input: Write a function to optimize the given objective function using the scipy.optimize.minimize function. The function should return the optimized values of the variables after optimization.
  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 list(optimize()) == [-0.5, 1.0, 0.0, 0.5]
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