Balanced truncation model order reduction in limited time intervals for large systems

TL;DR

This paper explores efficient numerical methods for time-limited balanced truncation in large-scale systems, demonstrating that it can produce more accurate reduced models within specific time intervals by leveraging low-rank solutions.

Contribution

It introduces rational Krylov subspace methods for efficient computation in time-limited balanced truncation and analyzes the rank properties of Lyapunov solutions for large systems.

Findings

Time-limited balanced truncation can yield lower-rank Lyapunov solutions.

Reduced models are more accurate within the specified time interval.

Numerical experiments confirm the efficiency and effectiveness of the proposed methods.

Abstract

In this article we investigate model order reduction of large-scale systems using time-limited balanced truncation, which restricts the well known balanced truncation framework to prescribed finite time intervals. The main emphasis is on the efficient numerical realization of this model reduction approach in case of large system dimensions. We discuss numerical methods to deal with the resulting matrix exponential functions and Lyapunov equations which are solved for low-rank approximations. Our main tool for this purpose are rational Krylov subspace methods. We also discuss the eigenvalue decay and numerical rank of the solutions of the Lyapunov equations. These results, and also numerical experiments, will show that depending on the final time horizon, the numerical rank of the Lyapunov solutions in time-limited balanced truncation can be smaller compared to standard balanced truncation. In numerical experiments we test the approaches for computing low-rank factors of the involved Lyapunov solutions and illustrate that time-limited balanced truncation can generate reduced order models having a higher accuracy in the considered time region.