More practical sketching algorithms for low-rank matrix approximation

This paper describes new algorithms for constructing a low-rank approximation of an input matrix from a sketch, a random low-dimensional linear image of the matrix. These algorithms come with rigorous performance guarantees. Empirically, the proposed methods achieve significantly smaller relative errors than other approaches that have appeared in the literature. For a concrete application, the paper outlines how the algorithms support on-the-fly compression of data from a direct Navier-Stokes (DNS) simulation.