Software for Derivative-free Unconstrained Nonlinear Optimization. WEDGE is designed for solving problems in which the objective function is smooth and the number of variables is moderate, but derivativesare not available. The method generates a model that interpolates the objective function at a set of sample points, and uses trust regions to promote convergence. A geometric constraint (or wedge) aims at keeping the sample points non-degenerate at each iteration. The code has been developed at the Optimization Center, a joint venture of Argonne National Laboratory and Northwestern University.

References in zbMATH (referenced in 40 articles , 1 standard article )

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  1. Chu, Fei; Cheng, Xiang; Peng, Chuang; Jia, Runda; Chen, Tao; Wei, Qinglai: A process transfer model-based optimal compensation control strategy for batch process using just-in-time learning and trust region method (2021)
  2. Xi, Min; Sun, Wenyu; Chen, Yannan; Sun, Hailin: A derivative-free algorithm for spherically constrained optimization (2020)
  3. Berahas, Albert S.; Byrd, Richard H.; Nocedal, Jorge: Derivative-free optimization of noisy functions via quasi-Newton methods (2019)
  4. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  5. Wang, Peng; Zhu, Detong; Song, Yufeng: Derivative-free feasible backtracking search methods for nonlinear multiobjective optimization with simple boundary constraint (2019)
  6. 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)
  7. Chen, Lu-Hung; Jiang, Ci-Ren: Multi-dimensional functional principal component analysis (2017)
  8. Wang, Peng; Zhu, Detong: An inexact derivative-free Levenberg-Marquardt method for linear inequality constrained nonlinear systems under local error bound conditions (2016)
  9. Arouxét, Ma. Belén; Echebest, Nélida E.; Pilotta, Elvio A.: Inexact restoration method for nonlinear optimization without derivatives (2015)
  10. Yuan, Jinyun; Sampaio, Raimundo; Sun, Wenyu; Zhang, Liang: A wedge trust region method with self-correcting geometry for derivative-free optimization (2015)
  11. Yuan, Ya-xiang: Recent advances in trust region algorithms (2015)
  12. Zhang, Zaikun: Sobolev seminorm of quadratic functions with applications to derivative-free optimization (2014)
  13. Zhao, Hui; Li, Gaoming; Reynolds, Albert C.; Yao, Jun: Large-scale history matching with quadratic interpolation models (2013)
  14. Zhang, Hongchao; Conn, Andrew R.: On the local convergence of a derivative-free algorithm for least-squares minimization (2012)
  15. Arouxét, Ma. Belén; Echebest, Nélida; Pilotta, Elvio A.: Active-set strategy in Powell’s method for optimization without derivatives (2011)
  16. Gratton, Serge; Toint, Philippe L.; Tröltzsch, Anke: An active-set trust-region method for derivative-free nonlinear bound-constrained optimization (2011)
  17. Wild, Stefan M.; Shoemaker, Christine: Global convergence of radial basis function trust region derivative-free algorithms (2011)
  18. Scheinberg, K.; Toint, Ph. L.: Self-correcting geometry in model-based algorithms for derivative-free unconstrained optimization (2010)
  19. Conn, Andrew R.; Scheinberg, Katya; Vicente, Luís N.: Global convergence of general derivative-free trust-region algorithms to first- and second-order critical points (2009)
  20. Deng, Geng; Ferris, Michael C.: Variable-number sample-path optimization (2009)

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