Module bfgs 

BFGS quasi-Newton: builds an inverse-Hessian approximation from gradient differences, giving superlinear convergence on smooth problems. The default choice for unconstrained smooth calibration.

Functions

bfgs

Minimize f from x0 by BFGS. grad falls back to central finite differences when absent. The inverse-Hessian update is skipped whenever the curvature condition s.y > 0 fails, which keeps the approximation positive definite under the Armijo-only line search.