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 69 articles )

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  1. Julien, Jean-Daniel; Pumir, Alain; Boudaoud, Arezki: Strain- or stress-sensing in mechanochemical patterning by the phytohormone auxin (2019)
  2. 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
  3. Najman, Jaromił; Mitsos, Alexander: Tighter McCormick relaxations through subgradient propagation (2019)
  4. Najman, Jaromił; Mitsos, Alexander: On tightness and anchoring of McCormick and other relaxations (2019)
  5. 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
  6. Schweidtmann, Artur M.; Mitsos, Alexander: Deterministic global optimization with artificial neural networks embedded (2019)
  7. 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
  8. Bánhelyi, Balázs; Csendes, Tibor; Lévai, Balázs; Pál, László; Zombori, Dániel: The GLOBAL optimization algorithm. Newly updated with Java implementation and parallelization (2018)
  9. Chiquet, Julien; Mariadassou, Mahendra; Robin, Stéphane: Variational inference for probabilistic Poisson PCA (2018)
  10. Costa, Alberto; Nannicini, Giacomo: RBFOpt: an open-source library for black-box optimization with costly function evaluations (2018)
  11. Csercsik, Dávid; Kiss, Hubert János: Optimal payments to connected depositors in turbulent times: a Markov chain approach (2018)
  12. 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)
  13. Larson, Jeffrey; Wild, Stefan M.: Asynchronously parallel optimization solver for finding multiple minima (2018)
  14. Loiseau, Jean-Christophe; Brunton, Steven L.: Constrained sparse Galerkin regression (2018)
  15. López-Lopera, Andrés F.; Bachoc, François; Durrande, Nicolas; Roustant, Olivier: Finite-dimensional Gaussian approximation with linear inequality constraints (2018)
  16. Magron, Victor: Interval enclosures of upper bounds of roundoff errors using semidefinite programming (2018)
  17. P. K. Mogensen; A. N. Riseth: Optim: A mathematical optimization package for Julia (2018) not zbMATH
  18. 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)
  19. Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd: CVXR: An R Package for Disciplined Convex Optimization (2017) arXiv
  20. Bongartz, Dominik; Mitsos, Alexander: Deterministic global optimization of process flowsheets in a reduced space using McCormick relaxations (2017)

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