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 543 articles , 1 standard article )

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  1. Cipolla, Stefano; Durastante, Fabio: Fractional PDE constrained optimization: an optimize-then-discretize approach with L-BFGS and approximate inverse preconditioning (2018)
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  3. Andrea, Caliciotti; Giovanni, Fasano; Massimo, Roma: Novel preconditioners based on quasi-Newton updates for nonlinear conjugate gradient methods (2017)
  4. Antunes, Pedro R.S.; Oudet, Édouard: Numerical minimization of Dirichlet Laplacian eigenvalues of four-dimensional geometries (2017)
  5. Burdakov, Oleg; Gong, Lujin; Zikrin, Spartak; Yuan, Ya-xiang: On efficiently combining limited-memory and trust-region techniques (2017)
  6. Cao, Hui-Ping; Li, Dong-Hui: Partitioned quasi-Newton methods for sparse nonlinear equations (2017)
  7. Chen, Jingrun; García-Cervera, Carlos J.: An efficient multigrid strategy for large-scale molecular mechanics optimization (2017)
  8. Erway, Jennifer B.; Marcia, Roummel F.: On solving large-scale limited-memory quasi-Newton equations (2017)
  9. Feng, Wensen; Qiao, Peng; Xi, Xuanyang; Chen, Yunjin: Image denoising via multiscale nonlinear diffusion models (2017)
  10. Hillar, Christopher J.; Marzen, Sarah E.: Neural network coding of natural images with applications to pure mathematics (2017)
  11. Jensen, T.L.; Diehl, Moritz: An approach for analyzing the global rate of convergence of quasi-Newton and truncated-Newton methods (2017)
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  13. Mons, Vincent; Chassaing, Jean-Camille; Sagaut, Pierre: Optimal sensor placement for variational data assimilation of unsteady flows past a rotationally oscillating cylinder (2017)
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  16. Stella, Lorenzo; Themelis, Andreas; Patrinos, Panagiotis: Forward-backward quasi-Newton methods for nonsmooth optimization problems (2017)
  17. Wang, Xiao; Ma, Shiqian; Goldfarb, Donald; Liu, Wei: Stochastic quasi-Newton methods for nonconvex stochastic optimization (2017)
  18. Yang, Yueting; Chen, Yuting; Lu, Yunlong: A subspace conjugate gradient algorithm for large-scale unconstrained optimization (2017)
  19. Yu, Dongjin; Mu, Yunlei; Jin, Yike: Rating prediction using review texts with underlying sentiments (2017)
  20. Auroux, Didier; Blum, Jacques; Ruggiero, Giovanni: Data assimilation for geophysical fluids: the diffusive back and forth nudging (2016)

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