matplotlib Code

>>> from scipy.optimize import fmin_ncg
>>> def rosen_hess_p(x,p):
        x = asarray(x)
        Hp = zeros(len(x),x.typecode())
        Hp[0] = (1200*x[0]**2 - 400*x[1] + 2)*p[0] - 400*x[0]*p[1]
        Hp[1:-1] = -400*x[:-2]*p[:-2]+(202+1200*x[1:-1]**2-400*x[2:])*p[1:-1] \
                   -400*x[1:-1]*p[2:]
        Hp[-1] = -400*x[-2]*p[-2] + 200*p[-1]
        return Hp

>>> x0 = [1.3, 0.7, 0.8, 1.9, 1.2]
>>> xopt = fmin_ncg(rosen, x0, rosen_der, fhess_p=rosen_hess_p)
Optimization terminated successfully.
         Current function value: 0.000000
         Iterations: 20
         Function evaluations: 42
         Gradient evaluations: 20
         Hessian evaluations: 44
>>> print xopt
[ 1.      1.      1.      0.9999  0.9999]