input: Write a function to minimize a given objective function using scipy.optimize and Nelder-Mead method.
  assert minimize_objective([0,0,0]) == [0.0, 0.0, 0.0]
  assert minimize_objective([1,1,1]) == [0.0, 0.0, 0.0]
  assert minimize_objective([2,2,2]) == [0.0, 0.0, 0.0]
output: from scipy.optimize import minimize
  
  def minimize_objective(x0):
      def objective_function(x):
          # Example objective function: sum of squares
          return sum(xi**2 for xi in x)
      
      result = minimize(objective_function, x0, method='Nelder-Mead')
      return list(result.x)