Randomized LU Decomposition

Abstract

We present a fast randomized algorithm that computes a low rank LU decomposition. Our algorithm uses random projections type techniques to efficiently compute a low rank approximation of large matrices. The randomized LU algorithm can be parallelized and further accelerated by using sparse random matrices in its projection step. Several different error bounds are proven for the algorithm approximations. To prove these bounds, recent results from random matrix theory related to subgaussian matrices are used. As an application, we also show how the algorithm can be utilized to solve problems such as the rank-deficient least squares problem. Numerical examples, which illustrate the performance of the algorithm and compare it to other decomposition methods, are presented.