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 189 articles , 1 standard article )

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  4. Zarepisheh, Masoud; Xing, Lei; Ye, Yinyu: A computation study on an integrated alternating direction method of multipliers for large scale optimization (2018)
  5. Beliakov, Gleb; Gómez, Daniel; James, Simon; Montero, Javier; Rodríguez, J.Tinguaro: Approaches to learning strictly-stable weights for data with missing values (2017)
  6. Bowman, Dale; George, E.Olusegun: Weighted least squares estimation for exchangeable binary data (2017)
  7. Chen, Tianyi; Curtis, Frank E.; Robinson, Daniel P.: A reduced-space algorithm for minimizing $\ell_1$-regularized convex functions (2017)
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  9. Mao, Qi; Wang, Li; Tsang, Ivor W.: A unified probabilistic framework for robust manifold learning and embedding (2017)
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  12. Racine, Jeffrey S.; Li, Kevin: Nonparametric conditional quantile estimation: a locally weighted quantile kernel approach (2017)
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  19. Comets, Francis; Falconnet, Mikael; Loukianov, Oleg; Loukianova, Dasha: Maximum likelihood estimator consistency for recurrent random walk in a parametric random environment with finite support (2016)
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