l-bfgs.py

import numpy as	np
from scipy.optimize import fmin_bfgs

def rosen(x):
   return sum(100.0*(x[1:]-x[:-1]**2.0)**2.0 + (1-x[:1])**2.0)

def rosen_der(x):
   xm = x[1:-1]
   xm_m1 = x[:-2]
   xm_p1 = x[2:]
   der = np.zeros_like(x)
   der[1:-1] = 200*(xm-xm_m1**2) - 400*(xm_p1 - xm**2)*xm - 2*(1-xm)
   der[0] = -400*x[0]*(x[1]-x[0]**2) - 2*(1-x[0])
   der[-1] = 200*(x[-1]-x[-2]**2)
   return der

x0 = np.array([1.3, 0.7, 0.8, 1.9, 1.2])
res = fmin_bfgs(rosen, x0, fprime=rosen_der)
print res