DFO

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 127 articles , 1 standard article )

Showing results 81 to 100 of 127.
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  1. Harth, Zerrin; Sun, Hongtao; Schäfer, Michael: Comparison of derivative free Newton-based and evolutionary methods for shape optimization of flow problems (2007)
  2. Karasözen, Bülent: Survey of trust-region derivative free optimization methods (2007)
  3. Regis, Rommel G.; Shoemaker, Christine A.: A stochastic radial basis function method for the global optimization of expensive functions (2007)
  4. Regis, Rommel G.; Shoemaker, Christine A.: Improved strategies for radial basis function methods for global optimization (2007)
  5. Regis, Rommel G.; Shoemaker, Christine A.: Parallel radial basis function methods for the global optimization of expensive functions (2007)
  6. Zhou, Qing-Hua: On the use of simplex methods in constructing quadratic models (2007)
  7. Bagirov, Adil M.; Ghosh, Moumita; Webb, Dean: A derivative-free method for linearly constrained nonsmooth optimization (2006)
  8. Driessen, Lonneke; Brekelmans, Ruud; Hamers, Herbert; den Hertog, Dick: On (D)-optimality based trust regions for black-box optimization problems (2006)
  9. Hirschen, K.; Schäfer, M.: Bayesian regularization neural networks for optimizing fluid flow processes (2006)
  10. Schäfer, M.; Karasözen, B.; Uğur, Ö.; Yapıcı, K.: Derivative free optimization of stirrer configurations (2006)
  11. Wan, Zailong; Igusa, Takeru: Statistics of Nadaraya-Watson estimator errors in surrogate-based optimization (2006)
  12. Brekelmans, Ruud; Driessen, Lonneke; Hamers, Herbert; den Hertog, Dick: Constrained optimization involving expensive function evaluations: A sequential approach (2005)
  13. Colson, Benoît; Toint, Philippe L.: Optimizing partially separable functions without derivatives (2005)
  14. Lee, Kang Seok; Geem, Zong Woo: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice (2005)
  15. Lehnhäuser, T.; Schäfer, M.: A numerical approach for shape optimization of fluid flow domains (2005)
  16. Regis, Rommel G.; Shoemaker, Christine A.: Constrained global optimization of expensive black box functions using radial basis functions (2005)
  17. Vanden Berghen, Frank; Bersini, Hugues: CONDOR, a new parallel, constrained extension of Powell’s UOBYQA algorithm: Experimental results and comparison with the DFO algorithm (2005)
  18. Zhang, Chunlei; Sheng, Qin; Ordóñez, Raúl: Notes on the convergence and applications of surrogate optimization (2005)
  19. Alberto, Pedro; Nogueira, Fernando; Rocha, Humberto; Vicente, Luís N.: Pattern search methods for user-provided points: application to molecular geometry problems (2004)
  20. Han, Lixing; Liu, Guanghui: On the convergence of the UOBYQA method (2004)