This report concerns with the solution of linearly constrained strictly convex quadratic programming problem by a splitting iterative method, called diagonalization algorithm (DA). This algorithm transforms the original problem into a sequence of subproblems easier to solve, for which there exists a large number of efficient methods in literature. In fact each subproblem may be formulated as a linear complementarity problem or as a constrained least distance problem. We give conditions for DA convergence and we present an application on a large scale sparse problem arising in constrained bivariate interpolation. An extensive experimentation on CRAY C94 permits to evaluate the DA performance.The Fortran 77 codes carried out on multivector computer Cray C94 implementing the algorithms described above, are reported in appendix.