input: 
  from scipy.optimize import minimize 
  
  def obj_func(x):
      """Objective function to be optimized.
      >>> obj_func([0.0, 0.0, 0.0])
      0.0
      >>> obj_func([1.0, 1.0, 1.0])
      3.0
      >>> obj_func([2.0, 2.0, 2.0])
      12.0
      """
      return sum(x_i**2 for x_i in x)
  
  def optimize(): 
      """Optimize the objective function using the Nelder-Mead method.
      >>> optimize()
      [0.0, 0.0, 0.0]
      """
      x0 = [0.0, 0.0, 0.0] 
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
      return res.x
  
output:     x0 = [0.0, 0.0, 0.0] 
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
      return res.x