def rosen_der(x):
xm = x[1:-1]
xm_m1 = x[:-2]
xm_p1 = x[2:]
der = MLab.zeros(x.shape,x.typecode())
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
def rosen_der(x):
xm = x[1:-1]
xm_m1 = x[:-2]
xm_p1 = x[2:]
der = MLab.zeros(x.shape, x.typecode())
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
def rosen(x): # The Rosenbrock function
return MLab.sum(100.0 * (x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)
def rosen(x): # The Rosenbrock function
return MLab.sum(100.0*(x[1:]-x[:-1]**2.0)**2.0 + (1-x[:-1])**2.0)