Preconditioned Krylov subspace methods have proved to be efficient for computing the smallest eigenvalue\nof large symmetric generalized eigenvalue problem. As in the case of linear systems the success of these methods in\nmany cases is due to the existence of good preconditioning techniques. In this paper we consider various \npreconditioners, such as ILU(0), ILU(k), ILUT(1), SSOR(Symmetric Successive OverRelaxation), and AGMG(AGgregate MultiGrid).\nWe tested on the large sparse symmetric matrices arising from discretizations of PDE(Partial Differential Equation)s on structured grids.\nOur results show that ILUT gives the best performance for almost all of the problems tested.