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 76 articles )

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  1. Birgin, E. G.; Martínez, J. M.: Accelerated derivative-free nonlinear least-squares applied to the estimation of Manning coefficients (2022)
  2. Ploskas, Nikolaos; Sahinidis, Nikolaos V.: Review and comparison of algorithms and software for mixed-integer derivative-free optimization (2022)
  3. Braglia, Matteo; Chen, Xingang; Hazra, Dhiraj Kumar: Comparing multi-field primordial feature models with the Planck data (2021)
  4. Browning, Alexander P.; Maclaren, Oliver J.; Buenzli, Pascal R.; Lanaro, Matthew; Allenby, Mark C.; Woodruff, Maria A.; Simpson, Matthew J.: Model-based data analysis of tissue growth in thin 3D printed scaffolds (2021)
  5. Hebbal, Ali; Brevault, Loïc; Balesdent, Mathieu; Talbi, El-Ghazali; Melab, Nouredine: Bayesian optimization using deep Gaussian processes with applications to aerospace system design (2021)
  6. Jones, Donald R.; Martins, Joaquim R. R. A.: The DIRECT algorithm: 25 years later (2021)
  7. Lakhmiri, Dounia; Digabel, Sébastien Le; Tribes, Christophe: HyperNOMAD. Hyperparameter optimization of deep neural networks using mesh adaptive direct search (2021)
  8. Petrasova, Iveta; Kotlan, Vaclav; Panek, David; Dolezel, Ivo: Methodology of determining material parameters based on optimization techniques (2021)
  9. Saras M. Windecker, Peter A. Vesk, Stacey M. Trevathan-Tackett, Nick Golding: mixchar: An R Package for the Deconvolution of Thermal Decay Curves (2021) not zbMATH
  10. Bajaj, Ishan; Faruque Hasan, M. M.: Deterministic global derivative-free optimization of black-box problems with bounded Hessian (2020)
  11. Beliakov, G.; Gagolewski, M.; James, S.: DC optimization for constructing discrete Sugeno integrals and learning nonadditive measures (2020)
  12. Gumma, E. A. E.; Ali, M. Montaz; Hashim, M. H. A.: A derivative-free algorithm for non-linear optimization with linear equality constraints (2020)
  13. Hare, Warren: A discussion on variational analysis in derivative-free optimization (2020)
  14. Meyer, Knut Andreas; Ekh, Magnus; Ahlström, Johan: Anisotropic yield surfaces after large shear deformations in pearlitic steel (2020)
  15. Paradezhenko, G. V.; Melnikov, N. B.; Reser, B. I.: Numerical continuation method for nonlinear system of scalar and functional equations (2020)
  16. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  17. Xi, Min; Sun, Wenyu; Chen, Jun: Survey of derivative-free optimization (2020)
  18. 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)
  19. Cartis, Coralia; Fiala, Jan; Marteau, Benjamin; Roberts, Lindon: Improving the flexibility and robustness of model-based derivative-free optimization solvers (2019)
  20. Cartis, Coralia; Roberts, Lindon: A derivative-free Gauss-Newton method (2019)

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