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 669 articles , 1 standard article )

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  1. Berahas, Albert S.; Takáč, Martin: A robust multi-batch L-BFGS method for machine learning (2020)
  2. Li, Min: A three term Polak-Ribière-Polyak conjugate gradient method close to the memoryless BFGS quasi-Newton method (2020)
  3. Ahookhosh, Masoud; Neumaier, Arnold: An optimal subgradient algorithm with subspace search for costly convex optimization problems (2019)
  4. Andrei, Neculai: A new diagonal quasi-Newton updating method with scaled forward finite differences directional derivative for unconstrained optimization (2019)
  5. Andrei, Neculai: A diagonal quasi-Newton updating method for unconstrained optimization (2019)
  6. Bagattini, Francesco; Schoen, Fabio; Tigli, Luca: Clustering methods for large scale geometrical global optimization (2019)
  7. Becker, Stephen; Fadili, Jalal; Ochs, Peter: On quasi-Newton forward-backward splitting: proximal calculus and convergence (2019)
  8. Boggs, Paul T.; Byrd, Richard H.: Adaptive, limited-memory BFGS algorithms for unconstrained optimization (2019)
  9. Brust, Johannes; Burdakov, Oleg; Erway, Jennifer B.; Marcia, Roummel F.: A dense initialization for limited-memory quasi-Newton methods (2019)
  10. Chen, Ke; Grapiglia, Geovani Nunes; Yuan, Jinyun; Zhang, Daoping: Improved optimization methods for image registration problems (2019)
  11. Debarnot, Valentin; Kahn, Jonas; Weiss, Pierre: Multiview attenuation estimation and correction (2019)
  12. Fard, Omid Solaymani; Sarani, Farhad; Borzabadi, Akbar Hashemi; Nosratipour, Hadi: A nonmonotone line search for the LBFGS method in parabolic optimal control problems. (2019)
  13. Fatemi, Masoud: A new conjugate gradient method with an efficient memory structure (2019)
  14. Fercoq, Olivier; Bianchi, Pascal: A coordinate-descent primal-dual algorithm with large step size and possibly nonseparable functions (2019)
  15. Gao, Wenbo; Goldfarb, Donald: Quasi-Newton methods: superlinear convergence without line searches for self-concordant functions (2019)
  16. Ghadimi, Saeed; Lan, Guanghui; Zhang, Hongchao: Generalized uniformly optimal methods for nonlinear programming (2019)
  17. Gu, Mengyang: Jointly robust prior for Gaussian stochastic process in emulation, calibration and variable selection (2019)
  18. Józsa, Tamas I.; Balaras, E.; Kashtalyan, M.; Borthwick, A. G. L.; Viola, I. M.: Active and passive in-plane wall fluctuations in turbulent channel flows (2019)
  19. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  20. Lee, Ching-pei; Wright, Stephen J.: Inexact successive quadratic approximation for regularized optimization (2019)

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