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 66 articles )

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  1. Bánhelyi, Balázs; Csendes, Tibor; Lévai, Balázs; Pál, László; Zombori, Dániel: The GLOBAL optimization algorithm. Newly updated with Java implementation and parallelization (2018)
  2. Gervais, Véronique; Le Ravalec, Mickaële: Identifying influence areas with connectivity analysis -- application to the local perturbation of heterogeneity distribution for history matching (2018)
  3. Patanè, Andrea; Santoro, Andrea; Romano, Vittorio; La Magna, Antonino; Nicosia, Giuseppe: Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality (2018)
  4. Echebest, N.; Schuverdt, M. L.; Vignau, R. P.: An inexact restoration derivative-free filter method for nonlinear programming (2017)
  5. 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)
  6. Hare, W.: Compositions of convex functions and fully linear models (2017)
  7. Pál, László: Empirical study of the improved UNIRANDI local search method (2017)
  8. Regis, Rommel G.; Wild, Stefan M.: CONORBIT: constrained optimization by radial basis function interpolation in trust regions (2017)
  9. Verdério, Adriano; Karas, Elizabeth W.; Pedroso, Lucas G.; Scheinberg, Katya: On the construction of quadratic models for derivative-free trust-region algorithms (2017)
  10. Wang, Jueyu; Zhu, Detong: Derivative-free restrictively preconditioned conjugate gradient path method without line search technique for solving linear equality constrained optimization (2017)
  11. Astete-Morales, Sandra; Cauwet, Marie-Liesse; Liu, Jialin; Teytaud, Olivier: Simple and cumulative regret for continuous noisy optimization (2016)
  12. Grapiglia, Geovani Nunes; Yuan, Jinyun; Yuan, Ya-xiang: A derivative-free trust-region algorithm for composite nonsmooth optimization (2016)
  13. Stich, S. U.; Müller, C. L.; Gärtner, B.: Variable metric random pursuit (2016)
  14. Tröltzsch, Anke: A sequential quadratic programming algorithm for equality-constrained optimization without derivatives (2016)
  15. Wang, Jueyu; Zhu, Detong: Conjugate gradient path method without line search technique for derivative-free unconstrained optimization (2016)
  16. Arouxét, Ma. Belén; Echebest, Nélida E.; Pilotta, Elvio A.: Inexact restoration method for nonlinear optimization without derivatives (2015)
  17. Ferreira, Priscila S.; Karas, Elizabeth W.; Sachine, Mael: A globally convergent trust-region algorithm for unconstrained derivative-free optimization (2015)
  18. Gould, Nicholas I. M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  19. Grippo, L.; Rinaldi, F.: A class of derivative-free nonmonotone optimization algorithms employing coordinate rotations and gradient approximations (2015)
  20. Hu, Tao; Qiu, Yan Ping; Cui, Heng Jian; Chen, Li Hong: Numerical discretization-based kernel type estimation methods for ordinary differential equation models (2015)

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