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:

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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. Brust, Johannes J.; Di, Zichao (Wendy); Leyffer, Sven; Petra, Cosmin G.: Compact representations of structured BFGS matrices (2021)
  4. Courbot, Jean-Baptiste; Colicchio, Bruno: A fast homotopy algorithm for gridless sparse recovery (2021)
  5. Croix, Jean-Charles; Durrande, Nicolas; Alvarez, Mauricio A.: Bayesian inversion of a diffusion model with application to biology (2021)
  6. Dharmavaram, Sanjay: A gauge-fixing procedure for spherical fluid membranes and application to computations (2021)
  7. Dierckx, Goedele; Goegebeur, Yuri; Guillou, Armelle: Local robust estimation of Pareto-type tails with random right censoring (2021)
  8. Ek, David; Forsgren, Anders: Approximate solution of system of equations arising in interior-point methods for bound-constrained optimization (2021)
  9. Gajardo, Diego; Mercado, Alberto; Muñoz, Juan Carlos: Identification of the anti-diffusion coefficient for the linear Kuramoto-Sivashinsky equation (2021)
  10. Gambella, Claudio; Ghaddar, Bissan; Naoum-Sawaya, Joe: Optimization problems for machine learning: a survey (2021)
  11. Girolami, Mark; Febrianto, Eky; Yin, Ge; Cirak, Fehmi: The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions (2021)
  12. Hrga, Timotej; Povh, Janez: \textttMADAM: a parallel exact solver for max-cut based on semidefinite programming and ADMM (2021)
  13. Itzá Balam, Reymundo; Hernandez-Lopez, Francisco; Trejo-Sánchez, Joel; Uh Zapata, Miguel: An immersed boundary neural network for solving elliptic equations with singular forces on arbitrary domains (2021)
  14. Jakubik, Johannes; Binding, Adrian; Feuerriegel, Stefan: Directed particle swarm optimization with Gaussian-process-based function forecasting (2021)
  15. Jin, Bangti; Zhou, Zhi: Recovering the potential and order in one-dimensional time-fractional diffusion with unknown initial condition and source (2021)
  16. Kan, Kelvin; Fung, Samy Wu; Ruthotto, Lars: PNKH-B: a projected Newton-Krylov method for large-scale bound-constrained optimization (2021)
  17. Kolkiewicz, Adam; Rice, Gregory; Xie, Yijun: Projection pursuit based tests of normality with functional data (2021)
  18. Kollnig, Konrad; Bientinesi, Paolo; Di Napoli, Edoardo A.: Rational spectral filters with optimal convergence rate (2021)
  19. Legrain, Grégory: Non-negative moment fitting quadrature rules for fictitious domain methods (2021)
  20. Lin, Huihui; Chaganty, N. Rao: Multivariate distributions of correlated binary variables generated by pair-copulas (2021)

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