input: Write a python function to optimize an objective function using the Nelder-Mead method.
  assert optimize()[0] == ...
  assert optimize()[1] == ...
  assert optimize()[2] == ...
output: def optimize():
      from scipy.optimize import minimize
  
      def objective_function(x):
          return x[0]**2 + x[1]**2
  
      initial_guess = [1.0, 1.0]
      result = minimize(objective_function, initial_guess, method='Nelder-Mead')
  
      return result.x, result.fun, result.success