Proximal Gradient-based Low Rank Tensor Decomposition for State Dependent Riccati Equation
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| author | Jiang, Jiahua Navasca, Carmeliza |
| author_facet | Jiang, Jiahua Navasca, Carmeliza |
| contents | We address the optimal control problems arising from partial differential equations with large discrete dimensional control systems. To obtain reduced order models, we find basis elements from the canonical polyadic (CP) decomposition. Tensor datasets are from snapshots of the large models. Our method to reduce the control system is to use dimensionality reduction approaches through sparse optimization and flexible hybrid methods is to obtain low rank CP tensor basis elements. The reduced optimal control problem leads to reduced state-dependent Riccati Equations which can be solved efficiently. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21885 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Proximal Gradient-based Low Rank Tensor Decomposition for State Dependent Riccati Equation Jiang, Jiahua Navasca, Carmeliza Optimization and Control Numerical Analysis We address the optimal control problems arising from partial differential equations with large discrete dimensional control systems. To obtain reduced order models, we find basis elements from the canonical polyadic (CP) decomposition. Tensor datasets are from snapshots of the large models. Our method to reduce the control system is to use dimensionality reduction approaches through sparse optimization and flexible hybrid methods is to obtain low rank CP tensor basis elements. The reduced optimal control problem leads to reduced state-dependent Riccati Equations which can be solved efficiently. |
| title | Proximal Gradient-based Low Rank Tensor Decomposition for State Dependent Riccati Equation |
| topic | Optimization and Control Numerical Analysis |
| url | https://arxiv.org/abs/2605.21885 |