input: Write a function to implement the optimization of a given objective function using the SciPy library. The function should set up the optimization problem with an initial guess and return the optimized parameters.
  
  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
  
  assert list(optimize()) == [-1.0, 1.0, 0.5]
  assert list(optimize()) == [-0.5, 0.5, 0.0]
  assert list(optimize()) == [-0.2, 0.2, 0.1]
  assert list(optimize()) == [0.0, 0.0, 0.0]
  assert list(optimize()) == [1.0, -1.0, -0.5]
  
  # Output of each assert should be the optimized parameters
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