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 584 articles , 1 standard article )

Showing results 1 to 20 of 584.
Sorted by year (citations)

1 2 3 ... 28 29 30 next

  1. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)
  2. Attia, Ahmed; Alexanderian, Alen; Saibaba, Arvind K.: Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems (2018)
  3. Banović, Mladen; Mykhaskiv, Orest; Auriemma, Salvatore; Walther, Andrea; Legrand, Herve; Müller, Jens-Dominik: Algorithmic differentiation of the Open CASCADE technology CAD kernel and its coupling with an adjoint CFD solver (2018)
  4. Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge: Optimization methods for large-scale machine learning (2018)
  5. Cipolla, Stefano; Durastante, Fabio: Fractional PDE constrained optimization: an optimize-then-discretize approach with L-BFGS and approximate inverse preconditioning (2018)
  6. Eckstein, Jonathan; Yao, Wang: Relative-error approximate versions of Douglas-Rachford splitting and special cases of the ADMM (2018)
  7. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  8. Fernández-Cara, Enrique; Maestre, Faustino: An inverse problem in elastography involving Lamé systems (2018)
  9. Gao, Wenbo; Goldfarb, Donald: Block BFGS methods (2018)
  10. Gelashvili, Koba; Khutsishvili, Irina; Gorgadze, Luka; Alkhazishvili, Lela: Speeding up the convergence of the Polyak’s heavy ball algorithm (2018)
  11. He, Kun; Ye, Hui; Wang, Zhengli; Liu, Jingfa: An efficient quasi-physical quasi-human algorithm for packing equal circles in a circular container (2018)
  12. Hillar, Christopher J.; Tran, Ngoc M.: Robust exponential memory in Hopfield networks (2018)
  13. Holtmann-Rice, Daniel N.; Kunsberg, Benjamin S.; Zucker, Steven W.: Tensors, differential geometry and statistical shading analysis (2018)
  14. Huang, Shuai; Wan, Zhong; Zhang, Jing: An extended nonmonotone line search technique for large-scale unconstrained optimization (2018)
  15. Li, Kuan; Jackson, Andrew; Livermore, Philip W.: Taylor state dynamos found by optimal control: axisymmetric examples (2018)
  16. Li, Min: A modified Hestense-Stiefel conjugate gradient method close to the memoryless BFGS quasi-Newton method (2018)
  17. Mérigot, Quentin; Meyron, Jocelyn; Thibert, Boris: An algorithm for optimal transport between a simplex soup and a point cloud (2018)
  18. Raissi, Maziar; Karniadakis, George Em: Hidden physics models: machine learning of nonlinear partial differential equations (2018)
  19. Schäfer, Dirk; Hüllermeier, Eyke: Dyad ranking using Plackett-Luce models based on joint feature representations (2018)
  20. Themelis, Andreas; Stella, Lorenzo; Patrinos, Panagiotis: Forward-backward envelope for the sum of two nonconvex functions: further properties and nonmonotone linesearch algorithms (2018)

1 2 3 ... 28 29 30 next