input: from typing import List
  
  def optimize_objective_function(func: object) -> List[float]:
      """ Optimize the given objective function using the Scipy's minimize function with the Nelder-Mead method.
      The function should take a list of parameters and return a float. The initial guess for the optimization 
      is set to [0.0, 0.0, 0.0].
      >>> def example_func(x):
      ...     return (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 0.5)**2
      >>> optimize_objective_function(example_func)
      [1.0, 2.5, 0.5]
      """
      x0 = [0.0, 0.0, 0.0]
      res = minimize(func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x
output:     x0 = [0.0, 0.0, 0.0]
      res = minimize(func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return res.x