A Fast Randomized Algorithm for Computing an Approximate Null Space

Abstract

Randomized algorithms in numerical linear algebra can be fast, scalable and robust. This paper examines the effect of sketching on the right singular vectors corresponding to the smallest singular values of a tall-skinny matrix. We analyze a fast algorithm by Gilbert, Park and Wakin for finding the trailing right singular vectors using randomization by examining the quality of the solution using multiplicative perturbation theory. For an $m\times n$ ($m\ge n$) matrix, the algorithm runs with complexity $O(mn\log n+n^3)$ which is faster than the standard $O(m{n}^2)$ methods. In applications, numerical experiments show great speedups including a $30\times$ speedup for the AAA algorithm and $10\times$ speedup for the total least squares problem.

28 pages, 4 figures, 2 tables

References in corpus (5)

• Sketching as a Tool for Numerical Linear Algebra
• Sketched SVD: Recovering Spectral Features from Compressive Measurements
• Fast and stable randomized low-rank matrix approximation
• Fast & Accurate Randomized Algorithms for Linear Systems and Eigenvalue Problems
• Hashing embeddings of optimal dimension, with applications to linear least squares

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