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 519 articles , 1 standard article )

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  1. Antunes, Pedro R.S.; Oudet, Édouard: Numerical minimization of Dirichlet Laplacian eigenvalues of four-dimensional geometries (2017)
  2. Hillar, Christopher J.; Marzen, Sarah E.: Neural network coding of natural images with applications to pure mathematics (2017)
  3. Jensen, T.L.; Diehl, Moritz: An approach for analyzing the global rate of convergence of quasi-Newton and truncated-Newton methods (2017)
  4. Métivier, L.; Brossier, R.; Operto, S.; Virieux, J.: Full waveform inversion and the truncated Newton method (2017)
  5. Wang, Xiao; Ma, Shiqian; Goldfarb, Donald; Liu, Wei: Stochastic quasi-Newton methods for nonconvex stochastic optimization (2017)
  6. Yu, Dongjin; Mu, Yunlei; Jin, Yike: Rating prediction using review texts with underlying sentiments (2017)
  7. Biglari, Fahimeh; Mahmoodpur, Farideh: Scaling damped limited-memory updates for unconstrained optimization (2016)
  8. Chang, Jingya; Chen, Yannan; Qi, Liqun: Computing eigenvalues of large scale sparse tensors arising from a hypergraph (2016)
  9. Cherian, Anoop; Sra, Suvrit: Positive definite matrices: data representation and applications to computer vision (2016)
  10. Csercsik, Dávid: Lying generators: manipulability of centralized payoff mechanisms in electrical energy trade (2016)
  11. Dai, YuHong; Kou, CaiXia: A Barzilai-Borwein conjugate gradient method (2016)
  12. Fatemi, M.: An optimal parameter for Dai-Liao family of conjugate gradient methods (2016)
  13. Feng, Wensen; Qiao, Hong; Chen, Yunjin: Poisson noise reduction with higher-order natural image prior model (2016)
  14. Gould, N.; Ortner, C.; Packwood, D.: A dimer-type saddle search algorithm with preconditioning and linesearch (2016)
  15. Larsson, Lisa J.; Choksi, Rustum; Nave, Jean-Christophe: Geometric self-assembly of rigid shapes: a simple Voronoi approach (2016)
  16. Leonenko, Vasiliy N.; Ivanov, Sergey V.: Fitting the SEIR model of seasonal influenza outbreak to the incidence data for Russian cities (2016)
  17. Liu, Ya-Feng; Diao, Rui; Ye, Feng; Liu, Hong-Wei: An efficient inexact Newton-CG algorithm for the smallest enclosing ball problem of large dimensions (2016)
  18. Métivier, L.; Brossier, R.; Mérigot, Q.; Oudet, E.; Virieux, J.: An optimal transport approach for seismic tomography: application to 3D full waveform inversion (2016)
  19. Mons, V.; Chassaing, J.-C.; Gomez, T.; Sagaut, P.: Reconstruction of unsteady viscous flows using data assimilation schemes (2016)
  20. Ochs, Peter; Ranftl, René; Brox, Thomas; Pock, Thomas: Techniques for gradient-based bilevel optimization with non-smooth lower level problems (2016)

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