input: Write a function to optimize the given objective function using the `scipy.optimize.minimize` function. The objective function is to be defined in the function `obj_func`. The optimization should start from the initial guess `x0` and use the Nelder-Mead method with specific options.
  from scipy.optimize import minimize 
  
  def obj_func(x):
      # objective function
      # ...
  
  def optimize(): 
      # set up optimization problem
      x0 = [0.0, 0.0, 0.0] 
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
      return res.x
  
  print(optimize())
  
  assert optimize() == [expected_optimized_values]
output: def optimize(): 
      x0 = [0.0, 0.0, 0.0] 
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
      return res.x