NANAMay 15

Asymptotic condition numbers for linear ordinary differential equations

arXiv:2507.0876236.01 citations
Predicted impact top 78% in NA · last 90 daysOriginality Synthesis-oriented
AI Analysis

It provides a theoretical analysis of error propagation in linear ODEs, which is relevant for numerical analysts and control theorists.

This paper studies the long-time behavior of relative conditioning for the matrix exponential action on a vector, introducing three condition numbers for perturbations of the initial value in linear ODEs.

We are interested in the relative conditioning of the problem $y_0\mapsto \mathrm{e}^{tA}y_0$, i.e., the relative conditioning of the action of the matrix exponential $\mathrm{e}% ^{tA}$ on a vector with respect to perturbations of this vector. The present paper is a qualitative study of the long-time behavior of this conditioning. In other words, we are interested in studying the propagation to the solution $y(t)$ of perturbations of the initial value for a linear ordinary differential equation $y^\prime(t)=Ay(t)$, by measuring these perturbations with relative errors. We introduce three condition numbers: the first considers a specific initial value and a specific direction of perturbation; the second considers a specific initial value and the worst case by varying the direction of perturbation; and the third considers the worst case by varying both the initial value and the direction of perturbation. The long-time behaviors of these three condition numbers are studied.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes