input: Write a function to optimize the given objective function using the Scipy library. The function should initialize the optimization with a starting point of [0.0, 0.0, 0.0], use the Nelder-Mead method, and set the tolerance to 1e-8. If the optimization is successful, return the optimized values of the variables.
  
  from scipy.optimize import minimize
  
  def obj_func(x):
      # objective function
      # ...
  
  def optimize(): 
      x0 = [0.0, 0.0, 0.0]
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x
  
  print(optimize())
output: def optimize(): 
      x0 = [0.0, 0.0, 0.0]
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x