input: 
  def optimize_objective_function():
      """ Optimizes the given objective function using the scipy.optimize.minimize function.
      This function sets up the optimization problem and initializes the starting point 
      for the optimization method 'nelder-mead'. The optimization stops when the tolerance 
      for the norm of the gradient is less than 1e-8.
      >>> optimize_objective_function()
      array([ -8.89005823e-09,  -1.77801165e-08,   8.89005823e-09])
      """
      
      from scipy.optimize import minimize 
  
      def obj_func(x):
          # objective function
          return x[0]**2 + x[1]**2 + x[2]**2
  
      def optimize(): 
          # 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
  
      return optimize()
  
output:     from scipy.optimize import minimize 
  
      def obj_func(x):
          # objective function
          return x[0]**2 + x[1]**2 + x[2]**2
  
      def optimize(): 
          # 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
  
      return optimize()