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 701 articles , 1 standard article )

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  1. Andrei, Neculai: New conjugate gradient algorithms based on self-scaling memoryless Broyden-Fletcher-Goldfarb-Shanno method (2020)
  2. Andrei, Neculai: Diagonal approximation of the Hessian by finite differences for unconstrained optimization (2020)
  3. Andrei, Neculai: A double parameter self-scaling memoryless BFGS method for unconstrained optimization (2020)
  4. Asl, Azam; Overton, Michael L.: Analysis of the gradient method with an Armijo-Wolfe line search on a class of non-smooth convex functions (2020)
  5. Berahas, Albert S.; Takáč, Martin: A robust multi-batch L-BFGS method for machine learning (2020)
  6. Bouzid, Mouaouia Cherif; Salhi, Said: Packing rectangles into a fixed size circular container: constructive and metaheuristic search approaches (2020)
  7. Chandramouli, Pranav; Memin, Etienne; Heitz, Dominique: 4D large scale variational data assimilation of a turbulent flow with a dynamics error model (2020)
  8. de Zordo-Banliat, M.; Merle, X.; Dergham, G.; Cinnella, P.: Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models (2020)
  9. Dharmavaram, Sanjay; Perotti, Luigi E.: A Lagrangian formulation for interacting particles on a deformable medium (2020)
  10. Erway, Jennifer B.; Griffin, Joshua; Marcia, Roummel F.; Omheni, Riadh: Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations (2020)
  11. Gonçalves, M. L. N.; Prudente, L. F.: On the extension of the Hager-Zhang conjugate gradient method for vector optimization (2020)
  12. Li, Min: A three term Polak-Ribière-Polyak conjugate gradient method close to the memoryless BFGS quasi-Newton method (2020)
  13. Liu, Zexian; Liu, Hongwei; Dai, Yu-Hong: An improved Dai-Kou conjugate gradient algorithm for unconstrained optimization (2020)
  14. McKenna, Sean A.; Akhriev, Albert; Echeverría Ciaurri, David; Zhuk, Sergiy: Efficient uncertainty quantification of reservoir properties for parameter estimation and production forecasting (2020)
  15. Nguyen-Thanh, Vien Minh; Zhuang, Xiaoying; Rabczuk, Timon: A deep energy method for finite deformation hyperelasticity (2020)
  16. Shen, Chungen; Fan, Changxing; Wang, Yunlong; Xue, Wenjuan: Limited memory BFGS algorithm for the matrix approximation problem in Frobenius norm (2020)
  17. van den Berg, Ewout: A hybrid quasi-Newton projected-gradient method with application to lasso and basis-pursuit denoising (2020)
  18. Xu, Yong; Zhang, Hao; Li, Yongge; Zhou, Kuang; Liu, Qi; Kurths, Jürgen: Solving Fokker-Planck equation using deep learning (2020)
  19. Yousefian, Farzad; Nedić, Angelia; Shanbhag, Uday V.: On stochastic and deterministic quasi-Newton methods for nonstrongly convex optimization: asymptotic convergence and rate analysis (2020)
  20. Ahookhosh, Masoud; Neumaier, Arnold: An optimal subgradient algorithm with subspace search for costly convex optimization problems (2019)

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