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 606 articles , 1 standard article )

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  1. Fercoq, Olivier; Bianchi, Pascal: A coordinate-descent primal-dual algorithm with large step size and possibly nonseparable functions (2019)
  2. Gao, Wenbo; Goldfarb, Donald: Quasi-Newton methods: superlinear convergence without line searches for self-concordant functions (2019)
  3. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  4. Vlček, Jan; Lukšan, Ladislav: A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions (2019)
  5. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)
  6. Attia, Ahmed; Alexanderian, Alen; Saibaba, Arvind K.: Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems (2018)
  7. 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)
  8. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  9. Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge: Optimization methods for large-scale machine learning (2018)
  10. Brauchart, Johann S.; Dragnev, Peter D.; Saff, Edward B.; Womersley, Robert S.: Logarithmic and Riesz equilibrium for multiple sources on the sphere: the exceptional case (2018)
  11. Chen, Ning; Zhu, Jun; Chen, Jianfei; Chen, Ting: Dropout training for SVMs with data augmentation (2018)
  12. Cipolla, Stefano; Durastante, Fabio: Fractional PDE constrained optimization: an optimize-then-discretize approach with L-BFGS and approximate inverse preconditioning (2018)
  13. Eckstein, Jonathan; Yao, Wang: Relative-error approximate versions of Douglas-Rachford splitting and special cases of the ADMM (2018)
  14. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  15. Fernández-Cara, Enrique; Maestre, Faustino: An inverse problem in elastography involving Lamé systems (2018)
  16. Gao, Wenbo; Goldfarb, Donald: Block BFGS methods (2018)
  17. Gelashvili, Koba; Khutsishvili, Irina; Gorgadze, Luka; Alkhazishvili, Lela: Speeding up the convergence of the Polyak’s heavy ball algorithm (2018)
  18. Gu, Mengyang; Wang, Long: Scaled Gaussian stochastic process for computer model calibration and prediction (2018)
  19. He, Kun; Ye, Hui; Wang, Zhengli; Liu, Jingfa: An efficient quasi-physical quasi-human algorithm for packing equal circles in a circular container (2018)
  20. Hillar, Christopher J.; Tran, Ngoc M.: Robust exponential memory in Hopfield networks (2018)

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