Computing a partial Schur factorization of nonlinear eigenvalue problems using the infinite Arnoldi method

TL;DR

This paper introduces a novel method based on the infinite Arnoldi algorithm for computing partial Schur factorizations of nonlinear eigenvalue problems, improving efficiency and memory usage through structured restarting and locking.

Contribution

It adapts the infinite Arnoldi method to NEPs, characterizes invariant pairs, and incorporates structured restarting and locking for enhanced computational performance.

Findings

Reduced processing time in benchmark tests

Lower memory consumption with the new method

Effective structured restarting improves convergence

Abstract

The partial Schur factorization can be used to represent several eigenpairs of a matrix in a numerically robust way. Different adaptions of the Arnoldi method are often used to compute partial Schur factorizations. We propose here a technique to compute a partial Schur factorization of a nonlinear eigenvalue problem (NEP). The technique is inspired by the algorithm in [8], now called the infinite Arnoldi method. The infinite Arnoldi method is a method designed for NEPs, and can be interpreted as Arnoldi's method applied to a linear infinite-dimensional operator, whose reciprocal eigenvalues are the solutions to the NEP. As a first result we show that the invariant pairs of the operator are equivalent to invariant pairs of the NEP. We characterize the structure of the invariant pairs of the operator and show how one can carry out a modification of the infinite Arnoldi method by respecting the structure. This also allows us to naturally add the feature known as locking. We nest this algorithm with an outer iteration, where the infinite Arnoldi method for a particular type of structured functions is appropriately restarted. The restarting exploits the structure and is inspired by the well-known implicitly restarted Arnoldi method for standard eigenvalue problems. The final algorithm is applied to examples from a benchmark collection, showing that both processing time and memory consumption can be considerably reduced with the restarting technique.