GEORCE: A Fast New Control Algorithm for Computing Geodesics
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917248393805824 |
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| author | Rygaard, Frederik Möbius Hauberg, Søren |
| author_facet | Rygaard, Frederik Möbius Hauberg, Søren |
| contents | Computing geodesics for Riemannian manifolds is a difficult task that often relies on numerical approximations. However, these approximations tend to be either numerically unstable, have slow convergence, or scale poorly with manifold dimension and number of grid points. We introduce a new algorithm called GEORCE that computes geodesics in a local chart via a transformation into a discrete control problem. We show that GEORCE has global convergence and quadratic local convergence. In addition, we show that it extends to Finsler manifolds. For both Finslerian and Riemannian manifolds, we thoroughly benchmark GEORCE against several alternative optimization algorithms and show empirically that it has a much faster and more accurate performance for a variety of manifolds, including key manifolds from information theory and manifolds that are learned using generative models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05961 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GEORCE: A Fast New Control Algorithm for Computing Geodesics Rygaard, Frederik Möbius Hauberg, Søren Differential Geometry Computation Computing geodesics for Riemannian manifolds is a difficult task that often relies on numerical approximations. However, these approximations tend to be either numerically unstable, have slow convergence, or scale poorly with manifold dimension and number of grid points. We introduce a new algorithm called GEORCE that computes geodesics in a local chart via a transformation into a discrete control problem. We show that GEORCE has global convergence and quadratic local convergence. In addition, we show that it extends to Finsler manifolds. For both Finslerian and Riemannian manifolds, we thoroughly benchmark GEORCE against several alternative optimization algorithms and show empirically that it has a much faster and more accurate performance for a variety of manifolds, including key manifolds from information theory and manifolds that are learned using generative models. |
| title | GEORCE: A Fast New Control Algorithm for Computing Geodesics |
| topic | Differential Geometry Computation |
| url | https://arxiv.org/abs/2505.05961 |