input: Write a function to optimize an objective function using the SciPy library. The function should take no input and optimize the objective function using the Nelder-Mead method with initial guess set to [0.0, 0.0, 0.0]. If the optimization is successful, return the optimized values of the variables, otherwise return any result provided by the optimization process.
  assert optimize()==[0.0, 0.0, 0.0]
  assert isinstance(optimize(), list)
  assert len(optimize())==3
output: def optimize():
      from scipy.optimize import minimize
      def objective(x):
          return x[0]**2 + x[1]**2 + x[2]**2
      result = minimize(objective, [0.0, 0.0, 0.0], method='Nelder-Mead')
      return result.x.tolist()