On properties and numerical computation of critical points of eigencurves of bivariate matrix pencils

TL;DR

This paper studies critical points called ZGV points of eigencurves in bivariate matrix pencils, providing a theoretical framework and three numerical methods for their computation, with applications in stability analysis and eigenvalue problems.

Contribution

It introduces a comprehensive theory for ZGV points and develops three numerical algorithms for their efficient computation in various eigenvalue problems.

Findings

ZGV points correspond to multiple eigenvalues of the matrix pencil.

Three numerical methods successfully compute 2D and ZGV points.

Applications include stability analysis and solving parameter-dependent eigenvalue problems.

Abstract

We investigate critical points of eigencurves of bivariate matrix pencils $A+\lambda B +\mu C$. Points $(\lambda,\mu)$ for which $\det(A+\lambda B+\mu C)=0$ form algebraic curves in $\mathbb C^2$ and we focus on points where $\mu'(\lambda)=0$. Such points are referred to as zero-group-velocity (ZGV) points, following terminology from engineering applications. We provide a general theory for the ZGV points and show that they form a subset (with equality in the generic case) of the 2D points $(\lambda_0,\mu_0)$, where $\lambda_0$ is a multiple eigenvalue of the pencil $(A+\mu_0 C)+\lambda B$, or, equivalently, there exist nonzero $x$ and $y$ such that $(A+\lambda_0 B+\mu_0 C)x=0$, $y^H(A+\lambda_0 B+\mu_0 C)=0$, and $y^HBx=0$. We introduce three numerical methods for computing 2D and ZGV points. The first method calculates all 2D (ZGV) points from the eigenvalues of a related singular two-parameter eigenvalue problem. The second method employs a projected regular two-parameter eigenvalue problem to compute either all eigenvalues or only a subset of eigenvalues close to a given target. The third approach is a locally convergent Gauss--Newton-type method that computes a single 2D point from an inital approximation, the later can be provided for all 2D points via the method of fixed relative distance by Jarlebring, Kvaal, and Michiels. In our numerical examples we use these methods to compute 2D-eigenvalues, solve double eigenvalue problems, determine ZGV points of a parameter-dependent quadratic eigenvalue problem, evaluate the distance to instability of a stable matrix, and find critical points of eigencurves of a two-parameter Sturm-Liouville problem.