Gradient enhanced ADMM Algorithm for dynamic optimal transport on surfaces
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866929397342142464 |
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| author | Dong, Guozhi Guo, Hailong Jiang, Chengrun Shi, Zuoqiang |
| author_facet | Dong, Guozhi Guo, Hailong Jiang, Chengrun Shi, Zuoqiang |
| contents | A gradient enhanced ADMM algorithm for optimal transport on general surfaces is proposed in this paper. Based on Benamou and Brenier's dynamical formulation, we combine gradient recovery techniques on surfaces with the ADMM algorithm, not only improving the computational accuracy, but also providing a novel method to deal with dual variables in the algorithm. This method avoids the use of stagger grids, has better accuracy and is more robust comparing to other averaging techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16285 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gradient enhanced ADMM Algorithm for dynamic optimal transport on surfaces Dong, Guozhi Guo, Hailong Jiang, Chengrun Shi, Zuoqiang Numerical Analysis Optimization and Control 65M22, 49M41 A gradient enhanced ADMM algorithm for optimal transport on general surfaces is proposed in this paper. Based on Benamou and Brenier's dynamical formulation, we combine gradient recovery techniques on surfaces with the ADMM algorithm, not only improving the computational accuracy, but also providing a novel method to deal with dual variables in the algorithm. This method avoids the use of stagger grids, has better accuracy and is more robust comparing to other averaging techniques. |
| title | Gradient enhanced ADMM Algorithm for dynamic optimal transport on surfaces |
| topic | Numerical Analysis Optimization and Control 65M22, 49M41 |
| url | https://arxiv.org/abs/2406.16285 |