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 93 articles )

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  1. Blank, Laura; Meneses Rioseco, Ernesto; Caiazzo, Alfonso; Wilbrandt, Ulrich: Modeling, simulation, and optimization of geothermal energy production from hot sedimentary aquifers (2021)
  2. Ji, Ye; Yu, Ying-Ying; Wang, Meng-Yun; Zhu, Chun-Gang: Constructing high-quality planar NURBS parameterization for isogeometric analysis by adjustment control points and weights (2021)
  3. Karban, Pavel; Pánek, David; Orosz, Tamás; Petrášová, Iveta; Doležel, Ivo: FEM based robust design optimization with Agros and Ārtap (2021)
  4. Beliakov, G.; Gagolewski, M.; James, S.: DC optimization for constructing discrete Sugeno integrals and learning nonadditive measures (2020)
  5. Bemporad, Alberto: Global optimization via inverse distance weighting and radial basis functions (2020)
  6. Bhosekar, Atharv; Ierapetritou, Marianthi: A discontinuous derivative-free optimization framework for multi-enterprise supply chain (2020)
  7. Chan Beom Park: YAM2: Yet another library for the M2 variables using sequential quadratic programming (2020) arXiv
  8. Chung, Hayoung; Amir, Oded; Kim, H. Alicia: Level-set topology optimization considering nonlinear thermoelasticity (2020)
  9. Doubova, Anna; Fernández-Cara, Enrique: Some geometric inverse problems for the Lamé system with applications in elastography (2020)
  10. Francesco Biscani; Dario Izzo: A parallel global multiobjective framework for optimization: pagmo (2020) not zbMATH
  11. J L Kaplan, A Bonfanti, A Kabla: RHEOS.jl-A Julia Package for Rheology Data Analysis (2020) arXiv
  12. Meyer, Knut Andreas; Ekh, Magnus; Ahlström, Johan: Anisotropic yield surfaces after large shear deformations in pearlitic steel (2020)
  13. Mies, Fabian: Rate-optimal estimation of the Blumenthal-Getoor index of a Lévy process (2020)
  14. Nosouhi Dehnavi, Fayyaz; Safdari, Masoud; Abrinia, Karen; Hasanabadi, Ali; Baniassadi, Majid: A framework for optimal microstructural design of random heterogeneous materials (2020)
  15. Rodriguez, Sergio; Ludkovski, Michael: Probabilistic bisection with spatial metamodels (2020)
  16. Roustant, Olivier; Padonou, Espéran; Deville, Yves; Clément, Aloïs; Perrin, Guillaume; Giorla, Jean; Wynn, Henry: Group kernels for Gaussian process metamodels with categorical inputs (2020)
  17. Wenchao Ma, Jimmy de la Torre: GDINA: An R Package for Cognitive Diagnosis Modeling (2020) not zbMATH
  18. Wu, N., Kenway, G., Mader, C. A., Jasa, J., Martins, J. R. R. A.: pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems (2020) not zbMATH
  19. 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)
  20. Zheltkov, Dmitry; Tyrtyshnikov, Eugene: Global optimization based on TT-decomposition (2020)

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