An improved error analysis of CholeskyQR with the randomized model

TL;DR

This paper provides an improved probabilistic error analysis of CholeskyQR algorithms, offering tighter bounds and conditions that enhance understanding of their stability and applicability for tall-skinny matrices.

Contribution

It introduces a refined error analysis for CholeskyQR with the randomized model, including better conditions and bounds, and demonstrates improved stability and applicability.

Findings

Tighter residual bounds for CholeskyQR2

Probabilistic shifted parameter s improves Shifted CholeskyQR3

Numerical experiments confirm theoretical improvements

Abstract

This work is about an improved error analysis of CholeskyQR with the randomized model for the tall-skinny $X \in \mathbb{R}^{m\times n}$. Due to the structure of CholeskyQR, we utilize the randomized model in the first step of CholeskyQR with a weak assumption. We receive a better sufficient condition of $\kappa_{2}(X)$ and a tighter upper bound of residual for CholeskyQR2, together with a probabilistic shifted item $s$ for Shifted CholeskyQR3 based on $\norm{X}_{F}$ after improved error analysis. Numerical experiments demonstrate the effectiveness of our new theoretical results. The probabilistic $s$ for Shifted CholeskyQR3 can enhance the applicability of Shifted CholeskyQR3 while maintaining numerical stability. It is also robust enough after numerous experiments.