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 335 articles , 1 standard article )

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  1. Bagirov, Adil M.; Taheri, Sona; Cimen, Emre: Incremental DC optimization algorithm for large-scale clusterwise linear regression (2021)
  2. Courbot, Jean-Baptiste; Colicchio, Bruno: A fast homotopy algorithm for gridless sparse recovery (2021)
  3. Gajardo, Diego; Mercado, Alberto; Muñoz, Juan Carlos: Identification of the anti-diffusion coefficient for the linear Kuramoto-Sivashinsky equation (2021)
  4. Grajales, Juan Carlos Muñoz: Non-homogeneous boundary value problems for some KdV-type equations on a finite interval: a numerical approach (2021)
  5. Kolkiewicz, Adam; Rice, Gregory; Xie, Yijun: Projection pursuit based tests of normality with functional data (2021)
  6. Lu, Lu; Meng, Xuhui; Mao, Zhiping; Karniadakis, George Em: DeepXDE: a deep learning library for solving differential equations (2021)
  7. Rioux, Gabriel; Choksi, Rustum; Hoheisel, Tim; Maréchal, Pierre; Scarvelis, Christopher: The maximum entropy on the mean method for image deblurring (2021)
  8. Sun, Yanli; Wang, Xinyu; Guo, Xu; Mei, Yue: Adhesion behavior of an extensible soft thin film-substrate system based on finite deformation theory (2021)
  9. Baghfalaki, Taban; Ganjali, Mojtaba: A transition model for analyzing multivariate longitudinal data using Gaussian copula approach (2020)
  10. Bauweraerts, Pieter; Meyers, Johan: Reconstruction of turbulent flow fields from lidar measurements using large-eddy simulation (2020)
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  12. de Zordo-Banliat, M.; Merle, X.; Dergham, G.; Cinnella, P.: Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models (2020)
  13. Dharmavaram, Sanjay; Perotti, Luigi E.: A Lagrangian formulation for interacting particles on a deformable medium (2020)
  14. 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)
  15. Grymin, Radosław; Bożejko, Wojciech; Chaczko, Zenon; Pempera, Jarosław; Wodecki, Mieczysław: Algorithm for solving the discrete-continuous inspection problem (2020)
  16. Hong, David; Kolda, Tamara G.; Duersch, Jed A.: Generalized canonical polyadic tensor decomposition (2020)
  17. Huang, Daniel Z.; Xu, Kailai; Farhat, Charbel; Darve, Eric: Learning constitutive relations from indirect observations using deep neural networks (2020)
  18. Jäkle, Christian; Volkwein, Stefan: POD-based mixed-integer optimal control of evolution systems (2020)
  19. Khaniyev, Taghi; Kayış, Enis; Güllü, Refik: Next-day operating room scheduling with uncertain surgery durations: exact analysis and heuristics (2020)
  20. Klemens, Fabian; Förster, Benjamin; Dorn, Márcio; Thäter, Gudrun; Krause, Mathias J.: Solving fluid flow domain identification problems with adjoint lattice Boltzmann methods (2020)

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