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 482 articles , 1 standard article )

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  1. Biglari, Fahimeh; Mahmoodpur, Farideh: Scaling damped limited-memory updates for unconstrained optimization (2016)
  2. Fatemi, M.: An optimal parameter for dai-liao family of conjugate gradient methods (2016)
  3. Gould, N.; Ortner, C.; Packwood, D.: A dimer-type saddle search algorithm with preconditioning and linesearch (2016)
  4. Larsson, Lisa J.; Choksi, Rustum; Nave, Jean-Christophe: Geometric self-assembly of rigid shapes: a simple Voronoi approach (2016)
  5. Liu, Ya-Feng; Diao, Rui; Ye, Feng; Liu, Hong-Wei: An efficient inexact Newton-CG algorithm for the smallest enclosing ball problem of large dimensions (2016)
  6. Pawela, Łukasz; Sadowski, Przemysław: Various methods of optimizing control pulses for quantum systems with decoherence (2016)
  7. Rahpeymaii, Farzad; Kimiaei, Morteza; Bagheri, Alireza: A limited memory quasi-Newton trust-region method for box constrained optimization (2016)
  8. Schöpfer, Frank: Linear convergence of descent methods for the unconstrained minimization of restricted strongly convex functions (2016)
  9. Shen, Chungen; Zhang, Lei-Hong; Yang, Wei Hong: A filter active-set algorithm for ball/sphere constrained optimization problem (2016)
  10. Shi, Zhanwen; Yang, Guanyu; Xiao, Yunhai: A limited memory BFGS algorithm for non-convex minimization with applications in matrix largest eigenvalue problem (2016)
  11. Vaksman, Gregory; Zibulevsky, Michael; Elad, Michael: Patch ordering as a regularization for inverse problems in image processing (2016)
  12. Van Haaren, Jan; Van den Broeck, Guy; Meert, Wannes; Davis, Jesse: Lifted generative learning of Markov logic networks (2016)
  13. Andrei, Neculai: A new three-term conjugate gradient algorithm for unconstrained optimization (2015)
  14. Barthelmé, Simon: Fast matrix computations for functional additive models (2015)
  15. Biglari, Fahimeh; Ebadian, Ali: Limited memory BFGS method based on a high-order tensor model (2015)
  16. Blesgen, Thomas: On rotation deformation zones for finite-strain Cosserat plasticity (2015)
  17. Brune, Peter R.; Knepley, Matthew G.; Smith, Barry F.; Tu, Xuemin: Composing scalable nonlinear algebraic solvers (2015)
  18. Chen, Yannan; Sun, Wenyu: A dwindling filter line search method for unconstrained optimization (2015)
  19. Cui, Ming: Adjoint-free calculation method for conditional nonlinear optimal perturbations (2015)
  20. Curtis, Frank E.; Que, Xiaocun: A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees (2015)

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