Fred Hamprecht, Peter Lippmann, Enrique Sanmartín
We lifted 24 functions out of this paper's own repositories and ran 20 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 |
|---|---|---|
| hci-unihd/BranchedOT | canonical | 20 of 24 |
| Function | Status | Where it lives |
|---|---|---|
| MST_prior_topology | Ran | hci-unihd/BranchedOT/src/MST_prior.py code served (permissive licence) · get_code("a04c5db725bb57a8") |
| OT_prior_topology | Ran | hci-unihd/BranchedOT/src/OT_prior.py code served (permissive licence) · get_code("c58f1014c3a2dc58") |
| acceptance_probability | Ran | hci-unihd/BranchedOT/src/greedy_topology_optimization.py code served (permissive licence) · get_code("9d792a64ee44a8be") |
| calc_pivot_point | Ran | hci-unihd/BranchedOT/src/geometric_construction_solver.py code served (permissive licence) · get_code("43a6ab516b27adaa") |
| constr_mat | Ran | hci-unihd/BranchedOT/src/OT_prior.py code served (permissive licence) · get_code("579b831de4b85636") |
| cost_mat | Ran | hci-unihd/BranchedOT/src/OT_prior.py code served (permissive licence) · get_code("0fd87c4a1a8c80f8") |
| dist_point_segments | Ran | hci-unihd/BranchedOT/src/utils.py code served (permissive licence) · get_code("2986c41f24d30fa6") |
| dist_segment_points | Ran | hci-unihd/BranchedOT/src/utils.py code served (permissive licence) · get_code("56f15ba093c99ba4") |
| eucl_dist | Ran | hci-unihd/BranchedOT/src/geometric_construction_solver.py code served (permissive licence) · get_code("0588f2d4bbee52c4") |
| eucl_dist | Ran | hci-unihd/BranchedOT/src/utils.py code served (permissive licence) · get_code("33b1fb5567d1ac3d") |
| f | Ran | hci-unihd/BranchedOT/src/angular_stress_heuristic.py code served (permissive licence) · get_code("8037bcf18cb92126") |
| f | Ran | hci-unihd/BranchedOT/src/tree_growing_heuristic.py code served (permissive licence) · get_code("d7c5382f04e9bfb6") |
| get_MST | Ran | hci-unihd/BranchedOT/src/MST_prior.py code served (permissive licence) · get_code("a9eadc58f1d34aad") |
| get_angles | Ran | hci-unihd/BranchedOT/src/tree_growing_heuristic.py code served (permissive licence) · get_code("e507661252422201") |
| get_phi | Ran | hci-unihd/BranchedOT/src/angular_stress_heuristic.py code served (permissive licence) · get_code("c1adb979ae3016bf") |
| get_stationary_angle | Ran | hci-unihd/BranchedOT/src/angular_stress_heuristic.py code served (permissive licence) · get_code("196f3129b9c2f5f1") |
| init_topo | Ran | hci-unihd/BranchedOT/src/tree_growing_heuristic.py code served (permissive licence) · get_code("85cc104fd53fdcdd") |
| kernel | Ran | hci-unihd/BranchedOT/src/greedy_topology_optimization.py code served (permissive licence) · get_code("383e7bb99ccb4c4a") |
| phi_from_x | Ran | hci-unihd/BranchedOT/src/geometric_construction_solver.py code served (permissive licence) · get_code("5b33b44a4c2f5523") |
| preprocess_topo | Ran | hci-unihd/BranchedOT/src/fast_geometry_optimizer.py code served (permissive licence) · get_code("156adf24aa20c165") |
| build_A_and_b | Not yet run | hci-unihd/BranchedOT/src/iterative_geometry_solver.py code served (permissive licence) · get_code("4ad943abb762b708") |
| check_full_tree_topo | Not yet run | hci-unihd/BranchedOT/src/iterative_geometry_solver.py code served (permissive licence) · get_code("606b346190bd1f6c") |
| left_child_halfplane_decider | Not yet run | hci-unihd/BranchedOT/src/com_heuristic.py code served (permissive licence) · get_code("bcd251c042b7ff16") |
| preprocess_from_topo_to_flows | Not yet run | hci-unihd/BranchedOT/src/general_preprocessing.py code served (permissive licence) · get_code("5541e639d3a73f2f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Branched optimal transport (BOT) is a generalization of optimal transport in which transportation costs along an edge are subadditive. This subadditivity models an increase in transport efficiency when shipping mass along the same route, favoring branched transportation networks. We here study the NP-hard optimization of BOT networks connecting a finite number of sources and sinks in R 2 . First, we show how to efficiently find the best geometry of a BOT network for many sources and sinks, given a topology. Second, we argue that a topology with more than three edges meeting at a branching point is never optimal. Third, we show that the results obtained for the Euclidean plane generalize directly to optimal transportation networks on two-dimensional Riemannian manifolds. Finally, we present a simple but effective approximate BOT solver combining geometric optimization with a combinatorial optimization of the network topology.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.07702")
get_code_for_paper("2210.07702")
have("2210.07702")
Connect an agent — have() is free.