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 116 articles )

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  1. Jerker Nordh: pyParticleEst: A Python Framework for Particle-Based Estimation Methods (2017)
  2. Krislock, Nathan; Malick, Jér^ome; Roupin, Frédéric: BiqCrunch: a semidefinite branch-and-bound method for solving binary quadratic problems (2017)
  3. Zabinyako, Gerard Idelfonovich: Applications of quasi-Newton algorithms for solving large scale problems (2017)
  4. Csercsik, Dávid: Lying generators: manipulability of centralized payoff mechanisms in electrical energy trade (2016)
  5. Pawela, Łukasz; Sadowski, Przemysław: Various methods of optimizing control pulses for quantum systems with decoherence (2016)
  6. 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)
  7. Gallard, François; Mohammadi, Bijan; Montagnac, Marc; Meaux, Matthieu: An adaptive multipoint formulation for robust parametric optimization (2015)
  8. Lampariello, F.; Liuzzi, G.: A filling function method for unconstrained global optimization (2015)
  9. Mishra, Asitav; Mani, Karthik; Mavriplis, Dimitri; Sitaraman, Jay: Time dependent adjoint-based optimization for coupled fluid-structure problems (2015)
  10. Mohy-ud-Din, Hassan; Robinson, Daniel P.: A solver for nonconvex bound-constrained quadratic optimization (2015)
  11. 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)
  12. 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)
  13. 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)
  14. Cioaca, Alexandru; Sandu, Adrian: Low-rank approximations for computing observation impact in 4D-Var data assimilation (2014)
  15. Cioaca, Alexandru; Sandu, Adrian: An optimization framework to improve 4D-Var data assimilation system performance (2014)
  16. John Nash: On Best Practice Optimization Methods in R (2014)
  17. Krislock, Nathan; Malick, Jér^ome; Roupin, Frédéric: Improved semidefinite bounding procedure for solving max-cut problems to optimality (2014)
  18. Kurbatsky, V. G.; Sidorov, D. N.; Spiryaev, V. A.; Tomin, N. V.: Forecasting nonstationary time series based on Hilbert-Huang transform and machine learning (2014)
  19. Kurbatsky, Victor Grigorevich; Leahy, Paul; Spiryaev, Vadim Aleksandrovich; Tomin, Nikita Viktorovich; Sidorov, Denis Nikolaevich; Zhukov, Aleksei Vitalevich: Power system parameters forecasting using Hilbert-Huang transform and machine learning (2014)
  20. Le Thi, Hoai An; Huynh Van Ngai; Dinh, Tao Pham; Vaz, A. Ismael F.; Vicente, L. N.: Globally convergent DC trust-region methods (2014)

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