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 178 articles )

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  1. Ek, David; Forsgren, Anders: Approximate solution of system of equations arising in interior-point methods for bound-constrained optimization (2021)
  2. Gajardo, Diego; Mercado, Alberto; Muñoz, Juan Carlos: Identification of the anti-diffusion coefficient for the linear Kuramoto-Sivashinsky equation (2021)
  3. Girolami, Mark; Febrianto, Eky; Yin, Ge; Cirak, Fehmi: The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions (2021)
  4. Ma, Chenxin; Jaggi, Martin; Curtis, Frank E.; Srebro, Nathan; Takáč, Martin: An accelerated communication-efficient primal-dual optimization framework for structured machine learning (2021)
  5. Muñoz Grajales, Juan Carlos: Non-homogeneous boundary value problems for some KdV-type equations on a finite interval: a numerical approach (2021)
  6. Yuan, Zhenfei; Hu, Taizhong: pyvine: the Python package for regular vine copula modeling, sampling and testing (2021)
  7. Baghfalaki, Taban; Ganjali, Mojtaba: A transition model for analyzing multivariate longitudinal data using Gaussian copula approach (2020)
  8. de Zordo-Banliat, M.; Merle, X.; Dergham, G.; Cinnella, P.: Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models (2020)
  9. Dharmavaram, Sanjay; Perotti, Luigi E.: A Lagrangian formulation for interacting particles on a deformable medium (2020)
  10. 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)
  11. 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)
  12. Leyffer, Sven; Vanaret, Charlie: An augmented Lagrangian filter method (2020)
  13. Likhosherstov, Valerii; Maximov, Yury; Chertkov, Michael: Tractable minor-free generalization of planar zero-field Ising models (2020)
  14. Li, Pengyuan; Wang, Zhan; Luo, Dan; Pham, Hongtruong: Global convergence of a modified two-parameter scaled BFGS method with Yuan-Wei-Lu line search for unconstrained optimization (2020)
  15. Mathias, Sonja; Coulier, Adrien; Bouchnita, Anass; Hellander, Andreas: Impact of force function formulations on the numerical simulation of centre-based models (2020)
  16. McKenna, Sean A.; Akhriev, Albert; Echeverría Ciaurri, David; Zhuk, Sergiy: Efficient uncertainty quantification of reservoir properties for parameter estimation and production forecasting (2020)
  17. Mestdagh, Guillaume; Goussard, Yves; Orban, Dominique: Scaled projected-directions methods with application to transmission tomography (2020)
  18. Moriconi, Riccardo; Deisenroth, Marc Peter; Sesh Kumar, K. S.: High-dimensional Bayesian optimization using low-dimensional feature spaces (2020)
  19. 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)
  20. Sherman, Samantha; Kolda, Tamara G.: Estimating higher-order moments using symmetric tensor decomposition (2020)

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