LBFGS-B

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. (Source: http://plato.asu.edu)


References in zbMATH (referenced in 342 articles , 1 standard article )

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  1. Volkening, Alexandria; Linder, Daniel F.; Porter, Mason A.; Rempala, Grzegorz A.: Forecasting elections using compartmental models of infection (2020)
  2. Wei, Wei; Dai, Hua; Liang, Weitai: A novel projected gradient-like method for optimization problems with simple constraints (2020)
  3. Xin, Hua; Zhu, Jian-Ping: Accelerated life testing for double-truncated general half normal distribution (2020)
  4. Xu, Yong; Zhang, Hao; Li, Yongge; Zhou, Kuang; Liu, Qi; Kurths, Jürgen: Solving Fokker-Planck equation using deep learning (2020)
  5. Zhang, Shanglong; Gain, Arun L.; Norato, Julián A.: Adaptive mesh refinement for topology optimization with discrete geometric components (2020)
  6. Bachoc, François; Bevilacqua, Moreno; Velandia, Daira: Composite likelihood estimation for a Gaussian process under fixed domain asymptotics (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. Bolancé, Catalina; Vernic, Raluca: Multivariate count data generalized linear models: three approaches based on the Sarmanov distribution (2019)
  10. Brust, Johannes; Burdakov, Oleg; Erway, Jennifer B.; Marcia, Roummel F.: A dense initialization for limited-memory quasi-Newton methods (2019)
  11. Creamer, Germán G.; Lee, Chihoon: A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures (2019)
  12. Debarnot, Valentin; Kahn, Jonas; Weiss, Pierre: Multiview attenuation estimation and correction (2019)
  13. Di Gangi, Leonardo; Lapucci, M.; Schoen, F.; Sortino, A.: An efficient optimization approach for best subset selection in linear regression, with application to model selection and fitting in autoregressive time-series (2019)
  14. Fercoq, Olivier; Bianchi, Pascal: A coordinate-descent primal-dual algorithm with large step size and possibly nonseparable functions (2019)
  15. Ferreiro, Ana M.; García-Rodríguez, José Antonio; Vázquez, Carlos; Costa e Silva, E.; Correia, A.: Parallel two-phase methods for global optimization on GPU (2019)
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  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. Keshavarz, Hossein; Nguyen, XuanLong; Scott, Clayton: Local inversion-free estimation of spatial Gaussian processes (2019)
  20. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)

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