input: 
  from scipy.optimize import minimize 
  
  def obj_func(x):
      # objective function
      # ...
  
  def optimize(): 
      """Optimizes the objective function using the Nelder-Mead method.
      The function initializes x with [0.0, 0.0, 0.0] and uses the minimize function
      from scipy.optimize with the Nelder-Mead method. Prints the optimal x values 
      after optimization.
      >>> optimize()
      [0.00987654, 0.00345678, 0.00123456]
      """
      # set up optimization problem
      x0 = [0.0, 0.0, 0.0] 
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
      return res.x
  
  print(optimize())
  
output:     return (x[0] - 0.00987654)**2 + (x[1] - 0.00345678)**2 + (x[2] - 0.00123456)**2