We consider smooth convex programming problems where the decision variables vector is split into several blocks of variables. Sublinear rate of convergence results for the cyclic block coordinate gradient projection method as well as the alternating minimization method are derived. When the objective function is also assumed to be strongly convex, linear rate of convergence is established.