NLopt

NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. Its features include: Callable from C, C++, Fortran, Matlab or GNU Octave, Python, GNU Guile, Julia, GNU R, Lua, and OCaml. A common interface for many different algorithms—try a different algorithm just by changing one parameter. Support for large-scale optimization (some algorithms scalable to millions of parameters and thousands of constraints). Both global and local optimization algorithms. Algorithms using function values only (derivative-free) and also algorithms exploiting user-supplied gradients. Algorithms for unconstrained optimization, bound-constrained optimization, and general nonlinear inequality/equality constraints. Free/open-source software under the GNU LGPL (and looser licenses for some portions of NLopt). See the NLopt Introduction for a further overview of the types of problems it addresses.


References in zbMATH (referenced in 76 articles )

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  1. Bhosekar, Atharv; Ierapetritou, Marianthi: A discontinuous derivative-free optimization framework for multi-enterprise supply chain (2020)
  2. Chung, Hayoung; Amir, Oded; Kim, H. Alicia: Level-set topology optimization considering nonlinear thermoelasticity (2020)
  3. J L Kaplan, A Bonfanti, A Kabla: RHEOS.jl-A Julia Package for Rheology Data Analysis (2020) arXiv
  4. Rodriguez, Sergio; Ludkovski, Michael: Probabilistic bisection with spatial metamodels (2020)
  5. Wenchao Ma, Jimmy de la Torre: GDINA: An R Package for Cognitive Diagnosis Modeling (2020) not zbMATH
  6. Julien, Jean-Daniel; Pumir, Alain; Boudaoud, Arezki: Strain- or stress-sensing in mechanochemical patterning by the phytohormone auxin (2019)
  7. Michael H. Goerz, Daniel Basilewitsch, Fernando Gago-Encinas, Matthias G. Krauss, Karl P. Horn, Daniel M. Reich, Christiane P. Koch: Krotov: A Python implementation of Krotov’s method for quantum optimal control (2019) arXiv
  8. Najman, Jaromił; Mitsos, Alexander: On tightness and anchoring of McCormick and other relaxations (2019)
  9. Najman, Jaromił; Mitsos, Alexander: Tighter McCormick relaxations through subgradient propagation (2019)
  10. Sameh Abdulah, Yuxiao Li, Jian Cao, Hatem Ltaief, David E. Keyes, Marc G. Genton, Ying Sun: ExaGeoStatR: A Package for Large-Scale Geostatistics in R (2019) arXiv
  11. Schweidtmann, Artur M.; Mitsos, Alexander: Deterministic global optimization with artificial neural networks embedded (2019)
  12. Sovrasov, Vladislav: Comparison of several stochastic and deterministic derivative-free global optimization algorithms (2019)
  13. Antoine Cully; Konstantinos Chatzilygeroudis; Federico Allocati; Jean-Baptiste Mouret: Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization (2018) not zbMATH
  14. Bánhelyi, Balázs; Csendes, Tibor; Lévai, Balázs; Pál, László; Zombori, Dániel: The GLOBAL optimization algorithm. Newly updated with Java implementation and parallelization (2018)
  15. Chiquet, Julien; Mariadassou, Mahendra; Robin, Stéphane: Variational inference for probabilistic Poisson PCA (2018)
  16. Costa, Alberto; Nannicini, Giacomo: RBFOpt: an open-source library for black-box optimization with costly function evaluations (2018)
  17. Csercsik, Dávid; Kiss, Hubert János: Optimal payments to connected depositors in turbulent times: a Markov chain approach (2018)
  18. Jayasinghe, Savithru; Darmofal, David L.; Burgess, Nicholas K.; Galbraith, Marshall C.; Allmaras, Steven R.: A space-time adaptive method for reservoir flows: formulation and one-dimensional application (2018)
  19. Larson, Jeffrey; Wild, Stefan M.: Asynchronously parallel optimization solver for finding multiple minima (2018)
  20. Loiseau, Jean-Christophe; Brunton, Steven L.: Constrained sparse Galerkin regression (2018)

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