Solving unregularized optimal transport problems with Julia
This package provides some Julia implementations of algorithms for solving unregularized optimal transport (Kantorovich) problems.
using ExactOptimalTransport using Distances using Tulip # uniform histograms μ = fill(1/250, 250) ν = fill(1/200, 200) # random cost matrix C = pairwise(SqEuclidean(), rand(1, 250), rand(1, 200); dims=2) # compute optimal transport map with Tulip lp = Tulip.Optimizer() P = emd(μ, ν, C, lp) # compute optimal transport cost without recomputing the plan emd2(μ, ν, C, lp; plan=P)
Please see the documentation pages for further information.
- OptimalTransport.jl: Julia implementation of algorithms for regularized optimal transport problems with GPU support.
- StochasticOptimalTransport.jl: Julia implementation of stochastic optimization algorithms for large-scale optimal transport.
- PythonOT.jl: Julia interface for the Python Optimal Transport (POT) package.
Contributions are more than welcome! Please feel free to submit an issue or pull request in this repository.
This package was originally part of OptimalTransport.jl.