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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.19310 |
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| _version_ | 1866916592538877952 |
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| author | Jaap, Patrick Sander, Oliver |
| author_facet | Jaap, Patrick Sander, Oliver |
| contents | We consider the closest-point projection with respect to the Frobenius norm of a general real square matrix to the set SL($n$) of matrices with unit determinant. As it turns out, it is sufficient to consider diagonal matrices only. We investigate the structure of the problem both in Euclidean coordinates and in an $n$-dimensional generalization of the classical hyperbolic coordinates of the positive quadrant. Using symmetry arguments we show that the global minimizer is contained in a particular cone. Based on different views of the problem, we propose four different iterative algorithms, and we give convergence results for all of them. Numerical tests show that computing the projection costs essentially as much as a singular value decomposition. Finally, we give an explicit formula for the first derivative of the projection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19310 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How to project onto SL($n$) Jaap, Patrick Sander, Oliver Optimization and Control 65K10, We consider the closest-point projection with respect to the Frobenius norm of a general real square matrix to the set SL($n$) of matrices with unit determinant. As it turns out, it is sufficient to consider diagonal matrices only. We investigate the structure of the problem both in Euclidean coordinates and in an $n$-dimensional generalization of the classical hyperbolic coordinates of the positive quadrant. Using symmetry arguments we show that the global minimizer is contained in a particular cone. Based on different views of the problem, we propose four different iterative algorithms, and we give convergence results for all of them. Numerical tests show that computing the projection costs essentially as much as a singular value decomposition. Finally, we give an explicit formula for the first derivative of the projection. |
| title | How to project onto SL($n$) |
| topic | Optimization and Control 65K10, |
| url | https://arxiv.org/abs/2501.19310 |