input: Write a function to optimize the given objective function using the Scipy library. The function should return the optimized values of the variables. If no initial guess is provided, use [0.0, 0.0, 0.0] as the default initial guess. The optimization method should be 'nelder-mead' with an xtol of 1e-8 and display the optimization process.
  
  def obj_func(x):
      # objective function
      # ...
  
  def optimize(initial_guess=[0.0, 0.0, 0.0]):
      res = minimize(obj_func, initial_guess, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x
  
  assert all(optimize() == [0.0, 0.0, 0.0])
  assert all(optimize([1.0, 1.0, 1.0]) == [1.0, 1.0, 1.0])
  assert all(optimize([-1.0, -1.0, -1.0]) == [-1.0, -1.0, -1.0])
output: def optimize(initial_guess=[0.0, 0.0, 0.0]):
      res = minimize(obj_func, initial_guess, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x