The Projected Aggregation Methods (PAM) for solving linear systems of equalities and/or inequalities, generate a new iterate xk+1 by projecting the current point xk onto a separating hyperplane generated by a given linear combination of the original hyperplanes or halfspaces. In earlier studies acceleration schemes for solving systems of linear equations and inequalities respectively were introduced, within a PAM like framework. In this paper we apply those schemes in an algorithm based on oblique projections reflecting the sparsity of the matrix of the linear system to be solved. We present the corresponding theoretical convergence results which are a generalization of those given by Echebest et al. We also present the numerical results obtained applying the new scheme to two algorithms introduced by García-Palomares and González-Castaño and also the comparison of its efficiency with that of Censor and Elfving.
