Generalized conditional gradient: analysis of convergence and applications. Rakotomamonjy, A., Flamary, R., & Courty, N. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. Rakotomamonjy, Optimal Transport for Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.PP, no.99, pp.1-1 Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. Iterative Bregman projections for regularized transportation problems. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. In Advances in Neural Information Processing Systems (pp. Sinkhorn distances: Lightspeed computation of optimal transport. In ACM Transactions on Graphics (TOG) (Vol. Displacement interpolation using Lagrangian mass transport. Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. You can also post bug reports and feature requests in Github issues. You can ask questions and join the development discussion: Each member of the project is expected to follow the code of conduct. POT has benefited from the financing or manpower from the following partners:Įvery contribution is welcome and should respect the contribution guidelines. The numerous contributors to this library are listed here. This toolbox has been created and is maintained by The full documentation with examples and output is available on. The examples folder contain several examples and use case for the library. barycenter ( A, M, reg ) # reg is regularization parameter Examples and Notebooks # A is a n*d matrix containing d 1D histograms # M is the ground cost matrix ba = ot. You can install the toolbox through PyPI with: It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules:Ĭython (>=0.23) (build only, not necessary when installing from pip or conda) The library has been tested on Linux, MacOSX and Windows. Using the following reference from our JMLR Installation If you use this toolbox in your research and find it useful, please cite POT Some other examples are available in the documentation. JCPOT algorithm for multi-source domain adaptation with target shift. Wasserstein Discriminant Analysis (requires autograd + pymanopt). Linear OT mapping and Joint OT mapping estimation. With group lasso regularization, Laplacian regularization and semi POT provides the following Machine Learning related solvers: Several backends for easy use of POT with Pytorch/ jax/ Numpy/ Cupy/ Tensorflow arrays. Semi-relaxed (Fused) Gromov-Wasserstein divergences. Sliced Wasserstein and Max-sliced Wasserstein that can be used for gradient flows. Partial Wasserstein and Gromov-Wasserstein (exact and entropic Also exact unbalanced OT with KL and quadratic regularization and the regularization path of UOT One dimensional Unbalanced OT with KL relaxation and barycenter. Non regularized free support Wasserstein barycenters. Sampled solver of Gromov Wasserstein for large-scale problem with any loss functions Large-scale Optimal Transport (semi-dual problem and dual problem ) ![]() Gromov-Wasserstein distances and GW barycenters (exact and regularized ), differentiable using gradients from Graph Dictionary Learning įused-Gromov-Wasserstein distances solver and FGW barycenters Non regularized Wasserstein barycenters with LP solver (only small scale). Weak OT solver between empirical distributions Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations. Sinkhorn divergence and entropic regularization OT from empirical data.ĭebiased Sinkhorn barycenters Sinkhorn divergence barycenter īregman projections for Wasserstein barycenter, convolutional barycenter and unmixing. Įntropic regularization OT solver with Sinkhorn Knopp Algorithm, stabilized version, greedy Sinkhorn and Screening Sinkhorn. Ĭonditional gradient and Generalized conditional gradient for regularized OT. OT Network Simplex solver for the linear program/ Earth Movers Distance. POT provides the following generic OT solvers (links to examples): Problems related to Optimal Transport for signal, image processing and machine This open source Python library provide several solvers for optimization
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