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 287 articles , 1 standard article )

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  1. 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)
  2. Keshavarz, Hossein; Nguyen, XuanLong; Scott, Clayton: Local inversion-free estimation of spatial Gaussian processes (2019)
  3. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  4. Kirschstein, Thomas; Liebscher, Steffen; Pandolfo, Giuseppe; Porzio, Giovanni C.; Ragozini, Giancarlo: On finite-sample robustness of directional location estimators (2019)
  5. Le Coz, Sebastian; Cheptou, Pierre-Olivier; Peyrard, Nathalie: A spatial Markovian framework for estimating regional and local dynamics of annual plants with dormancy (2019)
  6. Lee, Ching-pei; Wright, Stephen J.: Inexact successive quadratic approximation for regularized optimization (2019)
  7. Lin, L.; Schatz, M.; Sornette, D.: A simple mechanism for financial bubbles: time-varying momentum horizon (2019)
  8. Livieris, Ioannis E.: Forecasting economy-related data utilizing weight-constrained recurrent neural networks (2019)
  9. Mourad, Aya; Rosier, Carole: A nonlinear optimization method applied to the hydraulic conductivity identification in unconfined aquifers (2019)
  10. Ochs, Peter; Pock, Thomas: Adaptive FISTA for nonconvex optimization (2019)
  11. O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
  12. Pernot, Jean-Philippe; Michelucci, Dominique; Daniel, Marc; Foufou, Sebti: Towards a better integration of modelers and black box constraint solvers within the product design process (2019)
  13. Prabakaran, Sellamuthu: Construction of the Black-Scholes PDE with jump-diffusion model (2019)
  14. Pumi, Guilherme; Valk, Marcio; Bisognin, Cleber; Bayer, Fábio Mariano; Prass, Taiane Schaedler: Beta autoregressive fractionally integrated moving average models (2019)
  15. Sun, Furong; Gramacy, Robert B.; Haaland, Benjamin; Lawrence, Earl; Walker, Andrew: Emulating satellite drag from large simulation experiments (2019)
  16. Tsokos, Alkeos; Narayanan, Santhosh; Kosmidis, Ioannis; Baio, Gianluca; Cucuringu, Mihai; Whitaker, Gavin; Király, Franz: Modeling outcomes of soccer matches (2019)
  17. Wang, Chun; Xu, Gongjun; Zhang, Xue: Correction for item response theory latent trait measurement error in linear mixed effects models (2019)
  18. Xu, Jinchao; Li, Yukun; Wu, Shuonan; Bousquet, Arthur: On the stability and accuracy of partially and fully implicit schemes for phase field modeling (2019)
  19. Zhang, Nailong; Yang, Qingyu; Kelleher, Aidan; Si, Wujun: A new mixture cure model under competing risks to score online consumer loans (2019)
  20. Zhang, Shanglong; Le, Chau; Gain, Arun L.; Norato, Julián A.: Fatigue-based topology optimization with non-proportional loads (2019)

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