L-BFGS

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 552 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)
  2. Huang, Shuai; Wan, Zhong; Zhang, Jing: An extended nonmonotone line search technique for large-scale unconstrained optimization (2018)
  3. Li, Min: A modified Hestense-Stiefel conjugate gradient method close to the memoryless BFGS quasi-Newton method (2018)
  4. Raissi, Maziar; Karniadakis, George Em: Hidden physics models: machine learning of nonlinear partial differential equations (2018)
  5. Andrea, Caliciotti; Giovanni, Fasano; Massimo, Roma: Novel preconditioners based on quasi-Newton updates for nonlinear conjugate gradient methods (2017)
  6. Antunes, Pedro R.S.; Oudet, Édouard: Numerical minimization of Dirichlet Laplacian eigenvalues of four-dimensional geometries (2017)
  7. Auroux, D.; Groza, V.: Optimal parameters identification and sensitivity study for abrasive waterjet milling model (2017)
  8. Burdakov, Oleg; Gong, Lujin; Zikrin, Spartak; Yuan, Ya-xiang: On efficiently combining limited-memory and trust-region techniques (2017)
  9. Cao, Hui-Ping; Li, Dong-Hui: Partitioned quasi-Newton methods for sparse nonlinear equations (2017)
  10. Chen, Jingrun; García-Cervera, Carlos J.: An efficient multigrid strategy for large-scale molecular mechanics optimization (2017)
  11. Erway, Jennifer B.; Marcia, Roummel F.: On solving large-scale limited-memory quasi-Newton equations (2017)
  12. Feng, Wensen; Qiao, Peng; Xi, Xuanyang; Chen, Yunjin: Image denoising via multiscale nonlinear diffusion models (2017)
  13. Gower, Robert M.; Richtárik, Peter: Randomized quasi-Newton updates are linearly convergent matrix inversion algorithms (2017)
  14. Hillar, Christopher J.; Marzen, Sarah E.: Neural network coding of natural images with applications to pure mathematics (2017)
  15. Jensen, T.L.; Diehl, Moritz: An approach for analyzing the global rate of convergence of quasi-Newton and truncated-Newton methods (2017)
  16. Métivier, L.; Brossier, R.; Operto, S.; Virieux, J.: Full waveform inversion and the truncated Newton method (2017)
  17. Mons, Vincent; Chassaing, Jean-Camille; Sagaut, Pierre: Optimal sensor placement for variational data assimilation of unsteady flows past a rotationally oscillating cylinder (2017)
  18. Nosratipour, Hadi; Hashemi Borzabadi, Akbar; Solaymani Fard, Omid: On the nonmonotonicity degree of nonmonotone line searches (2017)
  19. Raissi, Maziar; Perdikaris, Paris; Karniadakis, George Em: Machine learning of linear differential equations using Gaussian processes (2017)
  20. Raissi, Maziar; Perdikaris, Paris; Karniadakis, George Em: Inferring solutions of differential equations using noisy multi-fidelity data (2017)

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