We lifted 3 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| theodumont/monge-gromov-wasserstein | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| cost_x2y2 | Ran | theodumont/monge-gromov-wasserstein/utils.py code served (permissive licence) · get_code("4c8df766544a071a") |
| cost_x2y2_p_4Mxy | Not yet run | theodumont/monge-gromov-wasserstein/utils.py code served (permissive licence) · get_code("b839397e5b790052") |
| cost_xy | Not yet run | theodumont/monge-gromov-wasserstein/utils.py code served (permissive licence) · get_code("eb0af3edafbd6dc1") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The Gromov--Wasserstein problem is a non-convex optimization problem over the polytope of transportation plans between two probability measures supported on two spaces, each equipped with a cost function evaluating similarities between points. Akin to the standard optimal transportation problem, it is natural to ask for conditions guaranteeing some structure on the optimizers, for instance if these are induced by a (Monge) map. We study this question in Euclidean spaces when the cost functions are either given by (i) inner products or (ii) squared distances, two standard choices in the literature. We establish the existence of an optimal map in case (i) and of an optimal 2-map (the union of the graphs of two maps) in case (ii), both under an absolute continuity condition on the source measure. Additionally, in case (ii) and in dimension one, we numerically design situations where optimizers of the Gromov--Wasserstein problem are 2-maps but are not maps. This suggests that our result cannot be improved in general for this cost. Still in dimension one, we additionally establish the optimality of monotone maps under some conditions on the measures, thereby giving insight on why such maps often appear to be optimal in numerical experiments.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.11945")
get_code_for_paper("2210.11945")
have("2210.11945")
Connect an agent — have() is free.