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 98 articles , 1 standard article )

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  1. Cauwet, Marie-Liesse; Liu, Jialin; Rozière, Baptiste; Teytaud, Olivier: Algorithm portfolios for noisy optimization (2016)
  2. Garmanjani, R.; Júdice, D.; Vicente, L.N.: Trust-region methods without using derivatives: worst case complexity and the nonsmooth case (2016)
  3. Tröltzsch, Anke: A sequential quadratic programming algorithm for equality-constrained optimization without derivatives (2016)
  4. Wang, Jueyu; Zhu, Detong: Conjugate gradient path method without line search technique for derivative-free unconstrained optimization (2016)
  5. Audet, Charles; Le Digabel, Sébastien; Peyrega, Mathilde: Linear equalities in blackbox optimization (2015)
  6. Ferreira, Priscila S.; Karas, Elizabeth W.; Sachine, Mael: A globally convergent trust-region algorithm for unconstrained derivative-free optimization (2015)
  7. Lv, Wei; Sun, Qiang; Lin, He; Sui, Ruirui: A penalty derivative-free algorithm for nonlinear constrained optimization (2015)
  8. Newby, Eric; Ali, M.M.: A trust-region-based derivative free algorithm for mixed integer programming (2015)
  9. Sampaio, Ph.R.; Toint, Ph.L.: A derivative-free trust-funnel method for equality-constrained nonlinear optimization (2015)
  10. Yuan, Ya-xiang: Recent advances in trust region algorithms (2015)
  11. Zhou, Weijun: A globally and R-linearly convergent hybrid HS and PRP method and its inexact version with applications (2015)
  12. Teytaud, Fabien; Teytaud, Olivier: Convergence rates of evolutionary algorithms and parallel evolutionary algorithms (2014)
  13. Xue, Dan; Sun, Wenyu: On convergence analysis of a derivative-free trust region algorithm for constrained optimization with separable structure (2014)
  14. Zhang, Zaikun: Sobolev seminorm of quadratic functions with applications to derivative-free optimization (2014)
  15. Zhou, Qinghua; Geng, Yan: Revising two trust region subproblems for unconstrained derivative free methods (2014)
  16. Conejo, P.D.; Karas, E.W.; Pedroso, L.G.; Ribeiro, A.A.; Sachine, M.: Global convergence of trust-region algorithms for convex constrained minimization without derivatives (2013)
  17. Martínez, J.M.; Sobral, F.N.C.: Constrained derivative-free optimization on thin domains (2013)
  18. Rios, Luis Miguel; Sahinidis, Nikolaos V.: Derivative-free optimization: a review of algorithms and comparison of software implementations (2013)
  19. Zhao, Hui; Li, Gaoming; Reynolds, Albert C.; Yao, Jun: Large-scale history matching with quadratic interpolation models (2013)
  20. Bandeira, Michael Martin A.S.; Scheinberg, K.; Vicente, L.N.: Computation of sparse low degree interpolating polynomials and their application to derivative-free optimization (2012)

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