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 201 articles , 1 standard article )

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  2. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  3. Kirschstein, Thomas: Planning of multi-product pipelines by economic lot scheduling models (2018)
  4. Klemens, Fabian; Schuhmann, Sebastian; Guthausen, Gisela; Thäter, Gudrun; Krause, Mathias J.: CFD-MRI: A coupled measurement and simulation approach for accurate fluid flow characterisation and domain identification (2018)
  5. Rahman, Adam; Oldford, R. Wayne: Euclidean distance matrix completion and point configurations from the minimal spanning tree (2018)
  6. Sanchez, Fabio; Barboza, Luis A.; Burton, David; Cintrón-Arias, Ariel: Comparative analysis of dengue versus chikungunya outbreaks in Costa Rica (2018)
  7. Tsilifis, Panagiotis; Browning, William J.; Wood, Thomas E.; Newton, Paul K.; Ghanem, Roger G.: The stochastic quasi-chemical model for bacterial growth: variational Bayesian parameter update (2018)
  8. Zarepisheh, Masoud; Xing, Lei; Ye, Yinyu: A computation study on an integrated alternating direction method of multipliers for large scale optimization (2018)
  9. Beliakov, Gleb; Gómez, Daniel; James, Simon; Montero, Javier; Rodríguez, J. Tinguaro: Approaches to learning strictly-stable weights for data with missing values (2017)
  10. Bowman, Dale; George, E. Olusegun: Weighted least squares estimation for exchangeable binary data (2017)
  11. Chen, Tianyi; Curtis, Frank E.; Robinson, Daniel P.: A reduced-space algorithm for minimizing $\ell_1$-regularized convex functions (2017)
  12. Karimi, Sahar; Vavasis, Stephen: IMRO: A proximal quasi-Newton method for solving $\ell_1$-regularized least squares problems (2017)
  13. Laval, J.-P.; Vassilicos, J. C.; Foucaut, J.-M.; Stanislas, M.: Comparison of turbulence profiles in high-Reynolds-number turbulent boundary layers and validation of a predictive model (2017)
  14. Mao, Qi; Wang, Li; Tsang, Ivor W.: A unified probabilistic framework for robust manifold learning and embedding (2017)
  15. Métivier, L.; Brossier, R.; Operto, S.; Virieux, J.: Full waveform inversion and the truncated Newton method (2017)
  16. Owen, N. E.; Challenor, P.; Menon, P. P.; Bennani, S.: Comparison of surrogate-based uncertainty quantification methods for computationally expensive simulators (2017)
  17. Racine, Jeffrey S.; Li, Kevin: Nonparametric conditional quantile estimation: a locally weighted quantile kernel approach (2017)
  18. Reimann, Olivier; Schumacher, Christian; Vetschera, Rudolf: How well does the OWA operator represent real preferences? (2017)
  19. Stiegelmeier, Elenice W.; Oliveira, Vilma A.; Silva, Geraldo N.; Karam, Décio: Optimal weed population control using nonlinear programming (2017)
  20. Zabinyako, Gerard Idelfonovich: Applications of quasi-Newton algorithms for solving large scale problems (2017)

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