An evaluation of software for computing eigenvalues of sparse nonsymmetric matrices Preprint MCS-P54

The past few years have seen a signiicant increase in research into numerical methods for computing selected eigenvalues of large sparse nonsymmetric matrices. This research has begun to lead to the development of high-quality mathematical software. The software includes codes that implement subspace iteration methods, Arnoldi-based algorithms , and nonsymmetric Lanczos methods. The aim of the current study is to evaluate this state-of-the-art software. In this study we consider subspace iteration and Arnoldi codes. We look at the key features of the codes and their ease of use. Then, using a wide range of test problems, we compare the performance of the codes in terms of storage requirements, execution times, accuracy, and reliability. We also consider their suitability for solving large-scale industrial problems. Based on our ndings, we suggest how improved software should be designed.