Least Squares Model Reduction: A Two-Stage System-Theoretic Interpretation
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915306034692096 |
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| author | Padoan, Alberto |
| author_facet | Padoan, Alberto |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20604 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Least Squares Model Reduction: A Two-Stage System-Theoretic Interpretation Padoan, Alberto Optimization and Control Systems and Control 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. |
| title | Least Squares Model Reduction: A Two-Stage System-Theoretic Interpretation |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2505.20604 |