A treatment of breakdowns and near breakdowns in a reduction of a matrix to upper $J$-Hessenberg form and related topics

TL;DR

This paper addresses the challenges of breakdowns and near-breakdowns in algorithms for reducing matrices to upper J-Hessenberg form, proposing strategies to overcome these issues and improve numerical stability in eigenvalue computations.

Contribution

It introduces a new approach for handling breakdowns in J-Hessenberg reduction algorithms, enhancing their robustness and reliability.

Findings

Strategies effectively cure and treat breakdowns.

Numerical experiments demonstrate improved stability.

Enhanced algorithms maintain performance despite breakdowns.

Abstract

The reduction of a matrix to an upper $J$-Hessenberg form is a crucial step in the $SR$-algorithm (which is a $QR$-like algorithm), structure-preserving, for computing eigenvalues and vectors, for a class of structured matrices. This reduction may be handled via the algorithm JHESS or via the recent algorithm JHMSH and its variants. The main drawback of JHESS (or JHMSH) is that it may suffer from a fatal breakdown, causing a brutal stop of the computations and hence, the $SR$-algorithm does not run. JHESS may also encounter near-breakdowns, source of serious numerical instability. In this paper, we focus on these aspects. We first bring light on the necessary and sufficient condition for the existence of the $SR$-decomposition, which is intimately linked to $J$-Hessenberg reduction. Then we will derive a strategy for curing fatal breakdowns and also for treating near breakdowns. Hence, the $J$-Hessenberg form may be obtained. Numerical experiments are given, demonstrating the efficiency of our strategies to cure and treat breakdowns or near breakdowns.