Deriving the Gradients of Some Popular Optimal Transport Algorithms
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908689032544256 |
|---|---|
| author | Xie, Fangzhou |
| author_facet | Xie, Fangzhou |
| contents | In this note, I review entropy-regularized Monge-Kantorovich problem in Optimal Transport, and derive the gradients of several popular algorithms popular in Computational Optimal Transport, including the Sinkhorn algorithms, Wasserstein Barycenter algorithms, and the Wasserstein Dictionary Learning algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deriving the Gradients of Some Popular Optimal Transport Algorithms Xie, Fangzhou Optimization and Control Data Structures and Algorithms In this note, I review entropy-regularized Monge-Kantorovich problem in Optimal Transport, and derive the gradients of several popular algorithms popular in Computational Optimal Transport, including the Sinkhorn algorithms, Wasserstein Barycenter algorithms, and the Wasserstein Dictionary Learning algorithms. |
| title | Deriving the Gradients of Some Popular Optimal Transport Algorithms |
| topic | Optimization and Control Data Structures and Algorithms |
| url | https://arxiv.org/abs/2504.08722 |