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 124 articles )

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  1. Attia, Ahmed; Alexanderian, Alen; Saibaba, Arvind K.: Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems (2018)
  2. 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)
  3. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  4. 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)
  5. Jerker Nordh: pyParticleEst: A Python Framework for Particle-Based Estimation Methods (2017)
  6. Krislock, Nathan; Malick, Jér^ome; Roupin, Frédéric: BiqCrunch: a semidefinite branch-and-bound method for solving binary quadratic problems (2017)
  7. Zabinyako, Gerard Idelfonovich: Applications of quasi-Newton algorithms for solving large scale problems (2017)
  8. Csercsik, Dávid: Lying generators: manipulability of centralized payoff mechanisms in electrical energy trade (2016)
  9. Long, Chengjiang; Hua, Gang; Kapoor, Ashish: A joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing (2016)
  10. Pawela, Łukasz; Sadowski, Przemysław: Various methods of optimizing control pulses for quantum systems with decoherence (2016)
  11. Wang, Peng; Shen, Chunhua; van den Hengel, Anton; Torr, Philip H. S.: Efficient semidefinite branch-and-cut for MAP-MRF inference (2016)
  12. 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)
  13. 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)
  14. Gallard, François; Mohammadi, Bijan; Montagnac, Marc; Meaux, Matthieu: An adaptive multipoint formulation for robust parametric optimization (2015)
  15. Lampariello, F.; Liuzzi, G.: A filling function method for unconstrained global optimization (2015)
  16. Mishra, Asitav; Mani, Karthik; Mavriplis, Dimitri; Sitaraman, Jay: Time dependent adjoint-based optimization for coupled fluid-structure problems (2015)
  17. Mohy-ud-Din, Hassan; Robinson, Daniel P.: A solver for nonconvex bound-constrained quadratic optimization (2015)
  18. Oferkin, I. V.; Zheltkov, D. A.; Tyrtyshnikov, E. E.; Sulimov, A. V.; Kutov, D. K.; Sulimov, V. B.: Evaluation of the docking algorithm based on tensor train global optimization (2015)
  19. Potyka, Nico; Beierle, Christoph; Kern-Isberner, Gabriele: A concept for the evolution of relational probabilistic belief states and the computation of their changes under optimum entropy semantics (2015)
  20. Simon, Moritz; Ulbrich, Michael: Adjoint based optimal control of partially miscible two-phase flow in porous media with applications to CO$_2$ sequestration in underground reservoirs (2015)

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