Weak and strong convergence of generalized proximal point algorithms with relaxed parameters

In this work, we propose and study a framework of generalized proximal point algorithms associated with maximally monotone operator. We indicate sufficient conditions on the regularization relaxation parameters for equivalence boundedness sequence iterations generated by algorithm non-emptiness zero set operator, weak strong convergence algorithm. Our results cover or improve many in our references. Improvements are illustrated comparing related known ones.