DFO is a Fortran package for solving general nonlinear optimization problems that have the following characteristics: they are relatively small scale (less than 100 variables), their objective function is relatively expensive to compute and derivatives of such functions are not available and cannot be estimated efficiently. There also may be some noise in the function evaluation procedures. Such optimization problems arise ,for example, in engineering design, where the objective function evaluation is a simulation package treated as a black box.

References in zbMATH (referenced in 125 articles , 1 standard article )

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  1. Li, Zhongguo; Dong, Zhen; Liang, Zhongchao; Ding, Zhengtao: Surrogate-based distributed optimisation for expensive black-box functions (2021)
  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. Hare, Warren: A discussion on variational analysis in derivative-free optimization (2020)
  4. Hare, Warren; Planiden, Chayne; Sagastizábal, Claudia: A derivative-free (\mathcalV\mathcalU)-algorithm for convex finite-max problems (2020)
  5. Manno, Andrea; Amaldi, Edoardo; Casella, Francesco; Martelli, Emanuele: A local search method for costly black-box problems and its application to CSP plant start-up optimization refinement (2020)
  6. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  7. Xi, Min; Sun, Wenyu; Chen, Jun: Survey of derivative-free optimization (2020)
  8. Xi, Min; Sun, Wenyu; Chen, Yannan; Sun, Hailin: A derivative-free algorithm for spherically constrained optimization (2020)
  9. Berahas, Albert S.; Byrd, Richard H.; Nocedal, Jorge: Derivative-free optimization of noisy functions via quasi-Newton methods (2019)
  10. Cartis, Coralia; Roberts, Lindon: A derivative-free Gauss-Newton method (2019)
  11. Falini, Antonella; Jüttler, Bert: THB-splines multi-patch parameterization for multiply-connected planar domains via template segmentation (2019)
  12. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  13. Wang, Peng; Zhu, Detong; Song, Yufeng: Derivative-free feasible backtracking search methods for nonlinear multiobjective optimization with simple boundary constraint (2019)
  14. Costa, Alberto; Nannicini, Giacomo: RBFOpt: an open-source library for black-box optimization with costly function evaluations (2018)
  15. Maggiar, Alvaro; Wächter, Andreas; Dolinskaya, Irina S.; Staum, Jeremy: A derivative-free trust-region algorithm for the optimization of functions smoothed via Gaussian convolution using adaptive multiple importance sampling (2018)
  16. Zhou, Zhe; Bai, Fusheng: An adaptive framework for costly black-box global optimization based on radial basis function interpolation (2018)
  17. Echebest, N.; Schuverdt, M. L.; Vignau, R. P.: An inexact restoration derivative-free filter method for nonlinear programming (2017)
  18. Fang, Xiaowei; Ni, Qin: A frame-based conjugate gradients direct search method with radial basis function interpolation model (2017)
  19. Hare, W.: Compositions of convex functions and fully linear models (2017)
  20. Rahmanpour, Fardin; Hosseini, Mohammad Mehdi; Maalek Ghaini, Farid Mohammad: Penalty-free method for nonsmooth constrained optimization via radial basis functions (2017)

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