L-BFGS stands for Limited-memory Broyden-Fletcher-Goldfarb-Shanno, which is an optimization algorithm used for solving unconstrained optimization problems. It is a variation of the BFGS method that uses limited memory, making it especially useful for large-scale problems often encountered in training neural networks. This method efficiently approximates the Hessian matrix to help find the minimum of a function, facilitating faster convergence during the training process.
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