input: Write a function to optimize the given objective function using the scipy.optimize.minimize method with the Nelder-Mead algorithm. The function should return the optimized parameters. 
  
  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
  
  assert list(optimize())==[0.0, 0.0, 0.0]
  assert list(optimize())==[0.0, 0.0, 0.0]
  assert list(optimize())==[0.0, 0.0, 0.0]
  assert list(optimize())==[0.0, 0.0, 0.0]
  assert list(optimize())==[0.0, 0.0, 0.0]
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