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 564 articles , 1 standard article )

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  1. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)
  2. Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge: Optimization methods for large-scale machine learning (2018)
  3. Cipolla, Stefano; Durastante, Fabio: Fractional PDE constrained optimization: an optimize-then-discretize approach with L-BFGS and approximate inverse preconditioning (2018)
  4. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  5. Gao, Wenbo; Goldfarb, Donald: Block BFGS methods (2018)
  6. Gelashvili, Koba; Khutsishvili, Irina; Gorgadze, Luka; Alkhazishvili, Lela: Speeding up the convergence of the Polyak’s heavy ball algorithm (2018)
  7. Huang, Shuai; Wan, Zhong; Zhang, Jing: An extended nonmonotone line search technique for large-scale unconstrained optimization (2018)
  8. Li, Min: A modified Hestense-Stiefel conjugate gradient method close to the memoryless BFGS quasi-Newton method (2018)
  9. Raissi, Maziar; Karniadakis, George Em: Hidden physics models: machine learning of nonlinear partial differential equations (2018)
  10. Schäfer, Dirk; Hüllermeier, Eyke: Dyad ranking using Plackett-Luce models based on joint feature representations (2018)
  11. Torrisi, Giampaolo; Grammatico, Sergio; Smith, Roy S.; Morari, Manfred: A projected gradient and constraint linearization method for nonlinear model predictive control (2018)
  12. Andrea, Caliciotti; Giovanni, Fasano; Massimo, Roma: Novel preconditioners based on quasi-Newton updates for nonlinear conjugate gradient methods (2017)
  13. Antunes, Pedro R. S.; Oudet, Édouard: Numerical minimization of Dirichlet Laplacian eigenvalues of four-dimensional geometries (2017)
  14. Auroux, D.; Groza, V.: Optimal parameters identification and sensitivity study for abrasive waterjet milling model (2017)
  15. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  16. Burdakov, Oleg; Gong, Lujin; Zikrin, Spartak; Yuan, Ya-xiang: On efficiently combining limited-memory and trust-region techniques (2017)
  17. Cao, Hui-Ping; Li, Dong-Hui: Partitioned quasi-Newton methods for sparse nonlinear equations (2017)
  18. Chen, Jingrun; García-Cervera, Carlos J.: An efficient multigrid strategy for large-scale molecular mechanics optimization (2017)
  19. Erway, Jennifer B.; Marcia, Roummel F.: On solving large-scale limited-memory quasi-Newton equations (2017)
  20. Feng, Wensen; Qiao, Peng; Xi, Xuanyang; Chen, Yunjin: Image denoising via multiscale nonlinear diffusion models (2017)

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