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 132 articles )

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  1. Fercoq, Olivier; Bianchi, Pascal: A coordinate-descent primal-dual algorithm with large step size and possibly nonseparable functions (2019)
  2. O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
  3. Sakamoto, Wataru: Bias-reduced marginal Akaike information criteria based on a Monte Carlo method for linear mixed-effects models (2019)
  4. Attia, Ahmed; Alexanderian, Alen; Saibaba, Arvind K.: Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems (2018)
  5. 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)
  6. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  7. 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)
  8. 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)
  9. Moye, Matthew J.; Diekman, Casey O.: Data assimilation methods for neuronal state and parameter estimation (2018)
  10. Nguyen, Thi Nhat Anh; Bouzerdoum, Abdesselam; Phung, Son Lam: Stochastic variational hierarchical mixture of sparse Gaussian processes for regression (2018)
  11. 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)
  12. Jerker Nordh: pyParticleEst: A Python Framework for Particle-Based Estimation Methods (2017) not zbMATH
  13. Krislock, Nathan; Malick, Jérôme; Roupin, Frédéric: BiqCrunch: a semidefinite branch-and-bound method for solving binary quadratic problems (2017)
  14. Zabinyako, Gerard Idelfonovich: Applications of quasi-Newton algorithms for solving large scale problems (2017)
  15. Csercsik, Dávid: Lying generators: manipulability of centralized payoff mechanisms in electrical energy trade (2016)
  16. Long, Chengjiang; Hua, Gang; Kapoor, Ashish: A joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing (2016)
  17. Pawela, Łukasz; Sadowski, Przemysław: Various methods of optimizing control pulses for quantum systems with decoherence (2016)
  18. Wang, Peng; Shen, Chunhua; van den Hengel, Anton; Torr, Philip H. S.: Efficient semidefinite branch-and-cut for MAP-MRF inference (2016)
  19. Zahr, M. J.; Persson, P.-O.: An adjoint method for a high-order discretization of deforming domain conservation laws for optimization of flow problems (2016)
  20. Zahr, M. J.; Persson, P.-O.; Wilkening, J.: A fully discrete adjoint method for optimization of flow problems on deforming domains with time-periodicity constraints (2016)

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