L-BFGS

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 737 articles , 1 standard article )

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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. Al-Baali, Mehiddin; Caliciotti, Andrea; Fasano, Giovanni; Roma, Massimo: A class of approximate inverse preconditioners based on Krylov-subspace methods for large-scale nonconvex optimization (2020)
  3. Al-Siyabi, Ahmed; Al-Baali, Mehiddin: New basic Hessian approximations for large-scale nonlinear least-squares optimization (2020)
  4. Andrei, Neculai: New conjugate gradient algorithms based on self-scaling memoryless Broyden-Fletcher-Goldfarb-Shanno method (2020)
  5. Andrei, Neculai: A double parameter self-scaling memoryless BFGS method for unconstrained optimization (2020)
  6. Andrei, Neculai: Diagonal approximation of the Hessian by finite differences for unconstrained optimization (2020)
  7. Asl, Azam; Overton, Michael L.: Analysis of the gradient method with an Armijo-Wolfe line search on a class of non-smooth convex functions (2020)
  8. Baghfalaki, Taban; Ganjali, Mojtaba: A transition model for analyzing multivariate longitudinal data using Gaussian copula approach (2020)
  9. Barratt, Shane; Angeris, Guillermo; Boyd, Stephen: Minimizing a sum of clipped convex functions (2020)
  10. Berahas, Albert S.; Takáč, Martin: A robust multi-batch L-BFGS method for machine learning (2020)
  11. Bouzid, Mouaouia Cherif; Salhi, Said: Packing rectangles into a fixed size circular container: constructive and metaheuristic search approaches (2020)
  12. Carmon, Yair; Duchi, John C.; Hinder, Oliver; Sidford, Aaron: Lower bounds for finding stationary points I (2020)
  13. Chandramouli, Pranav; Memin, Etienne; Heitz, Dominique: 4D large scale variational data assimilation of a turbulent flow with a dynamics error model (2020)
  14. de Zordo-Banliat, M.; Merle, X.; Dergham, G.; Cinnella, P.: Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models (2020)
  15. Dharmavaram, Sanjay; Perotti, Luigi E.: A Lagrangian formulation for interacting particles on a deformable medium (2020)
  16. Erway, Jennifer B.; Griffin, Joshua; Marcia, Roummel F.; Omheni, Riadh: Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations (2020)
  17. 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)
  18. Geng, Zhenglin; Johnson, Daniel; Fedkiw, Ronald: Coercing machine learning to output physically accurate results (2020)
  19. Gonçalves, M. L. N.; Prudente, L. F.: On the extension of the Hager-Zhang conjugate gradient method for vector optimization (2020)
  20. Hartmann, Valentin; Schuhmacher, Dominic: Semi-discrete optimal transport: a solution procedure for the unsquared Euclidean distance case (2020)

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