Two Algorithms for the The Efficient Computation of Truncated Pivoted QR Approximations to a Sparse Matrix
Abstract
In this note we propose two algorithms to compute truncated pivoted QR
approximations to a sparse matrix. One is based on the Gram--Schmidt
algorithm, and the other on Householder triangularization. Both
algorithms leave the original matrix unchanged, and the only
additional storage requirements are arrays to contain the
factorization itself. Thus, the algorithms are particularly suited to
determining low-rank approximations to a sparse matrix.
(Also cross-referenced as UMIACS-TR-98-12)