Limited-memory BFGS (L-BFGS) is an optimization algorithm designed to solve large-scale optimization problems by approximating the Hessian matrix using a limited amount of memory. It achieves this by storing only a few vectors that represent the approximate curvature information, making it highly efficient for problems with a high number of variables.