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 17 articles )

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  1. Boukouvala, Fani; Faruque Hasan, M.M.; Floudas, Christodoulos A.: Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption (2017)
  2. Gorodetsky, Alex; Marzouk, Youssef: Mercer kernels and integrated variance experimental design: connections between Gaussian process regression and polynomial approximation (2016)
  3. Hernández-Lobato, José Miguel; Gelbart, Michael A.; Adams, Ryan P.; Hoffman, Matthew W.; Ghahramani, Zoubin: A general framework for constrained Bayesian optimization using information-based search (2016)
  4. Neale, Michael C.; Hunter, Michael D.; Pritikin, Joshua N.; Zahery, Mahsa; Brick, Timothy R.; Kirkpatrick, Robert M.; Estabrook, Ryne; Bates, Timothy C.; Maes, Hermine H.; Boker, Steven M.: OpenMX 2.0: extended structural equation and statistical modeling (2016)
  5. Šíp, Viktor; Beneš, Luděk: CFD optimization of a vegetation barrier (2016)
  6. Vidal-Codina, F.; Nguyen, N.C.; Giles, M.B.; Peraire, J.: An empirical interpolation and model-variance reduction method for computing statistical outputs of parametrized stochastic partial differential equations (2016)
  7. Cirak, Fehmi; Bandara, Kosala: Multiresolution shape optimisation with subdivision surfaces (2015)
  8. Dow, Eric; Wang, Qiqi: Optimization of Gaussian random fields (2015)
  9. Fußeder, Daniela; Simeon, Bernd: Algorithmic aspects of isogeometric shape optimization (2015)
  10. Zhang, Yongjin; Feng, Lihong; Li, Suzhou; Benner, Peter: An efficient output error estimation for model order reduction of parametrized evolution equations (2015)
  11. Bloomfield, Victor A.: Using R for numerical analysis in science and engineering (2014)
  12. Lara, Pedro C.S.; Portugal, Renato; Lavor, Carlile: A new hybrid classical-quantum algorithm for continuous global optimization problems (2014)
  13. Dörsek, Philipp; Teichmann, Josef: Efficient simulation and calibration of general HJM models by splitting schemes (2013)
  14. Gerstl, Enrique; Mosheiov, Gur: Scheduling job classes on uniform machines (2012)
  15. Niegemann, Jens; Diehl, Richard; Busch, Kurt: Efficient low-storage Runge-Kutta schemes with optimized stability regions (2012)
  16. Reif, Matthias; Shafait, Faisal; Dengel, Andreas: Meta-learning for evolutionary parameter optimization of classifiers (2012)
  17. Montes de Oca, Marco A.; Aydın, Doğan; Stützle, Thomas: An incremental particle swarm for large-scale continuous optimization problems: An example of tuning-in-the-loop (re)design of optimization algorithms (2011)