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. (Source:

References in zbMATH (referenced in 431 articles , 1 standard article )

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  1. Azimi, Seyede Sedighe; Bahrami Samani, Ehsan; Ganjali, Mojtaba: Analysis of mixed correlated overdispersed binomial and ordinal longitudinal responses: LogLindley-Binomial and ordinal random effects model (2022)
  2. Betsch, Steffen; Ebner, Bruno; Nestmann, Franz: Characterizations of non-normalized discrete probability distributions and their application in statistics (2022)
  3. Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.; Saunders, Michael A.: Large-scale optimization with linear equality constraints using reduced compact representation (2022)
  4. Bui-Thanh, Tan; Li, Qin; Zepeda-Núñez, Leonardo: Bridging and improving theoretical and computational electrical impedance tomography via data completion (2022)
  5. Chen, Zheng; Liu, Liu; Mu, Lin: Solving the linear transport equation by a deep neural network approach (2022)
  6. Curley, B.: A nonlinear measurement error model and its application to describing the dependency of health outcomes on dietary intake (2022)
  7. da Silva, Sérgio Luiz E. F.; Silva, R.; dos Santos Lima, Gustavo Z.; de Araújo, João M.; Corso, Gilberto: An outlier-resistant (\kappa)-generalized approach for robust physical parameter estimation (2022)
  8. Fang, Liang; Vandewalle, Stefan; Meyers, Johan: A parallel-in-time multiple shooting algorithm for large-scale PDE-constrained optimal control problems (2022)
  9. Fuenzalida-Henriquez, Ignacio; Oumaziz, Paul; Castillo-Ibarra, Emilio; Hinojosa, Jorge: Global-local non intrusive analysis with Robin parameters: application to plastic hardening behavior and crack propagation in 2D and 3D structures (2022)
  10. Gallagher, Shannon K.; Follmann, Dean: Branching process models to identify risk factors for infectious disease transmission (2022)
  11. Giannelli, Luigi; Sgroi, Pierpaolo; Brown, Jonathon; Paraoanu, Gheorghe Sorin; Paternostro, Mauro; Paladino, Elisabetta; Falci, Giuseppe: A tutorial on optimal control and reinforcement learning methods for quantum technologies (2022)
  12. Gorissen, Bram L.: Interior point methods can exploit structure of convex piecewise linear functions with application in radiation therapy (2022)
  13. Henn, L. L.: Limitations and performance of three approaches to Bayesian inference for Gaussian copula regression models of discrete data (2022)
  14. Hofmann, S.; Borzì, A.: A sequential quadratic Hamiltonian algorithm for training explicit RK neural networks (2022)
  15. Kimiaei, Morteza; Neumaier, Arnold; Azmi, Behzad: LMBOPT: a limited memory method for bound-constrained optimization (2022)
  16. Liang, Xiubo; Wang, Guoqiang; Yu, Bo: A reduced proximal-point homotopy method for large-scale non-convex BQP (2022)
  17. Ponce-Hinestroza, A.-N.; Drews-Jr, Paulo L. J.; Torres-Méndez, L. Abril: A probabilistic approach to restore images acquired in underwater scenes (2022)
  18. Raynaud, Gaétan; Houde, Sébastien; Gosselin, Frédérick P.: ModalPINN: an extension of physics-informed neural networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors (2022)
  19. Saul, Lawrence K.: A nonlinear matrix decomposition for mining the zeros of sparse data (2022)
  20. Steakelum, Joshua; Aubertine, Jacob; Chen, Kenan; Nagaraju, Vidhyashree; Fiondella, Lance: Multi-phase algorithm design for accurate and efficient model fitting (2022)

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