input: Write a function that optimizes the given objective function using the scipy.optimize.minimize method with the Nelder-Mead algorithm. The function should set up the optimization problem, initialize the starting point as [0.0, 0.0, 0.0], and return the optimized values of x. If the objective function is not provided, the function should return an error message stating that the objective function needs to be defined.
  
  assert optimize()==[0.0, 0.0, 0.0]
output: def optimize():
      try:
          from scipy.optimize import minimize
          objective_function = globals().get('objective_function')
          if not objective_function:
              return "The objective function needs to be defined."
          result = minimize(objective_function, [0.0, 0.0, 0.0], method='Nelder-Mead')
          return result.x
      except ImportError:
          return "The objective function needs to be defined."