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. (Source:

References in zbMATH (referenced in 235 articles , 1 standard article )

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  1. Bolancé, Catalina; Vernic, Raluca: Multivariate count data generalized linear models: three approaches based on the Sarmanov distribution (2019)
  2. Fercoq, Olivier; Bianchi, Pascal: A coordinate-descent primal-dual algorithm with large step size and possibly nonseparable functions (2019)
  3. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  4. Kirschstein, Thomas; Liebscher, Steffen; Pandolfo, Giuseppe; Porzio, Giovanni C.; Ragozini, Giancarlo: On finite-sample robustness of directional location estimators (2019)
  5. O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
  6. Tsokos, Alkeos; Narayanan, Santhosh; Kosmidis, Ioannis; Baio, Gianluca; Cucuringu, Mihai; Whitaker, Gavin; Király, Franz: Modeling outcomes of soccer matches (2019)
  7. Aggarwal, Manu; Hussaini, M. Y.; De La Fuente, Leonardo; Navarrete, Fernando; Cogan, N. G.: A framework for model analysis across multiple experiment regimes: investigating effects of zinc on \textitXylellafastidiosa as a case study (2018)
  8. Attia, Ahmed; Alexanderian, Alen; Saibaba, Arvind K.: Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems (2018)
  9. 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)
  10. Barbero, Álvaro; Sra, Suvrit: Modular proximal optimization for multidimensional total-variation regularization (2018)
  11. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  12. Bogert, Kenneth; Doshi, Prashant: Multi-robot inverse reinforcement learning under occlusion with estimation of state transitions (2018)
  13. 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)
  14. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  15. Jafrasteh, Bahram; Fathianpour, Nader; Suárez, Alberto: Comparison of machine learning methods for copper ore grade estimation (2018)
  16. John Hughes: sklarsomega: An R Package for Measuring Agreement Using Sklar's Omega Coefficient (2018) arXiv
  17. Kirschstein, Thomas: Planning of multi-product pipelines by economic lot scheduling models (2018)
  18. Klemens, Fabian; Schuhmann, Sebastian; Guthausen, Gisela; Thäter, Gudrun; Krause, Mathias J.: CFD-MRI: A coupled measurement and simulation approach for accurate fluid flow characterisation and domain identification (2018)
  19. Michel, T.; Fehrenbach, J.; Lobjois, V.; Laurent, J.; Gomes, A.; Colin, T.; Poignard, Clair: Mathematical modeling of the proliferation gradient in multicellular tumor spheroids (2018)
  20. Moghadam, M. Bameni; Moghadam, Masoud Ghaderi; Rafiey, Saeedreza; Naderi, Mohamad Hosein: An extension of Banerjee and Rahim’s model for economic design of (\barX) control charts under generalized exponential shock model (2018)

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