On the Effect of Quadratic Regularization in Direct Data-Driven LQR
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908980625801216 |
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| author | Klädtke, Manuel Zhao, Feiran Dörfler, Florian Darup, Moritz Schulze |
| author_facet | Klädtke, Manuel Zhao, Feiran Dörfler, Florian Darup, Moritz Schulze |
| contents | This paper proposes an explainability concept for direct data-driven linear quadratic regulation (LQR) with quadratic regularization. Our perspective follows the parametric effect of regularization, an analysis approach that translates regularization costs from auxiliary variables to system quantities, enabling intuitive interpretations. The framework further enables the elimination of auxiliary variables, thereby reducing computational complexity. We demonstrate the effectiveness of our approach and the identified effect of regularization via simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18453 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On the Effect of Quadratic Regularization in Direct Data-Driven LQR Klädtke, Manuel Zhao, Feiran Dörfler, Florian Darup, Moritz Schulze Systems and Control Optimization and Control This paper proposes an explainability concept for direct data-driven linear quadratic regulation (LQR) with quadratic regularization. Our perspective follows the parametric effect of regularization, an analysis approach that translates regularization costs from auxiliary variables to system quantities, enabling intuitive interpretations. The framework further enables the elimination of auxiliary variables, thereby reducing computational complexity. We demonstrate the effectiveness of our approach and the identified effect of regularization via simulations. |
| title | On the Effect of Quadratic Regularization in Direct Data-Driven LQR |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2604.18453 |