input: from typing import List
  
  def optimize_objective_function(objective: str) -> List[float]:
      """ Optimize the given objective function using the scipy.optimize.minimize method.
      >>> optimize_objective_function('(x[0] - 2)**2 + (x[1] - 3)**2 + (x[2] - 1)**2')
      [2.0, 3.0, 1.0]
      >>> optimize_objective_function('x[0]**2 + x[1]**2 + x[2]**2')
      [0.0, 0.0, 0.0]
      """
      
      # Convert the objective string into a callable function
      def obj_func(x):
          return eval(objective)
  
      # 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
output:     # Convert the objective string into a callable function
      def obj_func(x):
          return eval(objective)
  
      # 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