Fast Parallel Randomized QR with Column Pivoting Algorithms for Reliable Low-rank Matrix Approximations

TL;DR

This paper introduces fast, reliable randomized QR algorithms with column pivoting for low-rank matrix approximations, demonstrating improved efficiency and reliability over traditional methods, especially in distributed computing environments.

Contribution

It develops distributed memory RQRCP algorithms, analyzes their reliability, and introduces spectrum-revealing QR factorizations for enhanced low-rank approximations.

Findings

RQRCP algorithms can match QRCP reliability with exponentially decreasing failure probabilities.

Distributed RQRCP outperforms ScaLAPACK QRCP in processing time.

Spectrum-revealing QR provides effective low-rank approximations compared to existing methods.

Abstract

Factorizing large matrices by QR with column pivoting (QRCP) is substantially more expensive than QR without pivoting, owing to communication costs required for pivoting decisions. In contrast, randomized QRCP (RQRCP) algorithms have proven themselves empirically to be highly competitive with high-performance implementations of QR in processing time, on uniprocessor and shared memory machines, and as reliable as QRCP in pivot quality. We show that RQRCP algorithms can be as reliable as QRCP with failure probabilities exponentially decaying in oversampling size. We also analyze efficiency differences among different RQRCP algorithms. More importantly, we develop distributed memory implementations of RQRCP that are significantly better than QRCP implementations in ScaLAPACK. As a further development, we introduce the concept of and develop algorithms for computing spectrum-revealing QR factorizations for low-rank matrix approximations, and demonstrate their effectiveness against leading low-rank approximation methods in both theoretical and numerical reliability and efficiency.