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 342 articles , 1 standard article )

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  1. Tarpey, Thaddeus; Petkova, Eva: Latent regression analysis (2010)
  2. Tsai, Frank T.-C.: Bayesian model averaging assessment on groundwater management under model structure uncertainty (2010)
  3. Wei, Jiawei; Zhou, Lan: Model selection using modified AIC and BIC in joint modeling of paired functional data (2010)
  4. Abramov, Rafail V.: The multidimensional moment-constrained maximum entropy problem: A BFGS algorithm with constraint scaling (2009)
  5. Alexe, Mihai; Sandu, Adrian: Forward and adjoint sensitivity analysis with continuous explicit Runge-Kutta schemes (2009)
  6. Beaulieu, François D.; Champagne, Benoît: Design of prototype filters for perfect reconstruction DFT filter bank transceivers (2009)
  7. Bermúdez, José D.; Corberán-Vallet, Ana; Vercher, Enriqueta: Multivariate exponential smoothing: a Bayesian forecast approach based on simulation (2009)
  8. Bermúdez, José D.; Corberán-Vallet, Ana; Vercher, Enriqueta: Forecasting time series with missing data using Holt’s model (2009)
  9. Fischbacher, Thomas: The many vacua of gauged extended supergravities (2009)
  10. Groenwold, Albert A.; Etman, L. F. P.; Kok, Schalk; Wood, Derren W.; Tosserams, Simon: An augmented Lagrangian approach to non-convex SAO using diagonal quadratic approximations (2009) ioport
  11. Lim, Yong B.; Park, Yeo Jung; Huh, Myung-Hoe: D-optimality criterion for weighting variables in K-means clustering (2009)
  12. Sainvitu, Caroline: How much do approximate derivatives hurt filter methods? (2009)
  13. Schwarz, Roland; Wolf, Matthias; Müller, Tobias: A probabilistic model of cell size reduction in \textitPseudo-nitzschia delicatissima (Bacillariophyta) (2009)
  14. Srinath, D. N.; Mittal, S.: Optimal airfoil shapes for low Reynolds number flows (2009)
  15. Sun, Li; He, Guoping; Wang, Yongli; Fang, Liang: An active set quasi-Newton method with projected search for bound constrained minimization (2009)
  16. Wood, Derren W.; Groenwold, Albert A.: Non-convex dual forms based on exponential intervening variables, with application to weight minimization (2009)
  17. Birgin, E. G.; Martínez, J. M.: Structured minimal-memory inexact quasi-Newton method and secant preconditioners for augmented Lagrangian optimization (2008)
  18. Carmichael, Gregory R.; Sandu, Adrian; Chai, Tianfeng; Daescu, Dacian N.; Constantinescu, Emil M.; Tang, Youhua: Predicting air quality: Improvements through advanced methods to integrate models and measurements (2008)
  19. Chen, Jein-Shan; Pan, Shaohua: A descent method for a reformulation of the second-order cone complementarity problem (2008)
  20. El Serafy, G. Y.; Heemink, A. W.; van Geer, F. C.: Identification of ground water flow patterns using particle models (2008)

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