  In this paper, a method via sparse-sparse iteration for computing a sparse
incomplete factorization of the inverse of a symmetric positive definite matrix
is proposed. The resulting factorized sparse approximate inverse is used as a
preconditioner for solving symmetric positive definite linear systems of
equations by using the preconditioned conjugate gradient algorithm. Some
numerical experiments on test matrices from the Harwell-Boeing collection for
comparing the numerical performance of the presented method with one available
well-known algorithm are also given.
