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 539 articles , 1 standard article )

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  1. Cipolla, Stefano; Durastante, Fabio: Fractional PDE constrained optimization: an optimize-then-discretize approach with L-BFGS and approximate inverse preconditioning (2018)
  2. Huang, Shuai; Wan, Zhong; Zhang, Jing: An extended nonmonotone line search technique for large-scale unconstrained optimization (2018)
  3. Andrea, Caliciotti; Giovanni, Fasano; Massimo, Roma: Novel preconditioners based on quasi-Newton updates for nonlinear conjugate gradient methods (2017)
  4. Antunes, Pedro R.S.; Oudet, Édouard: Numerical minimization of Dirichlet Laplacian eigenvalues of four-dimensional geometries (2017)
  5. Burdakov, Oleg; Gong, Lujin; Zikrin, Spartak; Yuan, Ya-xiang: On efficiently combining limited-memory and trust-region techniques (2017)
  6. Cao, Hui-Ping; Li, Dong-Hui: Partitioned quasi-Newton methods for sparse nonlinear equations (2017)
  7. Erway, Jennifer B.; Marcia, Roummel F.: On solving large-scale limited-memory quasi-Newton equations (2017)
  8. Hillar, Christopher J.; Marzen, Sarah E.: Neural network coding of natural images with applications to pure mathematics (2017)
  9. Jensen, T.L.; Diehl, Moritz: An approach for analyzing the global rate of convergence of quasi-Newton and truncated-Newton methods (2017)
  10. Métivier, L.; Brossier, R.; Operto, S.; Virieux, J.: Full waveform inversion and the truncated Newton method (2017)
  11. Mons, Vincent; Chassaing, Jean-Camille; Sagaut, Pierre: Optimal sensor placement for variational data assimilation of unsteady flows past a rotationally oscillating cylinder (2017)
  12. Ruiz-Sarmiento, Jose-Raul; Galindo, Cipriano; Gonzalez-Jimenez, Javier: A survey on learning approaches for undirected graphical models. application to scene object recognition (2017)
  13. Stella, Lorenzo; Themelis, Andreas; Patrinos, Panagiotis: Forward-backward quasi-Newton methods for nonsmooth optimization problems (2017)
  14. Wang, Xiao; Ma, Shiqian; Goldfarb, Donald; Liu, Wei: Stochastic quasi-Newton methods for nonconvex stochastic optimization (2017)
  15. Yang, Yueting; Chen, Yuting; Lu, Yunlong: A subspace conjugate gradient algorithm for large-scale unconstrained optimization (2017)
  16. Yu, Dongjin; Mu, Yunlei; Jin, Yike: Rating prediction using review texts with underlying sentiments (2017)
  17. Auroux, Didier; Blum, Jacques; Ruggiero, Giovanni: Data assimilation for geophysical fluids: the diffusive back and forth nudging (2016)
  18. Biglari, Fahimeh; Mahmoodpur, Farideh: Scaling damped limited-memory updates for unconstrained optimization (2016)
  19. Chang, Jingya; Chen, Yannan; Qi, Liqun: Computing eigenvalues of large scale sparse tensors arising from a hypergraph (2016)
  20. Cherian, Anoop; Sra, Suvrit: Positive definite matrices: data representation and applications to computer vision (2016)

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