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 26 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. Boukouvala, Fani; Floudas, Christodoulos A.: ARGONAUT: algorithms for global optimization of constrained grey-box computational problems (2017)
  3. Hao, Xuemiao; Liang, Chunli; Wei, Linghua: Evaluation of credit value adjustment in K-forward (2017)
  4. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
  5. Regis, Rommel G.; Wild, Stefan M.: CONORBIT: constrained optimization by radial basis function interpolation in trust regions (2017)
  6. Csercsik, Dávid: Lying generators: manipulability of centralized payoff mechanisms in electrical energy trade (2016)
  7. Gorodetsky, Alex; Marzouk, Youssef: Mercer kernels and integrated variance experimental design: connections between Gaussian process regression and polynomial approximation (2016)
  8. 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)
  9. 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)
  10. Šíp, Viktor; Beneš, Luděk: CFD optimization of a vegetation barrier (2016)
  11. 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)
  12. Aaron A. King, Dao Nguyen, Edward L. Ionides: Statistical Inference for Partially Observed Markov Processes via the R Package pomp (2015) arXiv
  13. Bandara, Kosala; Cirak, Fehmi; Of, Günther; Steinbach, Olaf; Zapletal, Jan: Boundary element based multiresolution shape optimisation in electrostatics (2015)
  14. Cirak, Fehmi; Bandara, Kosala: Multiresolution shape optimisation with subdivision surfaces (2015)
  15. Demirel, Abdullah; Niegemann, Jens; Busch, Kurt; Hochbruck, Marlis: Efficient multiple time-stepping algorithms of higher order (2015)
  16. Doubova, Anna; Fernández-Cara, Enrique: Some geometric inverse problems for the linear wave equation (2015)
  17. Dow, Eric; Wang, Qiqi: Optimization of Gaussian random fields (2015)
  18. Fußeder, Daniela; Simeon, Bernd: Algorithmic aspects of isogeometric shape optimization (2015)
  19. Zhang, Yongjin; Feng, Lihong; Li, Suzhou; Benner, Peter: An efficient output error estimation for model order reduction of parametrized evolution equations (2015)
  20. Bloomfield, Victor A.: Using R for numerical analysis in science and engineering (2014)

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