Model Reduction Based on Spectral Projection Methods

Abstract

We discuss the efficient implementation of model reduction methods such as modal truncation, balanced truncation, and other balancing-related truncation techniques, employing the idea of spectral projection. Mostly, we will be concerned with the sign function method which serves as the major computational tool of most of the discussed algorithms for computing reduced-order models. Implementations for large-scale problems based on parallelization or formatted arithmetic will also be discussed. This chapter can also serve as a tutorial on Gramian-based model reduction using spectral projection methods.