Algorithm 778: L-BFGS-B Fortran subroutines for large-scale bound-constrained optimization L-BFGS-B is a limited-memory algorithm for solving large nonlinear optimization problems subject to simple bounds on the variables. It is intended for problems in which information on the Hessian matrix is difficult to obtain, or for large dense problems. L-BFGS-B can also be used for unconstrained problems and in this case performs similarly to its predecessor, algorithm L-BFGS (Harwell routine VA15). The algorithm is implemened in Fortran 77. (Source:

References in zbMATH (referenced in 308 articles , 1 standard article )

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  1. Brás, C. P.; Martínez, J. M.; Raydan, M.: Large-scale unconstrained optimization using separable cubic modeling and matrix-free subspace minimization (2020)
  2. de Zordo-Banliat, M.; Merle, X.; Dergham, G.; Cinnella, P.: Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models (2020)
  3. Dharmavaram, Sanjay; Perotti, Luigi E.: A Lagrangian formulation for interacting particles on a deformable medium (2020)
  4. Hong, David; Kolda, Tamara G.; Duersch, Jed A.: Generalized canonical polyadic tensor decomposition (2020)
  5. Huang, Daniel Z.; Xu, Kailai; Farhat, Charbel; Darve, Eric: Learning constitutive relations from indirect observations using deep neural networks (2020)
  6. Khaniyev, Taghi; Kayış, Enis; Güllü, Refik: Next-day operating room scheduling with uncertain surgery durations: exact analysis and heuristics (2020)
  7. Klemens, Fabian; Förster, Benjamin; Dorn, Márcio; Thäter, Gudrun; Krause, Mathias J.: Solving fluid flow domain identification problems with adjoint lattice Boltzmann methods (2020)
  8. Ljungdahl, Mathias Mørck; Podolskij, Mark: A minimal contrast estimator for the linear fractional stable motion (2020)
  9. McKenna, Sean A.; Akhriev, Albert; Echeverría Ciaurri, David; Zhuk, Sergiy: Efficient uncertainty quantification of reservoir properties for parameter estimation and production forecasting (2020)
  10. Ongaro, Andrea; Migliorati, Sonia; Ascari, Roberto: A new mixture model on the simplex (2020)
  11. Rutkowski, Mariusz; Gryglas, Wojciech; Szumbarski, Jacek; Leonardi, Christopher; Łaniewski-Wołłk, Łukasz: Open-loop optimal control of a flapping wing using an adjoint lattice Boltzmann method (2020)
  12. Shao, Wei; Zuo, Yijun: Computing the halfspace depth with multiple try algorithm and simulated annealing algorithm (2020)
  13. Shen, Chungen; Fan, Changxing; Wang, Yunlong; Xue, Wenjuan: Limited memory BFGS algorithm for the matrix approximation problem in Frobenius norm (2020)
  14. Song, Dawei; Seidl, D. Thomas; Oberai, Assad A.: Three-dimensional traction microscopy accounting for cell-induced matrix degradation (2020)
  15. Wei, Wei; Dai, Hua; Liang, Weitai: A novel projected gradient-like method for optimization problems with simple constraints (2020)
  16. Xu, Yong; Zhang, Hao; Li, Yongge; Zhou, Kuang; Liu, Qi; Kurths, Jürgen: Solving Fokker-Planck equation using deep learning (2020)
  17. Zhang, Shanglong; Gain, Arun L.; Norato, Julián A.: Adaptive mesh refinement for topology optimization with discrete geometric components (2020)
  18. Bachoc, François; Bevilacqua, Moreno; Velandia, Daira: Composite likelihood estimation for a Gaussian process under fixed domain asymptotics (2019)
  19. Becker, Stephen; Fadili, Jalal; Ochs, Peter: On quasi-Newton forward-backward splitting: proximal calculus and convergence (2019)
  20. Boggs, Paul T.; Byrd, Richard H.: Adaptive, limited-memory BFGS algorithms for unconstrained optimization (2019)

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