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 759 articles , 1 standard article )

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  1. Awwal, A. M.; Kumam, Poom; Mohammad, Hassan; Watthayu, Wiboonsak; Abubakar, A. B.: A Perry-type derivative-free algorithm for solving nonlinear system of equations and minimizing (\ell_1) regularized problem (2021)
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  5. Ek, David; Forsgren, Anders: Approximate solution of system of equations arising in interior-point methods for bound-constrained optimization (2021)
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  11. Rath, Katharina; Albert, Christopher G.; Bischl, Bernd; von Toussaint, Udo: Symplectic Gaussian process regression of maps in Hamiltonian systems (2021)
  12. Rodomanov, Anton; Nesterov, Yurii: New results on superlinear convergence of classical quasi-Newton methods (2021)
  13. Shen, Chungen; Wang, Yunlong; Xue, Wenjuan; Zhang, Lei-Hong: An accelerated active-set algorithm for a quadratic semidefinite program with general constraints (2021)
  14. Tuck, Jonathan; Barratt, Shane; Boyd, Stephen: A distributed method for fitting Laplacian regularized stratified models (2021)
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  18. Zhang, Xinshuai; Xie, Fangfang; Ji, Tingwei; Zhu, Zaoxu; Zheng, Yao: Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization (2021)
  19. Zhuang, Xiaoying; Guo, Hongwei; Alajlan, Naif; Zhu, Hehua; Rabczuk, Timon: Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning (2021)
  20. Al-Baali, Mehiddin; Caliciotti, Andrea; Fasano, Giovanni; Roma, Massimo: A class of approximate inverse preconditioners based on Krylov-subspace methods for large-scale nonconvex optimization (2020)

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