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 57 articles )

Showing results 1 to 20 of 57.
Sorted by year (citations)

1 2 3 next

  1. Schweidtmann, Artur M.; Mitsos, Alexander: Deterministic global optimization with artificial neural networks embedded (2019)
  2. 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
  3. Bánhelyi, Balázs; Csendes, Tibor; Lévai, Balázs; Pál, László; Zombori, Dániel: The GLOBAL optimization algorithm. Newly updated with Java implementation and parallelization (2018)
  4. Chiquet, Julien; Mariadassou, Mahendra; Robin, Stéphane: Variational inference for probabilistic Poisson PCA (2018)
  5. Costa, Alberto; Nannicini, Giacomo: RBFOpt: an open-source library for black-box optimization with costly function evaluations (2018)
  6. Csercsik, Dávid; Kiss, Hubert János: Optimal payments to connected depositors in turbulent times: a Markov chain approach (2018)
  7. 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)
  8. Larson, Jeffrey; Wild, Stefan M.: Asynchronously parallel optimization solver for finding multiple minima (2018)
  9. López-Lopera, Andrés F.; Bachoc, François; Durrande, Nicolas; Roustant, Olivier: Finite-dimensional Gaussian approximation with linear inequality constraints (2018)
  10. Magron, Victor: Interval enclosures of upper bounds of roundoff errors using semidefinite programming (2018)
  11. Zheltkova, Valeriya V.; Zheltkov, Dmitry A.; Grossman, Zvi; Bocharov, Gennady A.; Tyrtyshnikov, Eugene E.: Tensor based approach to the numerical treatment of the parameter estimation problems in mathematical immunology (2018)
  12. Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd: CVXR: An R Package for Disciplined Convex Optimization (2017) arXiv
  13. Bongartz, Dominik; Mitsos, Alexander: Deterministic global optimization of process flowsheets in a reduced space using McCormick relaxations (2017)
  14. 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)
  15. Boukouvala, Fani; Floudas, Christodoulos A.: ARGONAUT: algorithms for global optimization of constrained grey-box computational problems (2017)
  16. Druedahl, Jeppe; Jørgensen, Thomas Høgholm: A general endogenous grid method for multi-dimensional models with non-convexities and constraints (2017)
  17. E. Schuyler Fried, Nicolas P. D. Sawaya, Yudong Cao, Ian D. Kivlichan, Jhonathan Romero, Alán Aspuru-Guzik: qTorch: The Quantum Tensor Contraction Handler (2017) arXiv
  18. Hao, Xuemiao; Liang, Chunli; Wei, Linghua: Evaluation of credit value adjustment in K-forward (2017)
  19. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
  20. Masoudi, Ehsan; Holling, Heinz; Wong, Weng Kee: Application of imperialist competitive algorithm to find minimax and standardized maximin optimal designs (2017)

1 2 3 next