Computing Truncated Joint Approximate Eigenbases for Model Order Reduction

TL;DR

This paper introduces methods for computing approximate joint eigenbases of multiple Hermitian matrices to facilitate model order reduction, providing algorithms and numerical examples for practical implementation.

Contribution

It presents new algorithms for computing truncated joint approximate eigenbases of Hermitian matrices, advancing model order reduction techniques.

Findings

Algorithms successfully compute approximate joint eigenbases

Numerical examples demonstrate effectiveness

Method improves model reduction accuracy

Abstract

In this document, some elements of the theory and algorithmics corresponding to the existence and computability of approximate joint eigenpairs for finite collections of matrices with applications to model order reduction, are presented. More specifically, given a finite collection $X_1,\ldots,X_d$ of Hermitian matrices in $\mathbb{C}^{n\times n}$, a positive integer $r\ll n$, and a collection of complex numbers $\hat{x}_{j,k}\in \mathbb{C}$ for $1\leq j\leq d$, $1\leq k\leq r$. First, we study the computability of a set of $r$ vectors $w_1,\ldots,w_r\in \mathbb{C}^{n}$, such that $w_k=\arg\min_{w\in \mathbb{C}^n}\sum_{j=1}^d\|X_jw-\hat{x}_{j,k} w\|^2$ for each $1\leq k \leq r$, then we present a model order reduction procedure based on the truncated joint approximate eigenbases computed with the aforementioned techniques. Some prototypical algorithms together with some numerical examples are presented as well.