L-BFGS-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.


References in zbMATH (referenced in 144 articles )

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  1. Becker, Stephen; Fadili, Jalal; Ochs, Peter: On quasi-Newton forward-backward splitting: proximal calculus and convergence (2019)
  2. Brust, Johannes; Burdakov, Oleg; Erway, Jennifer B.; Marcia, Roummel F.: A dense initialization for limited-memory quasi-Newton methods (2019)
  3. Debarnot, Valentin; Kahn, Jonas; Weiss, Pierre: Multiview attenuation estimation and correction (2019)
  4. Fercoq, Olivier; Bianchi, Pascal: A coordinate-descent primal-dual algorithm with large step size and possibly nonseparable functions (2019)
  5. 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)
  6. Livieris, Ioannis E.: Forecasting economy-related data utilizing weight-constrained recurrent neural networks (2019)
  7. Meshcheryakov Georgy, Igolkina Anna: semopy: A Python package for Structural Equation Modeling (2019) arXiv
  8. O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
  9. Pumi, Guilherme; Valk, Marcio; Bisognin, Cleber; Bayer, Fábio Mariano; Prass, Taiane Schaedler: Beta autoregressive fractionally integrated moving average models (2019)
  10. Sakamoto, Wataru: Bias-reduced marginal Akaike information criteria based on a Monte Carlo method for linear mixed-effects models (2019)
  11. Zhang, Nailong; Yang, Qingyu; Kelleher, Aidan; Si, Wujun: A new mixture cure model under competing risks to score online consumer loans (2019)
  12. Attia, Ahmed; Alexanderian, Alen; Saibaba, Arvind K.: Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems (2018)
  13. Banović, Mladen; Mykhaskiv, Orest; Auriemma, Salvatore; Walther, Andrea; Legrand, Herve; Müller, Jens-Dominik: Algorithmic differentiation of the Open CASCADE technology CAD kernel and its coupling with an adjoint CFD solver (2018)
  14. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  15. Brauchart, Johann S.; Dragnev, Peter D.; Saff, Edward B.; Womersley, Robert S.: Logarithmic and Riesz equilibrium for multiple sources on the sphere: the exceptional case (2018)
  16. Mei, Yue; Yu, Peng: Mapping heterogeneous elastic property distribution of soft tissues using harmonic motion data: a theoretical study (2018)
  17. Michel, T.; Fehrenbach, J.; Lobjois, V.; Laurent, J.; Gomes, A.; Colin, T.; Poignard, Clair: Mathematical modeling of the proliferation gradient in multicellular tumor spheroids (2018)
  18. Moye, Matthew J.; Diekman, Casey O.: Data assimilation methods for neuronal state and parameter estimation (2018)
  19. Nguyen, Thi Nhat Anh; Bouzerdoum, Abdesselam; Phung, Son Lam: Stochastic variational hierarchical mixture of sparse Gaussian processes for regression (2018)
  20. Schmitz, Morgan A.; Heitz, Matthieu; Bonneel, Nicolas; Ngolè, Fred; Coeurjolly, David; Cuturi, Marco; Peyré, Gabriel; Starck, Jean-Luc: Wasserstein dictionary learning: optimal transport-based unsupervised nonlinear dictionary learning (2018)

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