A comparison of numerical methods for model reduction of dense discrete-time systems

Abstract

Abstract Discrete-time systems are a common tool in the modeling of processes in many application areas such as digital signal processing and population dynamics. Model reduction is an essential remedy to handle high-fidelity systems in practice. To benefit from the performance gained by using reduced-order models, the computation of these models itself must be done with a reasonable use of resources. In this paper, we consider the case of medium-scale dense discrete-time systems and compare the performance of different numerical methods for the implementation of two basic model reduction techniques. Therefore, we give an overview of the considered model reduction methods and of the techniques used in underlying implementations. The outlined methods are then compared with established implementations in several numerical examples in terms of accuracy and performance.