L-BFGS, or Limited-memory Broyden-Fletcher-Goldfarb-Shanno, is an optimization algorithm that belongs to the family of quasi-Newton methods. It is specifically designed for large-scale optimization problems, where the storage of the full Hessian matrix is impractical due to memory constraints. L-BFGS approximates the inverse Hessian matrix using a limited amount of memory, making it efficient for problems with a large number of variables while still maintaining convergence properties similar to its full-memory counterparts.
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