scipy.optimize.minimize(fun, x0, args=(), method='trust-krylov', jac=None, hess=None, hessp=None, tol=None, callback=None, options={'inexact': True})

Minimization of a scalar function of one or more variables using a nearly exact trust-region algorithm that only requires matrix vector products with the hessian matrix.

New in version 1.0.0.

See also

For documentation for the rest of the parameters, see scipy.optimize.minimize

inexactbool, optional

Accuracy to solve subproblems. If True requires less nonlinear iterations, but more vector products.