import numpy as np
from scipy.optimize import minimize
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([1.2, 0.1, 0.8, 1.9, 1.2])
print(x0)
res = minimize(rosen, x0, method='nelder-mead',options={'xtol': 1e-8, 'disp': True})
print(x0)


res2 = minimize(rosen, x0, method='BFGS', jac=rosen_der, options={'disp': True})
print(x0)