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 154 articles , 1 standard article )

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  1. Karimi, Sahar; Vavasis, Stephen: IMRO: A proximal quasi-Newton method for solving $\ell_1$-regularized least squares problems (2017)
  2. Métivier, L.; Brossier, R.; Operto, S.; Virieux, J.: Full waveform inversion and the truncated Newton method (2017)
  3. Owen, N.E.; Challenor, P.; Menon, P.P.; Bennani, S.: Comparison of surrogate-based uncertainty quantification methods for computationally expensive simulators (2017)
  4. Azzimonti, Dario; Bect, Julien; Chevalier, Clément; Ginsbourger, David: Quantifying uncertainties on excursion sets under a Gaussian random field prior (2016)
  5. 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
  6. 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)
  7. 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)
  8. Csercsik, Dávid: Lying generators: manipulability of centralized payoff mechanisms in electrical energy trade (2016)
  9. De Lozzo, Matthias; Marrel, Amandine: Estimation of the derivative-based global sensitivity measures using a Gaussian process metamodel (2016)
  10. Dennis, Emily B.; Morgan, Byron J.T.; Freeman, Stephen N.; Roy, David B.; Brereton, Tom: Dynamic models for longitudinal butterfly data (2016)
  11. Keshavarz, Hossein; Scott, Clayton; Nguyen, XuanLong: On the consistency of inversion-free parameter estimation for Gaussian random fields (2016)
  12. Krislock, Nathan; Malick, Jér^ome; Roupin, Frédéric: Computational results of a semidefinite branch-and-bound algorithm for $k$-cluster (2016)
  13. Latouche, Pierre; Mattei, Pierre-Alexandre; Bouveyron, Charles; Chiquet, Julien: Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression (2016)
  14. Mérigot, Quentin; Mirebeau, Jean-Marie: Minimal geodesics along volume-preserving maps, through semidiscrete optimal transport (2016)
  15. Pawela, Łukasz; Sadowski, Przemysław: Various methods of optimizing control pulses for quantum systems with decoherence (2016)
  16. Rahpeymaii, Farzad; Kimiaei, Morteza; Bagheri, Alireza: A limited memory quasi-Newton trust-region method for box constrained optimization (2016)
  17. Tripathy, Rohit; Bilionis, Ilias; Gonzalez, Marcial: Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation (2016)
  18. Wang, Yong; Zhou, Guanglu; Zhang, Xin; Liu, Wanquan; Caccetta, Louis: The non-convex sparse problem with nonnegative constraint for signal reconstruction (2016)
  19. Zaidi, Nayyar A.; Webb, Geoffrey I.; Carman, Mark J.; Petitjean, François; Cerquides, Jesús: $\textALR^n$: accelerated higher-order logistic regression (2016)
  20. Zhang, Bo; Liu, Wei; Zhang, Hui; Chen, Qihui; Zhang, Zhiwei: Composite likelihood and maximum likelihood methods for joint latent class modeling of disease prevalence and high-dimensional semicontinuous biomarker data (2016)

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