The paper deals with infinite horizon optimization problems. The existence of optimal solutions is obtained as a consequence of an asymptotic growth condition. We also construct finite horizon approximates that yield upper and lower bounds for the optimal values and whose optimal solutions converge to the long-term optimal trajectories. One type of approximate results from a process which involves smoothening the tail of any trajectory. The other type emerges when we restrain ourselves to stay put after a finite lapse of time.