L-BFGS

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 629 articles , 1 standard article )

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  1. Chen, Ke; Grapiglia, Geovani Nunes; Yuan, Jinyun; Zhang, Daoping: Improved optimization methods for image registration problems (2019)
  2. Fercoq, Olivier; Bianchi, Pascal: A coordinate-descent primal-dual algorithm with large step size and possibly nonseparable functions (2019)
  3. Gao, Wenbo; Goldfarb, Donald: Quasi-Newton methods: superlinear convergence without line searches for self-concordant functions (2019)
  4. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  5. O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
  6. Pegon, Paul; Santambrogio, Filippo; Xia, Qinglan: A fractal shape optimization problem in branched transport (2019)
  7. Vlček, Jan; Lukšan, Ladislav: Properties of the block BFGS update and its application to the limited-memory block BNS method for unconstrained minimization (2019)
  8. Vlček, Jan; Lukšan, Ladislav: A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions (2019)
  9. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)
  10. Attia, Ahmed; Alexanderian, Alen; Saibaba, Arvind K.: Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems (2018)
  11. Baluch, Bakhtawar; Salleh, Zabidin; Alhawarat, Ahmad: A new modified three-term Hestenes-Stiefel conjugate gradient method with sufficient descent property and its global convergence (2018)
  12. 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)
  13. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  14. Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge: Optimization methods for large-scale machine learning (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. Chen, Ning; Zhu, Jun; Chen, Jianfei; Chen, Ting: Dropout training for SVMs with data augmentation (2018)
  17. Cipolla, Stefano; Durastante, Fabio: Fractional PDE constrained optimization: an optimize-then-discretize approach with L-BFGS and approximate inverse preconditioning (2018)
  18. Eckstein, Jonathan; Yao, Wang: Relative-error approximate versions of Douglas-Rachford splitting and special cases of the ADMM (2018)
  19. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  20. Fernández-Cara, Enrique; Maestre, Faustino: An inverse problem in elastography involving Lamé systems (2018)

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