input: Write a function to optimize the given objective function using the scipy.optimize.minimize method. The function should take an initial guess for the variables and return the optimized values. If the objective function is empty, return an empty list.
  
  from scipy.optimize import minimize
  
  def obj_func(x):
      # objective function
      # ...
  
  def optimize(x0):
      if not x0:
          return []
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x
  
  assert optimize([0.0, 0.0, 0.0])==[0.0, 0.0, 0.0]
  assert optimize([])==[]
output: def optimize(x0):
      if not x0:
          return []
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x