input: 
  from scipy.optimize import minimize
  
  def optimize_objective(obj_func):
      """
      Optimize the given objective function using the Nelder-Mead method.
      The function takes an objective function and returns the optimized values.
      
      >>> def test_func(x):
      ...     return x[0]**2 + x[1]**2 + x[2]**2
      >>> optimize_objective(test_func)
      [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