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:

References in zbMATH (referenced in 351 articles , 1 standard article )

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  1. Bagirov, Adil M.; Taheri, Sona; Cimen, Emre: Incremental DC optimization algorithm for large-scale clusterwise linear regression (2021)
  2. Beck, Nicholas; Di Bernardino, Elena; Mailhot, Mélina: Semi-parametric estimation of multivariate extreme expectiles (2021)
  3. Courbot, Jean-Baptiste; Colicchio, Bruno: A fast homotopy algorithm for gridless sparse recovery (2021)
  4. Croix, Jean-Charles; Durrande, Nicolas; Alvarez, Mauricio A.: Bayesian inversion of a diffusion model with application to biology (2021)
  5. Dierckx, Goedele; Goegebeur, Yuri; Guillou, Armelle: Local robust estimation of Pareto-type tails with random right censoring (2021)
  6. Ek, David; Forsgren, Anders: Approximate solution of system of equations arising in interior-point methods for bound-constrained optimization (2021)
  7. Gajardo, Diego; Mercado, Alberto; Muñoz, Juan Carlos: Identification of the anti-diffusion coefficient for the linear Kuramoto-Sivashinsky equation (2021)
  8. Gambella, Claudio; Ghaddar, Bissan; Naoum-Sawaya, Joe: Optimization problems for machine learning: a survey (2021)
  9. Girolami, Mark; Febrianto, Eky; Yin, Ge; Cirak, Fehmi: The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions (2021)
  10. Kolkiewicz, Adam; Rice, Gregory; Xie, Yijun: Projection pursuit based tests of normality with functional data (2021)
  11. Lin, Huihui; Chaganty, N. Rao: Multivariate distributions of correlated binary variables generated by pair-copulas (2021)
  12. Lu, Lu; Meng, Xuhui; Mao, Zhiping; Karniadakis, George Em: DeepXDE: a deep learning library for solving differential equations (2021)
  13. Ma, Chenxin; Jaggi, Martin; Curtis, Frank E.; Srebro, Nathan; Takáč, Martin: An accelerated communication-efficient primal-dual optimization framework for structured machine learning (2021)
  14. Muñoz Grajales, Juan Carlos: Non-homogeneous boundary value problems for some KdV-type equations on a finite interval: a numerical approach (2021)
  15. Nakamura-Zimmerer, Tenavi; Gong, Qi; Kang, Wei: Adaptive deep learning for high-dimensional Hamilton-Jacobi-Bellman equations (2021)
  16. Rath, Katharina; Albert, Christopher G.; Bischl, Bernd; von Toussaint, Udo: Symplectic Gaussian process regression of maps in Hamiltonian systems (2021)
  17. Rioux, Gabriel; Choksi, Rustum; Hoheisel, Tim; Maréchal, Pierre; Scarvelis, Christopher: The maximum entropy on the mean method for image deblurring (2021)
  18. Sun, Yanli; Wang, Xinyu; Guo, Xu; Mei, Yue: Adhesion behavior of an extensible soft thin film-substrate system based on finite deformation theory (2021)
  19. Yuan, Zhenfei; Hu, Taizhong: pyvine: the Python package for regular vine copula modeling, sampling and testing (2021)
  20. Zhang, Jin; Wang, Cong; Chen, Guoqing: A review selection method for finding an informative subset from online reviews (2021)

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