Proximal Point Methods (PPM) can be traced to the pioneer works of Moreau [16], Martinet [14,15] and Rockafellar [19, 20] who used as regularization function the square of the Euclideannorm. In this work, we study PPM in the context of optimization and we derive a class of suchmethods which contains Rockafellar's result. We also present a less stringent criterion to theacceptance of an approximate solution to the subproblems that arise in the inner loops of PPM.Moreover, we introduce a new family of augmented Lagrangian methods for convex constrainedoptimization, that generalizes the PE+ class presented in [2].