L-BFGS-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.


References in zbMATH (referenced in 134 articles )

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  1. Yu, Guangxu; Müller, Jens-Dominik; Jones, Dominic; Christakopoulos, Faidon: CAD-based shape optimisation using adjoint sensitivities (2011)
  2. Zhu, Wenxing; Lin, Geng: A dynamic convexized method for nonconvex mixed integer nonlinear programming (2011)
  3. Abellan, A.; Noetinger, B.: Optimizing subsurface field data acquisition using information theory (2010)
  4. Chen, Chaohui; Wang, Yudou; Li, Gaoming; Reynolds, Albert C.: Closed-loop reservoir management on the Brugge test case (2010)
  5. Chi, Jing: Automatic and self-adaptive facial expression tracking (2010)
  6. Egidi, N.; Maponi, P.: An efficient method for the solution of the inverse scattering problem for penetrable obstacles (2010)
  7. Park, Jinhae; Chen, Feng; Shen, Jie: Modeling and simulation of switchings in ferroelectric liquid crystals (2010)
  8. Pytlak, R.; Tarnawski, T.: Preconditioned conjugate gradient algorithms for nonconvex problems with box constraints (2010)
  9. Rasch, Arno; Bücker, H. Martin: EFCOSS: an interactive environment facilitating optimal experimental design (2010)
  10. Rumpfkeil, Markus P.; Zingg, David W.: A hybrid algorithm for far-field noise minimization (2010)
  11. Wen, Zaiwen; Yin, Wotao; Goldfarb, Donald; Zhang, Yin: A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization, and continuation (2010)
  12. Alexe, Mihai; Sandu, Adrian: Forward and adjoint sensitivity analysis with continuous explicit Runge-Kutta schemes (2009)
  13. Beaulieu, François D.; Champagne, Benoît: Design of prototype filters for perfect reconstruction DFT filter bank transceivers (2009)
  14. Boussaa, Djaffar: Optimization of temperature-dependent functionally graded material bodies (2009)
  15. Kim, Hyun Keol; Hielscher, Andreas H.: A PDE-constrained SQP algorithm for optical tomography based on the frequency-domain equation of radiative transfer (2009)
  16. Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan: Algorithm 896: LSA: algorithms for large-scale optimization (2009)
  17. Raoul Grasman; Han van der Maas; Eric-Jan Wagenmakers: Fitting the Cusp Catastrophe in R: A cusp Package Primer (2009) not zbMATH
  18. Tseng, Paul; Yun, Sangwoon: A coordinate gradient descent method for nonsmooth separable minimization (2009)
  19. Birgin, E. G.; Martínez, J. M.: Structured minimal-memory inexact quasi-Newton method and secant preconditioners for augmented Lagrangian optimization (2008)
  20. 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)