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 203 articles )

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  1. Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.; Saunders, Michael A.: Large-scale optimization with linear equality constraints using reduced compact representation (2022)
  2. Fang, Liang; Vandewalle, Stefan; Meyers, Johan: A parallel-in-time multiple shooting algorithm for large-scale PDE-constrained optimal control problems (2022)
  3. Farbod, Davood: Modeling and simulation studies for some truncated discrete distributions generated by stable densities (2022)
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  5. Gorissen, Bram L.: Interior point methods can exploit structure of convex piecewise linear functions with application in radiation therapy (2022)
  6. Leroy, Arthur; Latouche, Pierre; Guedj, Benjamin; Gey, Servane: MAGMA: inference and prediction using multi-task Gaussian processes with common mean (2022)
  7. Toscano-Palmerin, Saul; Frazier, Peter I.: Bayesian optimization with expensive integrands (2022)
  8. Borges, Patrick: Estimating the turning point of the log-logistic hazard function in the presence of long-term survivors with an application for uterine cervical cancer data (2021)
  9. Brust, Johannes J.; Di, Zichao (Wendy); Leyffer, Sven; Petra, Cosmin G.: Compact representations of structured BFGS matrices (2021)
  10. Chen, Hao; Zhang, Minguang; Han, Lanshan; Lim, Alvin: Hierarchical marketing mix models with sign constraints (2021)
  11. Dharmavaram, Sanjay: A gauge-fixing procedure for spherical fluid membranes and application to computations (2021)
  12. Ek, David; Forsgren, Anders: Approximate solution of system of equations arising in interior-point methods for bound-constrained optimization (2021)
  13. Forslund, Robert; Snis, Anders; Larsson, Stig: A greedy algorithm for optimal heating in powder-bed-based additive manufacturing (2021)
  14. Gafurov, Askar; Vinař, Tomáš; Brejová, Broňa: Probabilistic models of (k)-mer frequencies (extended abstract) (2021)
  15. Gajardo, Diego; Mercado, Alberto; Muñoz, Juan Carlos: Identification of the anti-diffusion coefficient for the linear Kuramoto-Sivashinsky equation (2021)
  16. Girolami, Mark; Febrianto, Eky; Yin, Ge; Cirak, Fehmi: The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions (2021)
  17. Grosnit, Antoine; Cowen-Rivers, Alexander I.; Tutunov, Rasul; Griffiths, Ryan-Rhys; Wang, Jun; Bou-Ammar, Haitham: Are we forgetting about compositional optimisers in Bayesian optimisation? (2021)
  18. Hannukainen, Antti; Hyvönen, Nuutti; Perkkiö, Lauri: Inverse heat source problem and experimental design for determining iron loss distribution (2021)
  19. Horvath, Blanka; Muguruza, Aitor; Tomas, Mehdi: Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models (2021)
  20. Kollnig, Konrad; Bientinesi, Paolo; Di Napoli, Edoardo A.: Rational spectral filters with optimal convergence rate (2021)

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