LBFGS-B

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: http://plato.asu.edu)


References in zbMATH (referenced in 211 articles , 1 standard article )

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  1. Attia, Ahmed; Alexanderian, Alen; Saibaba, Arvind K.: Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems (2018)
  2. 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)
  3. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  4. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  5. John Hughes: sklarsomega: An R Package for Measuring Agreement Using Sklar's Omega Coefficient (2018) arXiv
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  9. Rahman, Adam; Oldford, R. Wayne: Euclidean distance matrix completion and point configurations from the minimal spanning tree (2018)
  10. Römer, Ulrich; Narayanamurthi, Mahesh; Sandu, Adrian: Solving parameter estimation problems with discrete adjoint exponential integrators (2018)
  11. Sanchez, Fabio; Barboza, Luis A.; Burton, David; Cintrón-Arias, Ariel: Comparative analysis of dengue versus chikungunya outbreaks in Costa Rica (2018)
  12. Tsilifis, Panagiotis; Browning, William J.; Wood, Thomas E.; Newton, Paul K.; Ghanem, Roger G.: The stochastic quasi-chemical model for bacterial growth: variational Bayesian parameter update (2018)
  13. Zarepisheh, Masoud; Xing, Lei; Ye, Yinyu: A computation study on an integrated alternating direction method of multipliers for large scale optimization (2018)
  14. Beliakov, Gleb; Gómez, Daniel; James, Simon; Montero, Javier; Rodríguez, J. Tinguaro: Approaches to learning strictly-stable weights for data with missing values (2017)
  15. Bowman, Dale; George, E. Olusegun: Weighted least squares estimation for exchangeable binary data (2017)
  16. Chen, Tianyi; Curtis, Frank E.; Robinson, Daniel P.: A reduced-space algorithm for minimizing $\ell_1$-regularized convex functions (2017)
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  18. Laval, J.-P.; Vassilicos, J. C.; Foucaut, J.-M.; Stanislas, M.: Comparison of turbulence profiles in high-Reynolds-number turbulent boundary layers and validation of a predictive model (2017)
  19. Liu, Bo; Chang, Lo-Bin; Geman, Hélyette: Intraday pairs trading strategies on high frequency data: the case of oil companies (2017)
  20. Mao, Qi; Wang, Li; Tsang, Ivor W.: A unified probabilistic framework for robust manifold learning and embedding (2017)

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