L-BFGS, or Limited-memory Broyden-Fletcher-Goldfarb-Shanno, is an optimization algorithm designed to solve large-scale problems by using a limited amount of memory. It approximates the BFGS method, which is a popular approach in optimization, but does so by storing only a few vectors to represent the Hessian matrix instead of keeping the entire matrix in memory. This makes L-BFGS particularly useful for high-dimensional problems where memory constraints would otherwise be prohibitive.
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