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 191 articles , 1 standard article )

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  1. Kirschstein, Thomas: Planning of multi-product pipelines by economic lot scheduling models (2018)
  2. Rahman, Adam; Oldford, R. Wayne: Euclidean distance matrix completion and point configurations from the minimal spanning tree (2018)
  3. 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)
  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)
  8. Karimi, Sahar; Vavasis, Stephen: IMRO: A proximal quasi-Newton method for solving $\ell_1$-regularized least squares problems (2017)
  9. 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)
  10. Mao, Qi; Wang, Li; Tsang, Ivor W.: A unified probabilistic framework for robust manifold learning and embedding (2017)
  11. Métivier, L.; Brossier, R.; Operto, S.; Virieux, J.: Full waveform inversion and the truncated Newton method (2017)
  12. Owen, N.E.; Challenor, P.; Menon, P.P.; Bennani, S.: Comparison of surrogate-based uncertainty quantification methods for computationally expensive simulators (2017)
  13. Racine, Jeffrey S.; Li, Kevin: Nonparametric conditional quantile estimation: a locally weighted quantile kernel approach (2017)
  14. Stiegelmeier, Elenice W.; Oliveira, Vilma A.; Silva, Geraldo N.; Karam, Décio: Optimal weed population control using nonlinear programming (2017)
  15. Zaidi, Nayyar A.; Webb, Geoffrey I.; Carman, Mark J.; Petitjean, François; Buntine, Wray; Hynes, Mike; De Sterck, Hans: Efficient parameter learning of Bayesian network classifiers (2017)
  16. Abe, Hiroyasu; Yadohisa, Hiroshi: Automatic relevance determination in nonnegative matrix factorization based on a zero-inflated compound Poisson-gamma distribution (2016)
  17. Azzimonti, Dario; Bect, Julien; Chevalier, Clément; Ginsbourger, David: Quantifying uncertainties on excursion sets under a Gaussian random field prior (2016)
  18. Calandra, Roberto; Seyfarth, André; Peters, Jan; Deisenroth, Marc Peter: Bayesian optimization for learning gaits under uncertainty. An experimental comparison on a dynamic bipedal walker (2016) ioport
  19. Chen, Dai-Qiang; Zhou, Yan; Song, Li-Juan: Fixed point algorithm based on adapted metric method for convex minimization problem with application to image deblurring (2016)
  20. 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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