input: 
  from scipy.optimize import minimize
  
  def obj_func(x):
      # objective function
      # ...
  
  def optimize(): 
      """This function sets up and performs an optimization of the given objective function. It utilizes the Scipy library's minimize function with the Nelder-Mead method. The initial guess for the optimization is set to [0.0, 0.0, 0.0]. The tolerance for the optimization is set to 1e-8, and the result is printed. 
      >>> optimize()
      [0.25, 0.0, 0.5]
      """
      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] - 0.25)**2 + x[1]**2 + (x[2] - 0.5)**2