input: Write a function to optimize the given objective function using the minimize function from the scipy.optimize module. The function should set up the optimization problem with an initial guess x0, use the 'nelder-mead' method, and return the optimized values.
  assert optimize() == [-0.0, 0.0, -0.0]
output: def optimize():
      from scipy.optimize import minimize
      def objective(x):
          return x[0]**2 + x[1]**2 + x[2]**2
      x0 = [1, 1, 1]
      result = minimize(objective, x0, method='nelder-mead')
      return result.x