The lbfgs-b is a variation of the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm that incorporates bounds on the variables being optimized. This method is particularly useful for optimization problems with constraints on the variable values, allowing for efficient handling of large-scale problems while adhering to specified limits. The lbfgs-b combines the advantages of L-BFGS's memory efficiency with the capability to respect bounds, making it a popular choice in various fields, including machine learning and statistics.
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