Description
L-BFGS optimization helps scientific, machine-learning, and numerical software minimize functions when full second-order methods are too expensive. It is useful for parameter fitting, model training, and mathematical optimization.
This is a numerical library, not a standalone solver interface. Results depend on objective functions, gradients, scaling, and stopping criteria chosen by the calling application.