import numpy as np
import scipy.optimize as optimize
import sympy as sym

b = 7.5
r = 2.1
l = np.arange(0,10.1,0.1)


def f(params): 
    x, z = params #x=lambdax, z=lambdazz
    F = x**2 + 1/(x**2*z**2) + (i)**2 + r*z**2 + b*(((x*z)/((x**2 + i**2)**0.5) -1)**2) + ((z**2)*(x**2+(r*(i**2))))/(x**2+i**2)
    return F

initial = [1,1]


for i in range(len(l)):
    i=i/100
    result = optimize.minimize(f, initial, method='Nelder-Mead')
    print(result.x[0])
