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.
Keywords for this software
References in zbMATH (referenced in 85 articles )
Showing results 1 to 20 of 85.
Sorted by year (- 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)
- Gumma, E. A. E.; Ali, M. Montaz; Hashim, M. H. A.: A derivative-free algorithm for non-linear optimization with linear equality constraints (2020)
- Hare, Warren; Planiden, Chayne; Sagastizábal, Claudia: A derivative-free (\mathcalV\mathcalU)-algorithm for convex finite-max problems (2020)
- 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)
- Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
- Verma, Aekaansh; Wong, Kwai; Marsden, Alison L.: A concurrent implementation of the surrogate management framework with application to cardiovascular shape optimization (2020)
- Xi, Min; Sun, Wenyu; Chen, Jun: Survey of derivative-free optimization (2020)
- Xi, Min; Sun, Wenyu; Chen, Yannan; Sun, Hailin: A derivative-free algorithm for spherically constrained optimization (2020)
- Ahmadvand, Mohammad; Esmaeilbeigi, Mohsen; Kamandi, Ahmad; Yaghoobi, Farajollah Mohammadi: An improved hybrid-ORBIT algorithm based on point sorting and MLE technique (2019)
- Berahas, Albert S.; Byrd, Richard H.; Nocedal, Jorge: Derivative-free optimization of noisy functions via quasi-Newton methods (2019)
- Breitmoser, Yves: Knowing me, imagining you: projection and overbidding in auctions (2019)
- Cartis, Coralia; Roberts, Lindon: A derivative-free Gauss-Newton method (2019)
- Hirk, Rainer; Hornik, Kurt; Vana, Laura: Multivariate ordinal regression models: an analysis of corporate credit ratings (2019)
- Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
- Wagner, Heiko; Kneip, Alois: Nonparametric registration to low-dimensional function spaces (2019)
- 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)
- Bánhelyi, Balázs; Csendes, Tibor; Lévai, Balázs; Pál, László; Zombori, Dániel: The GLOBAL optimization algorithm. Newly updated with Java implementation and parallelization (2018)
- 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)
- Gervais, Véronique; Le Ravalec, Mickaële: Identifying influence areas with connectivity analysis -- application to the local perturbation of heterogeneity distribution for history matching (2018)
- Kim, Jiwoong: A fast algorithm for the coordinate-wise minimum distance estimation (2018)