GR decompositions and their relations to Cholesky-like factorizations

TL;DR

This paper explores generalized QR decompositions related to different scalar products, analyzing their connections to Cholesky-like factorizations and proposing methods with improved numerical stability.

Contribution

It establishes links between GR decompositions and Cholesky-like factorizations, offering more stable computational methods for hyperbolic and symplectic cases.

Findings

Cholesky-based methods improve numerical stability

Connections between GR and LDL^T, skew-symmetric factorizations

Repeated procedures enhance accuracy

Abstract

For a given matrix, we are interested in computing GR decompositions $A=GR$, where $G$ is an isometry with respect to given scalar products. The orthogonal QR decomposition is the representative for the Euclidian scalar product. For a signature matrix, a respective factorization is given as the hyperbolic QR decomposition. Considering a skew-symmetric matrix leads to the symplectic QR decomposition. The standard approach for computing GR decompositions is based on the successive elimination of subdiagonal matrix entries. For the hyperbolic and symplectic case, this approach does in general not lead to a satisfying numerical accuracy. An alternative approach computes the QR decomposition via a Cholesky factorization, but also has bad stability. It is improved by repeating the procedure a second time. In the same way, the hyperbolic and the symplectic QR decomposition are related to the $LDL^T$ and a skew-symmetric Cholesky-like factorization. We show that methods exploiting this connection can provide better numerical stability than elimination-based approaches.