Procrustes Problems on Random Matrices
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| author | Jasa, Hajg Bergmann, Ronny Kümmerle, Christian Athreya, Avanti Lubberts, Zachary |
| author_facet | Jasa, Hajg Bergmann, Ronny Kümmerle, Christian Athreya, Avanti Lubberts, Zachary |
| contents | Meaningful comparison between sets of observations often necessitates alignment or registration between them, and the resulting optimization problems range in complexity from those admitting simple closed-form solutions to those requiring advanced and novel techniques. We compare different Procrustes problems in which we align two sets of points after various perturbations by minimizing the norm of the difference between one matrix and an orthogonal transformation of the other. The minimization problem depends significantly on the choice of matrix norm; we highlight recent developments in nonsmooth Riemannian optimization and characterize which choices of norm work best for each perturbation. We show that in several applications, from low-dimensional alignments to hypothesis testing for random networks, when Procrustes alignment with the spectral or robust norm is the appropriate choice, it is often feasible to replace the computationally more expensive spectral and robust minimizers with their closed-form Frobenius-norm counterpart. Our work reinforces the synergy between optimization, geometry, and statistics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05182 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Procrustes Problems on Random Matrices Jasa, Hajg Bergmann, Ronny Kümmerle, Christian Athreya, Avanti Lubberts, Zachary Methodology Optimization and Control Computation 49Q99, 62-08, 62F40, 62-04 Meaningful comparison between sets of observations often necessitates alignment or registration between them, and the resulting optimization problems range in complexity from those admitting simple closed-form solutions to those requiring advanced and novel techniques. We compare different Procrustes problems in which we align two sets of points after various perturbations by minimizing the norm of the difference between one matrix and an orthogonal transformation of the other. The minimization problem depends significantly on the choice of matrix norm; we highlight recent developments in nonsmooth Riemannian optimization and characterize which choices of norm work best for each perturbation. We show that in several applications, from low-dimensional alignments to hypothesis testing for random networks, when Procrustes alignment with the spectral or robust norm is the appropriate choice, it is often feasible to replace the computationally more expensive spectral and robust minimizers with their closed-form Frobenius-norm counterpart. Our work reinforces the synergy between optimization, geometry, and statistics. |
| title | Procrustes Problems on Random Matrices |
| topic | Methodology Optimization and Control Computation 49Q99, 62-08, 62F40, 62-04 |
| url | https://arxiv.org/abs/2510.05182 |