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.

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References in zbMATH (referenced in 92 articles )

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  1. Braun, Phillip; Hare, Warren; Jarry-Bolduc, Gabriel: Limiting behavior of derivative approximation techniques as the number of points tends to infinity on a fixed interval in (\mathbbR) (2021)
  2. Cristofari, Andrea; Rinaldi, Francesco: A derivative-free method for structured optimization problems (2021)
  3. Hansen, Nikolaus; Auger, Anne; Ros, Raymond; Mersmann, Olaf; Tušar, Tea; Brockhoff, Dimo: COCO: a platform for comparing continuous optimizers in a black-box setting (2021)
  4. Ahmadvand, M.; Esmaeilbeigi, M.; Yaghoobi, F.; Kamandi, A.: Performance evaluation of ORBIT algorithm to some effective parameters (2020)
  5. Bemporad, Alberto: Global optimization via inverse distance weighting and radial basis functions (2020)
  6. Brás, C. P.; Custódio, A. L.: On the use of polynomial models in multiobjective directional direct search (2020)
  7. Gumma, E. A. E.; Ali, M. Montaz; Hashim, M. H. A.: A derivative-free algorithm for non-linear optimization with linear equality constraints (2020)
  8. Hare, Warren; Planiden, Chayne; Sagastizábal, Claudia: A derivative-free (\mathcalV\mathcalU)-algorithm for convex finite-max problems (2020)
  9. Manno, Andrea; Amaldi, Edoardo; Casella, Francesco; Martelli, Emanuele: A local search method for costly black-box problems and its application to CSP plant start-up optimization refinement (2020)
  10. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  11. Verma, Aekaansh; Wong, Kwai; Marsden, Alison L.: A concurrent implementation of the surrogate management framework with application to cardiovascular shape optimization (2020)
  12. Xi, Min; Sun, Wenyu; Chen, Jun: Survey of derivative-free optimization (2020)
  13. Xi, Min; Sun, Wenyu; Chen, Yannan; Sun, Hailin: A derivative-free algorithm for spherically constrained optimization (2020)
  14. Ahmadvand, Mohammad; Esmaeilbeigi, Mohsen; Kamandi, Ahmad; Yaghoobi, Farajollah Mohammadi: An improved hybrid-ORBIT algorithm based on point sorting and MLE technique (2019)
  15. Berahas, Albert S.; Byrd, Richard H.; Nocedal, Jorge: Derivative-free optimization of noisy functions via quasi-Newton methods (2019)
  16. Breitmoser, Yves: Knowing me, imagining you: projection and overbidding in auctions (2019)
  17. Cartis, Coralia; Roberts, Lindon: A derivative-free Gauss-Newton method (2019)
  18. Hirk, Rainer; Hornik, Kurt; Vana, Laura: Multivariate ordinal regression models: an analysis of corporate credit ratings (2019)
  19. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  20. Wagner, Heiko; Kneip, Alois: Nonparametric registration to low-dimensional function spaces (2019)

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