A quasi-Grassmannian gradient flow model for eigenvalue problems
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909660559179776 |
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| author | Wang, Shengyue Zhou, Aihui |
| author_facet | Wang, Shengyue Zhou, Aihui |
| contents | We propose a quasi-Grassmannian gradient flow model for eigenvalue problems of linear operators, aiming to efficiently address many eigenpairs. Our model inherently ensures asymptotic orthogonality: without the need for initial orthogonality, the solution naturally evolves toward being orthogonal over time. We establish the well-posedness of the model, and provide the analytical representation of solutions. Through asymptotic analysis, we show that the gradient converges exponentially to zero and that the energy decreases exponentially to its minimum. This implies that the solution of the quasi-Grassmannian gradient flow model converges to the solution of the eigenvalue problems as time progresses. These properties not only eliminate the need for explicit orthogonalization in numerical computation but also significantly enhance robustness of the model, rendering it far more resilient to numerical perturbations than conventional methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20195 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A quasi-Grassmannian gradient flow model for eigenvalue problems Wang, Shengyue Zhou, Aihui Numerical Analysis We propose a quasi-Grassmannian gradient flow model for eigenvalue problems of linear operators, aiming to efficiently address many eigenpairs. Our model inherently ensures asymptotic orthogonality: without the need for initial orthogonality, the solution naturally evolves toward being orthogonal over time. We establish the well-posedness of the model, and provide the analytical representation of solutions. Through asymptotic analysis, we show that the gradient converges exponentially to zero and that the energy decreases exponentially to its minimum. This implies that the solution of the quasi-Grassmannian gradient flow model converges to the solution of the eigenvalue problems as time progresses. These properties not only eliminate the need for explicit orthogonalization in numerical computation but also significantly enhance robustness of the model, rendering it far more resilient to numerical perturbations than conventional methods. |
| title | A quasi-Grassmannian gradient flow model for eigenvalue problems |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2506.20195 |