optimizing variable-axial fiber-reinforced composite laminates: the direct fiber path optimization concept (2019)
  6. Cartis, Coralia; Fiala, Jan; Marteau, Benjamin; Roberts, Lindon: Improving the flexibility and robustness of model-based derivative-free optimization solvers (2019)
  7. Cartis, Coralia; Roberts, Lindon: A derivative-free Gauss-Newton method (2019)
  8. García-Palomares, Ubaldo M.; Rodríguez-Hernández, Pedro S.: Unified approach for solving box-constrained models with continuous or discrete variables by non monotone direct search methods (2019)
  9. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  10. Miyaoka, Tiago Yuzo; Lenhart, Suzanne; Meyer, João F. C. A.: Optimal control of vaccination in a vector-Borne reaction -- diffusion model applied to Zika virus (2019)
  11. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  12. Takezawa, Masahito; Matsuo, Kohei; Maekawa, Takashi: Control of lines of curvature for plate forming in shipbuilding (2019)
  13. Wang, Peng; Zhu, Detong; Song, Yufeng: Derivative-free feasible backtracking search methods for nonlinear multiobjective optimization with simple boundary constraint (2019)
  14. Abdelhamid, Talaat; Elsheikh, A. H.; Elazab, Ahmed; Sharshir, S. W.; Selima, Ehab S.; Jiang, Daijun: Simultaneous reconstruction of the time-dependent Robin coefficient and heat flux in heat conduction problems (2018)
  15. Costa, Alberto; Nannicini, Giacomo: RBFOpt: an open-source library for black-box optimization with costly function evaluations (2018)
  16. Gerbeau, Jean-Frédéric; Lombardi, Damiano; Tixier, Eliott: How to choose biomarkers in view of parameter estimation (2018)
  17. Karban, Pavel; Kropík, Petr; Kotlan, Václav; Doležel, Ivo: Bayes approach to solving T.E.A.M. benchmark problems 22 and 25 and its comparison with other optimization techniques (2018)
  18. Kieslich, Chris A.; Boukouvala, Fani; Floudas, Christodoulos A.: Optimization of black-box problems using Smolyak grids and polynomial approximations (2018)
  19. Larson, Jeffrey; Wild, Stefan M.: Asynchronously parallel optimization solver for finding multiple minima (2018)
  20. Boukouvala, Fani; Faruque Hasan, M. M.; Floudas, Christodoulos A.: Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption (2017)

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