# Gramians, Energy Functionals and Balanced Truncation for Linear Dynamical Systems with Quadratic Outputs

arXiv: https://arxiv.org/abs/1909.04597 · HTML: https://mlsanity.com/paper/1909.04597/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [A Deep State-Space Model Compression Method using Upper Bound on Output Error](https://mlsanity.com/paper/2510.14542/) (2025)

## Abstract (arXiv)

Model order reduction is a technique that is used to construct low-order approximations of large-scale dynamical systems. In this paper, we investigate a balancing based model order reduction method for dynamical systems with a linear dynamical equation and a quadratic output function. To this aim, we propose a new algebraic observability Gramian for the system based on Hilbert space adjoint theory. We then show the proposed Gramians satisfy a particular type of generalized Lyapunov equations and we investigate their connections to energy functionals, namely, the controllability and observability. This allows us to find the states that are hard to control and hard to observe via an appropriate balancing transformation. Truncation of such states yields reduced-order systems. Finally, based on mathcal H2 energy considerations, we, furthermore, derive error bounds, depending on the neglected singular values. The efficiency of the proposed method is demonstrated by means of two semi-discretized partial differential equations and is compared with the existing model reduction techniques in the literature.