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 287 articles , 1 standard article )

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  1. Brás, C. P.; Martínez, J. M.; Raydan, M.: Large-scale unconstrained optimization using separable cubic modeling and matrix-free subspace minimization (2020)
  2. de Zordo-Banliat, M.; Merle, X.; Dergham, G.; Cinnella, P.: Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models (2020)
  3. Dharmavaram, Sanjay; Perotti, Luigi E.: A Lagrangian formulation for interacting particles on a deformable medium (2020)
  4. Hong, David; Kolda, Tamara G.; Duersch, Jed A.: Generalized canonical polyadic tensor decomposition (2020)
  5. McKenna, Sean A.; Akhriev, Albert; Echeverría Ciaurri, David; Zhuk, Sergiy: Efficient uncertainty quantification of reservoir properties for parameter estimation and production forecasting (2020)
  6. Shen, Chungen; Fan, Changxing; Wang, Yunlong; Xue, Wenjuan: Limited memory BFGS algorithm for the matrix approximation problem in Frobenius norm (2020)
  7. Song, Dawei; Seidl, D. Thomas; Oberai, Assad A.: Three-dimensional traction microscopy accounting for cell-induced matrix degradation (2020)
  8. Xu, Yong; Zhang, Hao; Li, Yongge; Zhou, Kuang; Liu, Qi; Kurths, Jürgen: Solving Fokker-Planck equation using deep learning (2020)
  9. Zhang, Shanglong; Gain, Arun L.; Norato, Julián A.: Adaptive mesh refinement for topology optimization with discrete geometric components (2020)
  10. Bachoc, François; Bevilacqua, Moreno; Velandia, Daira: Composite likelihood estimation for a Gaussian process under fixed domain asymptotics (2019)
  11. Becker, Stephen; Fadili, Jalal; Ochs, Peter: On quasi-Newton forward-backward splitting: proximal calculus and convergence (2019)
  12. Boggs, Paul T.; Byrd, Richard H.: Adaptive, limited-memory BFGS algorithms for unconstrained optimization (2019)
  13. Bolancé, Catalina; Vernic, Raluca: Multivariate count data generalized linear models: three approaches based on the Sarmanov distribution (2019)
  14. Brust, Johannes; Burdakov, Oleg; Erway, Jennifer B.; Marcia, Roummel F.: A dense initialization for limited-memory quasi-Newton methods (2019)
  15. Creamer, Germán G.; Lee, Chihoon: A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures (2019)
  16. Debarnot, Valentin; Kahn, Jonas; Weiss, Pierre: Multiview attenuation estimation and correction (2019)
  17. Di Gangi, Leonardo; Lapucci, M.; Schoen, F.; Sortino, A.: An efficient optimization approach for best subset selection in linear regression, with application to model selection and fitting in autoregressive time-series (2019)
  18. Fercoq, Olivier; Bianchi, Pascal: A coordinate-descent primal-dual algorithm with large step size and possibly nonseparable functions (2019)
  19. Gong, Gail; Wang, Wei; Hsieh, Chih-Lin; Van Den Berg, David J.; Haiman, Christopher; Oakley-Girvan, Ingrid; Whittemore, Alice S.: Data-adaptive multi-locus association testing in subjects with arbitrary genealogical relationships (2019)
  20. Gronski, Jessica; Ben Sassi, Mohamed-Amin; Becker, Stephen; Sankaranarayanan, Sriram: Template polyhedra and bilinear optimization (2019)

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