NEWUOA

NEWUOA is a software developped by M.J.D. Powell for unconstrained optimization without derivatives. The NEWUOA seeks the least value of a function F(x) (x is a vector of dimension n ) when F(x) can be calculated for any vector of variables x . The algorithm is iterative, a quadratic model being required at the beginning of each iteration, which is used in a trust region procedure for adjusting the variables. When the quadratic model is revised, the new model interpolates F at m points, the value m=2n+1 being recommended.


References in zbMATH (referenced in 93 articles )

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  1. Wagner, Heiko; Kneip, Alois: Nonparametric registration to low-dimensional function spaces (2019)
  2. Wu, Leqin; Qiu, Xing; Yuan, Ya-xiang; Wu, Hulin: Parameter estimation and variable selection for big systems of linear ordinary differential equations: a matrix-based approach (2019)
  3. Bánhelyi, Balázs; Csendes, Tibor; Lévai, Balázs; Pál, László; Zombori, Dániel: The GLOBAL optimization algorithm. Newly updated with Java implementation and parallelization (2018)
  4. Daubechies, Ingrid (ed.); Kutyniok, Gitta (ed.); Rauhut, Holger (ed.); Strohmer, Thomas (ed.): Applied harmonic analysis and data processing. Abstracts from the workshop held March 25--31, 2018 (2018)
  5. Gervais, Véronique; Le Ravalec, Mickaële: Identifying influence areas with connectivity analysis -- application to the local perturbation of heterogeneity distribution for history matching (2018)
  6. Kim, Jiwoong: A fast algorithm for the coordinate-wise minimum distance estimation (2018)
  7. Patanè, Andrea; Santoro, Andrea; Romano, Vittorio; La Magna, Antonino; Nicosia, Giuseppe: Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality (2018)
  8. Echebest, N.; Schuverdt, M. L.; Vignau, R. P.: An inexact restoration derivative-free filter method for nonlinear programming (2017)
  9. Gao, Guohua; Vink, Jeroen C.; Chen, Chaohui; El Khamra, Yaakoub; Tarrahi, Mohammadali: Distributed Gauss-Newton optimization method for history matching problems with multiple best matches (2017)
  10. Hare, W.: Compositions of convex functions and fully linear models (2017)
  11. Pál, László: Empirical study of the improved UNIRANDI local search method (2017)
  12. Regis, Rommel G.; Wild, Stefan M.: CONORBIT: constrained optimization by radial basis function interpolation in trust regions (2017)
  13. Verdério, Adriano; Karas, Elizabeth W.; Pedroso, Lucas G.; Scheinberg, Katya: On the construction of quadratic models for derivative-free trust-region algorithms (2017)
  14. Wang, Jueyu; Zhu, Detong: Derivative-free restrictively preconditioned conjugate gradient path method without line search technique for solving linear equality constrained optimization (2017)
  15. Astete-Morales, Sandra; Cauwet, Marie-Liesse; Liu, Jialin; Teytaud, Olivier: Simple and cumulative regret for continuous noisy optimization (2016)
  16. Grapiglia, Geovani Nunes; Yuan, Jinyun; Yuan, Ya-xiang: A derivative-free trust-region algorithm for composite nonsmooth optimization (2016)
  17. Qiu, Yanping; Hu, Tao; Liang, Baosheng; Cui, Hengjian: Robust estimation of parameters in nonlinear ordinary differential equation models (2016)
  18. Stich, S. U.; Müller, C. L.; Gärtner, B.: Variable metric random pursuit (2016)
  19. Tröltzsch, Anke: A sequential quadratic programming algorithm for equality-constrained optimization without derivatives (2016)
  20. Wang, Jueyu; Zhu, Detong: Conjugate gradient path method without line search technique for derivative-free unconstrained optimization (2016)