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 805 articles , 1 standard article )

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  1. Boullé, Nicolas; Farrell, Patrick E.; Paganini, Alberto: Control of bifurcation structures using shape optimization (2022)
  2. Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.; Saunders, Michael A.: Large-scale optimization with linear equality constraints using reduced compact representation (2022)
  3. Deepho, Jitsupa; Abubakar, Auwal Bala; Malik, Maulana; Argyros, Ioannis K.: Solving unconstrained optimization problems via hybrid CD-DY conjugate gradient methods with applications (2022)
  4. Guo, Ivan; Loeper, Grégoire; Obłój, Jan; Wang, Shiyi: Joint modeling and calibration of SPX and VIX by optimal transport (2022)
  5. Lai, Xiangjing; Hao, Jin-Kao; Yue, Dong; Lü, Zhipeng; Fu, Zhang-Hua: Iterated dynamic thresholding search for packing equal circles into a circular container (2022)
  6. Mejía-de-Dios, Jesús-Adolfo; Mezura-Montes, Efrén; Toledo-Hernández, Porfirio: Pseudo-feasible solutions in evolutionary bilevel optimization: test problems and performance assessment (2022)
  7. Mo, Yifan; Ling, Liming; Zeng, Delu: Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm (2022)
  8. Ren, Pu; Rao, Chengping; Liu, Yang; Wang, Jian-Xun; Sun, Hao: PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (2022)
  9. Shi, Hao-Jun M.; Xie, Yuchen; Byrd, Richard; Nocedal, Jorge: A noise-tolerant quasi-Newton algorithm for unconstrained optimization (2022)
  10. Wang, Hengjie; Planas, Robert; Chandramowlishwaran, Aparna; Bostanabad, Ramin: Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (2022)
  11. Waziri, Mohammed Yusuf; Ahmed, Kabiru; Halilu, Abubakar Sani: A modified PRP-type conjugate gradient projection algorithm for solving large-scale monotone nonlinear equations with convex constraint (2022)
  12. Awwal, A. M.; Kumam, Poom; Mohammad, Hassan; Watthayu, Wiboonsak; Abubakar, A. B.: A Perry-type derivative-free algorithm for solving nonlinear system of equations and minimizing (\ell_1) regularized problem (2021)
  13. Bai, Yuexing; Chaolu, Temuer; Bilige, Sudao: Physics informed by deep learning: numerical solutions of modified Korteweg-de Vries equation (2021)
  14. Bauer, Martin; Charon, Nicolas; Harms, Philipp; Hsieh, Hsi-Wei: A numerical framework for elastic surface matching, comparison, and interpolation (2021)
  15. Beck, Nicholas; Di Bernardino, Elena; Mailhot, Mélina: Semi-parametric estimation of multivariate extreme expectiles (2021)
  16. Bousquet, Arthur; Li, Yukun; Wang, Guanqian: Some algorithms for the mean curvature flow under topological changes (2021)
  17. Brust, Johannes J.; Di, Zichao (Wendy); Leyffer, Sven; Petra, Cosmin G.: Compact representations of structured BFGS matrices (2021)
  18. Buhmann, Martin; Siegel, Dirk: Implementing and modifying Broyden class updates for large scale optimization (2021)
  19. Cao, Jian; Genton, Marc G.; Keyes, David E.; Turkiyyah, George M.: Exploiting low-rank covariance structures for computing high-dimensional normal and Student-(t) probabilities (2021)
  20. Civitelli, Enrico; Lapucci, Matteo; Schoen, Fabio; Sortino, Alessio: An effective procedure for feature subset selection in logistic regression based on information criteria (2021)

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