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 154 articles )

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  1. Letham, Benjamin; Rudin, Cynthia; Madigan, David: Sequential event prediction (2013)
  2. Lockwood, Brian; Mavriplis, Dimitri: Gradient-based methods for uncertainty quantification in hypersonic flows (2013)
  3. Shen, Chunhua; Li, Hanxi; van den Hengel, Anton: Fully corrective boosting with arbitrary loss and regularization (2013)
  4. Birgin, Ernesto G.; Gentil, Jan M.: Evaluating bound-constrained minimization software (2012)
  5. Birgin, Ernesto G.; Martínez, J. M.: Augmented Lagrangian method with nonmonotone penalty parameters for constrained optimization (2012)
  6. Busseti, Enzo; Lillo, Fabrizio: Calibration of optimal execution of financial transactions in the presence of transient market impact (2012)
  7. Cioaca, Alexandru; Alexe, Mihai; Sandu, Adrian: Second-order adjoints for solving PDE-constrained optimization problems (2012)
  8. Hansen, Hallstein Asheim; Schneider, Gerardo; Steffen, Martin: Reachability analysis of non-linear planar autonomous systems (2012)
  9. Lessig, C.; de Witt, T.; Fiume, E.: Efficient and accurate rotation of finite spherical harmonics expansions (2012)
  10. Nickisch, Hannes: Glm-ie: generalised linear models inference & estimation toolbox (2012) ioport
  11. Tacheny, N.; Troestler, C.: A mountain pass algorithm with projector (2012)
  12. Benešová, Barbora: Global optimization numerical strategies for rate-independent processes (2011)
  13. Byrd, Richard H.; Waltz, Richard A.: An active-set algorithm for nonlinear programming using parametric linear programming (2011)
  14. Egidi, N.; Maponi, P.: Residual correction techniques for the efficient solution of inverse scattering problems (2011)
  15. Morales, José Luis; Nocedal, Jorge: Remark on “Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization” (2011)
  16. Piacentini, M.; Rinaldi, F.: Path loss prediction in urban environment using learning machines and dimensionality reduction techniques (2011)
  17. Qian, Dong; Phadke, Manas; Karpov, Eduard; Liu, Wing Kam: A domain-reduction approach to bridging-scale simulation of one-dimensional nanostructures (2011)
  18. Rachowicz, W.; Zdunek, A.: Application of the FEM with adaptivity for electromagnetic inverse medium scattering problems (2011)
  19. Xiao, Yun-Hai; Hu, Qing-Jie; Wei, Zengxin: Modified active set projected spectral gradient method for bound constrained optimization (2011)
  20. Yuan, Gonglin; Lu, Xiwen: An active set limited memory BFGS algorithm for bound constrained optimization (2011)