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 86 articles )

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  1. Bhosekar, Atharv; Ierapetritou, Marianthi: A discontinuous derivative-free optimization framework for multi-enterprise supply chain (2020)
  2. Chan Beom Park: YAM2: Yet another library for the M2 variables using sequential quadratic programming (2020) arXiv
  3. Chung, Hayoung; Amir, Oded; Kim, H. Alicia: Level-set topology optimization considering nonlinear thermoelasticity (2020)
  4. Doubova, Anna; Fernández-Cara, Enrique: Some geometric inverse problems for the Lamé system with applications in elastography (2020)
  5. Francesco Biscani; Dario Izzo: A parallel global multiobjective framework for optimization: pagmo (2020) not zbMATH
  6. J L Kaplan, A Bonfanti, A Kabla: RHEOS.jl-A Julia Package for Rheology Data Analysis (2020) arXiv
  7. Meyer, Knut Andreas; Ekh, Magnus; Ahlström, Johan: Anisotropic yield surfaces after large shear deformations in pearlitic steel (2020)
  8. Nosouhi Dehnavi, Fayyaz; Safdari, Masoud; Abrinia, Karen; Hasanabadi, Ali; Baniassadi, Majid: A framework for optimal microstructural design of random heterogeneous materials (2020)
  9. Rodriguez, Sergio; Ludkovski, Michael: Probabilistic bisection with spatial metamodels (2020)
  10. Roustant, Olivier; Padonou, Espéran; Deville, Yves; Clément, Aloïs; Perrin, Guillaume; Giorla, Jean; Wynn, Henry: Group kernels for Gaussian process metamodels with categorical inputs (2020)
  11. Wenchao Ma, Jimmy de la Torre: GDINA: An R Package for Cognitive Diagnosis Modeling (2020) not zbMATH
  12. Zheltkova, V. V.; Zheltkov, Dmitry A.; Bocharov, G. A.; Tyrtyshnikov, Eugene: Application of the global optimization methods for solving the parameter estimation problem in mathematical immunology (2020)
  13. Zheltkov, Dmitry; Tyrtyshnikov, Eugene: Global optimization based on TT-decomposition (2020)
  14. Julien, Jean-Daniel; Pumir, Alain; Boudaoud, Arezki: Strain- or stress-sensing in mechanochemical patterning by the phytohormone auxin (2019)
  15. 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
  16. Najman, Jaromił; Mitsos, Alexander: On tightness and anchoring of McCormick and other relaxations (2019)
  17. Najman, Jaromił; Mitsos, Alexander: Tighter McCormick relaxations through subgradient propagation (2019)
  18. 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
  19. Schweidtmann, Artur M.; Mitsos, Alexander: Deterministic global optimization with artificial neural networks embedded (2019)
  20. Sovrasov, Vladislav: Comparison of several stochastic and deterministic derivative-free global optimization algorithms (2019)

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