minimize(method=’Newton-CG’)#
- scipy.optimize.minimize(fun, x0, args=(), method='Newton-CG', jac=None, hess=None, hessp=None, tol=None, callback=None, options={'xtol': 1e-05, 'eps': 1.4901161193847656e-08, 'maxiter': None, 'disp': False, 'return_all': False})
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- Options
- dispbool
Set to True to print convergence messages.
- xtolfloat
Average relative error in solution xopt acceptable for convergence.
- maxiterint
Maximum number of iterations to perform.
- epsfloat or ndarray
If hessp is approximated, use this value for the step size.
- return_allbool, optional
Set to True to return a list of the best solution at each of the iterations.