scipy.optimize.minimize(fun, x0, args=(), method='Newton-CG', jac=None, hess=None, hessp=None, tol=None, callback=None, options={'disp': False, 'xtol': 1e-05, 'eps': 1.4901161193847656e-08, 'return_all': False, 'maxiter': None})

Minimization of scalar function of one or more variables using the Newton-CG algorithm.

Note that the jac parameter (Jacobian) is required.

See also

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


disp : bool

Set to True to print convergence messages.

xtol : float

Average relative error in solution xopt acceptable for convergence.

maxiter : int

Maximum number of iterations to perform.

eps : float or ndarray

If jac is approximated, use this value for the step size.