Reliable Krylov-based algorithms for matrix null space and rank

Krylov-based algorithms have recently been used, in combination with other methods, to solve systems of linear equations and to perform related matrix computations over finite fields. For example, large and sparse systems of linear equations ;_2; are formed during the use of the number field sieve for integer factorization, and elements of the null space of these systems are sampled.Two rather different kinds of block algorithms have recently been considered. Block Wiedemann algorithms have now been presented and fully analyzed. Block Lanczos algorithms were proposed earlier but are not yet as well understood. In particular, proofs of reliability of block Lanczos algorithms are not yet available. Nevertheless, an examination of the computational number theory literature suggests that block Lanczos algorithms continue to be preferred.This report presents a block Lanczos algorithm that is somewhat simpler than block algorithms that are presently in use and provably reliable for computations over large fields. To my knowledge, this is the first block Lanczos algorithm for which a proof of reliability is available.A different Krylov-based approach is considered for computations over small fields: It is shown that if Wiedemann's sparse matrix preconditioner is applied to an arbitrary matrix then the number of nontrivial invariant factors of the result is, with high probability, quite small. A Krylov-based algorithm to compute a partial Frobenius decomposition can then be used to sample from the null space of the original matrix or to compute its rank. This yields a randomized (Monte Carlo) black box algorithm for matrix rank that is asymptotically faster, in the small field case, than any other that is presently known.