input: 
  from typing import List
  
  def optimize_objective_function(x: List[float]) -> float:
      """ Optimize the given objective function using the minimize function from scipy.optimize.
      >>> optimize_objective_function([1.0, 2.0, 3.0])
      [1.51, 0.62, 1.43]
      >>> optimize_objective_function([0.5, 0.5, 0.5])
      [0.78, 0.21, 0.09]
      """
      from scipy.optimize import minimize 
  
      def obj_func(x):
          # objective function
          # ...
  
      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:     def obj_func(x):
          return (x[0] - 1.5)**2 + (x[1] - 0.5)**2 + (x[2] - 1.0)**2
  
      def optimize(): 
          x0 = x
          res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
          return res.x
  
      return optimize()