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 di cult 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 VA Th...

Full waveform inversion (FWI) is a non-linear optimization problem based on full-wavefield modeling to obtain quantitative information of subsurface structure by minimizing the difference between observed seismic data and predicted wavefield. The limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method an effective quasi-Newton in FWI due its high efficiency with low calculation storage ...

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 di cult 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 VA Th...

In this paper, we propose two limited memory BFGS algorithms with a nonmonotone line search technique for unconstrained optimization problems. The global convergence of the given methods will be established under suitable conditions. Numerical results show that the presented algorithms are more competitive than the normal BFGS method.

A new method for solving large nonlinear optimization problems is outlined It attempts to combine the best properties of the discrete truncated Newton method and the limited memory BFGS method to produce an algorithm that is both economical and capable of handling ill conditioned problems The key idea is to use the curvature information generated during the computation of the discrete Newton st...

The limited-memory BFGS (L-BFGS) algorithm is a popular method of solving large-scale unconstrained minimization problems. Since LBFGS conducts a line search with the Wolfe condition, it may require many function evaluations for ill-posed problems. To overcome this difficulty, we propose a method that combines L-BFGS with the regularized Newton method. The computational cost for a single iterat...

A new method for solving large nonlinear optimization problems is outlined. It attempts to combine the best properties of the discrete-truncated Newton method and the limited memory BFGS method, to produce an algorithm that is both economical and capable of handling ill-conditioned problems. The key idea is to use the curvature information generated during the computation of the discrete Newton...

A new method for solving large nonlinear optimization problems is outlined. It attempts to combine the best properties of the discrete-truncated Newton method and the limited memory BFGS method, to produce an algorithm that is both economical and capable of handling ill-conditioned problems. The key idea is to use the curvature information generated during the computation of the discrete Newton...

Computational experience with several limited-memory quasi-Newton and truncated Newton methods for unconstrained nonlinear optimization is described. Comparative tests were conducted on a well-known test library [J. on several synthetic problems allowing control of the clustering of eigenvalues in the Hessian spectrum, and on some large-scale problems in oceanography and meteorology. The result...