L-BFGS-B

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.


References in zbMATH (referenced in 172 articles )

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  1. Gajardo, Diego; Mercado, Alberto; Muñoz, Juan Carlos: Identification of the anti-diffusion coefficient for the linear Kuramoto-Sivashinsky equation (2021)
  2. Grajales, Juan Carlos Muñoz: Non-homogeneous boundary value problems for some KdV-type equations on a finite interval: a numerical approach (2021)
  3. Baghfalaki, Taban; Ganjali, Mojtaba: A transition model for analyzing multivariate longitudinal data using Gaussian copula approach (2020)
  4. de Zordo-Banliat, M.; Merle, X.; Dergham, G.; Cinnella, P.: Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models (2020)
  5. Dharmavaram, Sanjay; Perotti, Luigi E.: A Lagrangian formulation for interacting particles on a deformable medium (2020)
  6. Ferreiro-Ferreiro, A. M.; García-Rodríguez, J. A.; López-Salas, J. G.; Escalante, C.; Castro, M. J.: Global optimization for data assimilation in landslide tsunami models (2020)
  7. González-González, José M.; Vázquez-Méndez, Miguel E.; Diéguez-Aranda, Ulises: A note on the regularity of a new metric for measuring even-flow in forest planning (2020)
  8. Leyffer, Sven; Vanaret, Charlie: An augmented Lagrangian filter method (2020)
  9. Likhosherstov, Valerii; Maximov, Yury; Chertkov, Michael: Tractable minor-free generalization of planar zero-field Ising models (2020)
  10. McKenna, Sean A.; Akhriev, Albert; Echeverría Ciaurri, David; Zhuk, Sergiy: Efficient uncertainty quantification of reservoir properties for parameter estimation and production forecasting (2020)
  11. Mestdagh, Guillaume; Goussard, Yves; Orban, Dominique: Scaled projected-directions methods with application to transmission tomography (2020)
  12. Moriconi, Riccardo; Deisenroth, Marc Peter; Sesh Kumar, K. S.: High-dimensional Bayesian optimization using low-dimensional feature spaces (2020)
  13. 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)
  14. Sherman, Samantha; Kolda, Tamara G.: Estimating higher-order moments using symmetric tensor decomposition (2020)
  15. Xu, Yong; Zhang, Hao; Li, Yongge; Zhou, Kuang; Liu, Qi; Kurths, Jürgen: Solving Fokker-Planck equation using deep learning (2020)
  16. Becker, Stephen; Fadili, Jalal; Ochs, Peter: On quasi-Newton forward-backward splitting: proximal calculus and convergence (2019)
  17. Borges, Patrick; Godoi, Luciana G.: Pólya-Aeppli regression model for overdispersed count data (2019)
  18. Brust, Johannes; Burdakov, Oleg; Erway, Jennifer B.; Marcia, Roummel F.: A dense initialization for limited-memory quasi-Newton methods (2019)
  19. Debarnot, Valentin; Kahn, Jonas; Weiss, Pierre: Multiview attenuation estimation and correction (2019)
  20. Fercoq, Olivier; Bianchi, Pascal: A coordinate-descent primal-dual algorithm with large step size and possibly nonseparable functions (2019)

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