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diff --git a/docs/source/readme.rst b/docs/source/readme.rst index e7c2bd1..0871779 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -12,9 +12,11 @@ It provides the following solvers: - OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] - and stabilized version [9][10] and greedy SInkhorn [22] with optional - GPU implementation (requires cudamat). +- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], + stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU + implementation (requires cupy). +- Sinkhorn divergence [23] and entropic regularization OT from + empirical data. - Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. - Non regularized Wasserstein barycenters [16] with LP solver (only @@ -33,6 +35,7 @@ It provides the following solvers: - Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) - Non regularized free support Wasserstein barycenters [20]. +- Unbalanced OT with KL relaxation distance and barycenter [10, 25]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -67,6 +70,13 @@ modules: Pip installation ^^^^^^^^^^^^^^^^ +Note that due to a limitation of pip, ``cython`` and ``numpy`` need to +be installed prior to installing POT. This can be done easily with + +:: + + pip install numpy cython + You can install the toolbox through PyPI with: :: @@ -115,14 +125,9 @@ below pip install pymanopt autograd -- **ot.gpu** (GPU accelerated OT) depends on cudamat that have to be - installed with: - - :: - - git clone https://github.com/cudamat/cudamat.git - cd cudamat - python setup.py install --user # for user install (no root) +- **ot.gpu** (GPU accelerated OT) depends on cupy that have to be + installed following instructions on `this + page <https://docs-cupy.chainer.org/en/stable/install.html>`__. obviously you need CUDA installed and a compatible GPU. @@ -209,10 +214,13 @@ nbviewer <https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks Acknowledgements ---------------- -The contributors to this library are: +This toolbox has been created and is maintained by - `Rémi Flamary <http://remi.flamary.com/>`__ - `Nicolas Courty <http://people.irisa.fr/Nicolas.Courty/>`__ + +The contributors to this library are + - `Alexandre Gramfort <http://alexandre.gramfort.net/>`__ - `Laetitia Chapel <http://people.irisa.fr/Laetitia.Chapel/>`__ - `Michael Perrot <http://perso.univ-st-etienne.fr/pem82055/>`__ @@ -226,6 +234,9 @@ The contributors to this library are: - `Kilian Fatras <https://kilianfatras.github.io/>`__ - `Alain Rakotomamonjy <https://sites.google.com/site/alainrakotomamonjy/home>`__ +- `Vayer Titouan <https://tvayer.github.io/>`__ +- `Hicham Janati <https://hichamjanati.github.io/>`__ (Unbalanced OT) +- `Romain Tavenard <https://rtavenar.github.io/>`__ (1d Wasserstein) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -366,6 +377,20 @@ approximation algorithms for optimal transport via Sinkhorn iteration <https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf>`__, Advances in Neural Information Processing Systems (NIPS) 31 +[23] Aude, G., Peyré, G., Cuturi, M., `Learning Generative Models with +Sinkhorn Divergences <https://arxiv.org/abs/1706.00292>`__, Proceedings +of the Twenty-First International Conference on Artficial Intelligence +and Statistics, (AISTATS) 21, 2018 + +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. +(2019). `Optimal Transport for structured data with application on +graphs <http://proceedings.mlr.press/v97/titouan19a.html>`__ Proceedings +of the 36th International Conference on Machine Learning (ICML). + +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2019). +`Learning with a Wasserstein Loss <http://cbcl.mit.edu/wasserstein/>`__ +Advances in Neural Information Processing Systems (NIPS). + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg |