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 129 articles , 1 standard article )

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  1. Alberto, Pedro; Nogueira, Fernando; Rocha, Humberto; Vicente, Luís N.: Pattern search methods for user-provided points: application to molecular geometry problems (2004)
  2. Han, Lixing; Liu, Guanghui: On the convergence of the UOBYQA method (2004)
  3. Powell, M. J. D.: Least Frobenius norm updating of quadratic models that satisfy interpolation conditions (2004)
  4. Qin, Ni; Shuhua, Hu: A new derivative free optimization method based on conic interpolation model (2004)
  5. Bagirov, A. M.: Continuous subdifferential approximations and their applications (2003)
  6. Kolda, Tamara G.; Lewis, Robert Michael; Torczon, Virginia: Optimization by direct search: New perspectives on some Classical and modern methods (2003)
  7. Powell, M. J. D.: On trust region methods for unconstrained minimization without derivatives (2003)
  8. Price, C. J.; Coope, I. D.: Frame-based ray search algorithms in unconstrained optimization (2003)
  9. den Hertog, Dick; de Klerk, Etienne; Roos, Kees: On convex quadratic approximation. (2002)
  10. García-Palomares, U. M.; Rodríguez, J. F.: New sequential and parallel derivative-free algorithms for unconstrained minimization (2002)
  11. Lucidi, Stefano; Sciandrone, Marco: On the global convergence of derivative-free methods for unconstrained optimization (2002)
  12. Lucidi, Stefano; Sciandrone, Marco: A derivative-free algorithm for bound constrained optimization (2002)
  13. Powell, M. J. D.: UOBYQA: unconstrained optimization by quadratic approximation (2002)
  14. Carter, R. G.; Gablonsky, J. M.; Patrick, A.; Kelley, C. T.; Eslinger, O. J.: Algorithms for noisy problems in gas transmission pipeline optimization (2001)
  15. Colson, Benoît; Toint, Philippe L.: Exploiting band structure in unconstrained optimization without derivatives (2001)
  16. Coope, I. D.; Price, C. J.: On the convergence of grid-based methods for unconstrained optimization (2001)
  17. Liu, W.; Dai, Y. H.: Minimization algorithms based on supervisor and searcher cooperation (2001)
  18. Okano, Hiroyuki; Koda, Masato: An optimization algorithm based on stochastic sensitivity analysis for noisy objective landscapes (2001)
  19. Powell, M. J. D.: On the Lagrange functions of quadratic models that are defined by interpolation (2001)
  20. van der Lee, Patrick E. A.; Terlaky, Tamás; Woudstra, Theo: A new approach to optimizing energy systems (2001)