Balanced Truncation via Tangential Interpolation

TL;DR

This paper introduces an iterative, automatic tangential interpolation algorithm for balanced truncation that effectively reduces system order while preserving key system properties, demonstrated on benchmark models.

Contribution

It proposes a fully automatic, iterative tangential interpolation method for balanced truncation, including an adaptive scheme for selecting model order and an efficient solver for Lyapunov equations.

Findings

Algorithm accurately preserves the largest Hankel singular values.

Method automatically selects interpolation data and model order.

Validated on benchmark models, showing effective model reduction.

Abstract

This paper examines the construction of rth-order truncated balanced realizations via tangential interpolation at r specified interpolation points. It is demonstrated that when the truncated Hankel singular values are negligible-that is, when the discarded states are nearly uncontrollable and unobservable-balanced truncation simplifies to a bi-tangential Hermite interpolation problem at r interpolation points. In such cases, the resulting truncated balanced realization is nearly H2-optimal and thus interpolates the original model at the mirror images of its poles along its residual directions. Like standard H2-optimal model reduction, where the interpolation points and tangential directions that yield a local optimum are not known, in balanced truncation as well, the interpolation points and tangential directions required to produce a truncated balanced realization remain unknown. To address this, we propose an iterative tangential interpolation-based algorithm for balanced truncation. Upon convergence, the algorithm yields a low-rank truncated balanced realization that accurately preserves the r largest Hankel singular values of the original system. An adaptive scheme to automatically select the order r of the reduced model is also proposed. The algorithm is fully automatic, choosing both the interpolation data and the model order without user intervention. Additionally, an adaptive low-rank solver for Lyapunov equations based on tangential interpolation is proposed, automatically selecting both the interpolation data and the rank without user intervention. The performance of the proposed algorithms is evaluated on benchmark models, confirming their efficacy.