Least Squares Model Reduction: A Two-Stage System-Theoretic Interpretation
Abstract
Model reduction simplifies complex dynamical systems while preserving essential properties. This paper revisits a recently proposed system-theoretic framework for least squares moment matching. It interprets least squares model reduction in terms of two steps process: constructing a surrogate model to satisfy interpolation constraints, then projecting it onto a reduced-order space. Using tools from output regulation theory and Krylov projections, this approach provides a new view on classical methods. For illustration, we reexamine the least-squares model reduction method by Lucas and Smith, offering new insights into its structure.
Keywords
Cite
@article{arxiv.2505.20604,
title = {Least Squares Model Reduction: A Two-Stage System-Theoretic Interpretation},
author = {Alberto Padoan},
journal= {arXiv preprint arXiv:2505.20604},
year = {2025}
}
Comments
13th IFAC Symposium on Nonlinear Control Systems. arXiv admin note: substantial text overlap with arXiv:2110.06072