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 135 articles , 1 standard article )

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  1. 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)
  2. De Lozzo, Matthias; Marrel, Amandine: Estimation of the derivative-based global sensitivity measures using a Gaussian process metamodel (2016)
  3. Dennis, Emily B.; Morgan, Byron J.T.; Freeman, Stephen N.; Roy, David B.; Brereton, Tom: Dynamic models for longitudinal butterfly data (2016)
  4. 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)
  5. Pawela, Łukasz; Sadowski, Przemysław: Various methods of optimizing control pulses for quantum systems with decoherence (2016)
  6. Rahpeymaii, Farzad; Kimiaei, Morteza; Bagheri, Alireza: A limited memory quasi-Newton trust-region method for box constrained optimization (2016)
  7. 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)
  8. Andreoletti, Pierre; Loukianova, Dasha; Matias, Catherine: Hidden Markov model for parameter estimation of a random walk in a Markov environment (2015)
  9. Bitvai, Zsolt; Cohn, Trevor: Day trading profit maximization with multi-task learning and technical analysis (2015)
  10. Boehm, Christian; Ulbrich, Michael: A semismooth Newton-CG method for constrained parameter identification in seismic tomography (2015)
  11. Delicado, Pedro; Vieu, Philippe: Optimal level sets for bivariate density representation (2015)
  12. Gallard, François; Mohammadi, Bijan; Montagnac, Marc; Meaux, Matthieu: An adaptive multipoint formulation for robust parametric optimization (2015)
  13. Giordan, Marco; Wehrens, Ron: A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data (2015)
  14. Gündüz, Selim; Genç, Ali İ.: The distribution of the quotient of two triangularly distributed random variables (2015)
  15. Jain, Sambhav; Bhatt, Varun Dhananjay; Mittal, Sanjay: Shape optimization of corrugated airfoils (2015)
  16. Jones, M.C.; Pewsey, Arthur; Kato, Shogo: On a class of circulas: copulas for circular distributions (2015)
  17. Lampariello, F.; Liuzzi, G.: A filling function method for unconstrained global optimization (2015)
  18. Mohy-ud-Din, Hassan; Robinson, Daniel P.: A solver for nonconvex bound-constrained quadratic optimization (2015)
  19. Moutoussamy, Vincent; Nanty, Simon; Pauwels, Beno^ıt: Emulators for stochastic simulation codes (2015)
  20. Oferkin, I.V.; Zheltkov, D.A.; Tyrtyshnikov, E.E.; Sulimov, A.V.; Kutov, D.K.; Sulimov, V.B.: Evaluation of the docking algorithm based on tensor train global optimization (2015)

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