Four algorithms for the the efficient computation of truncated pivoted QR approximations to a sparse matrix

In this paper we propose four algorithms to compute truncated pivoted QR ap proximations to a sparse matrix. Three are based on the Gram-Schmidt algori thm and the other on Householder triangularization. All four algorithms lea ve the original matrix unchanged, and the only additional storage requireme nts are arrays to contain the factorization itself, Thus, the: algorithms a re particularly suited to determining tow-rank approximations to a sparse m atrix.