matplotlib Code

>>> from scipy.optimize import fmin_ncg
>>> def rosen_hess(x):
        x = asarray(x)
        H = diag(-400*x[:-1],1) - diag(400*x[:-1],-1)
        diagonal = zeros(len(x),x.typecode())
        diagonal[0] = 1200*x[0]-400*x[1]+2
        diagonal[-1] = 200
        diagonal[1:-1] = 202 + 1200*x[1:-1]**2 - 400*x[2:]
        H = H + diag(diagonal)
        return H

>>> x0 = [1.3, 0.7, 0.8, 1.9, 1.2]
>>> xopt = fmin_ncg(rosen, x0, rosen_der, fhess=rosen_hess)
Optimization terminated successfully.
         Current function value: 0.000000
         Iterations: 19
         Function evaluations: 40
         Gradient evaluations: 19
         Hessian evaluations: 19
>>> print xopt
[ 0.9999  0.9999  0.9998  0.9996  0.9991]