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diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 82d3e6c..ee32e2b 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -24,7 +24,7 @@ POT provides the following generic OT solvers (links to examples): for regularized OT [7]. - Entropic regularization OT solver with `Sinkhorn Knopp Algorithm <auto_examples/plot_OT_1D.html>`__ - [2] , stabilized version [9] [10], greedy Sinkhorn [22] and + [2] , stabilized version [9] [10] [34], greedy Sinkhorn [22] and `Screening Sinkhorn [26] <auto_examples/plot_screenkhorn_1D.html>`__. - Bregman projections for `Wasserstein @@ -54,6 +54,9 @@ POT provides the following generic OT solvers (links to examples): solver <auto_examples/plot_stochastic.html>`__ for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) +- `Stochastic solver of Gromov + Wasserstein <auto_examples/gromov/plot_gromov.html>`__ + for large-scale problem with any loss functions [33] - Non regularized `free support Wasserstein barycenters <auto_examples/barycenters/plot_free_support_barycenter.html>`__ [20]. @@ -137,19 +140,12 @@ following Python modules: - Numpy (>=1.16) - Scipy (>=1.0) -- Cython (>=0.23) (build only, not necessary when installing wheels - from pip or conda) +- Cython (>=0.23) (build only, not necessary when installing from pip + or conda) 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 - -.. code:: console - - pip install numpy cython - You can install the toolbox through PyPI with: .. code:: console @@ -183,7 +179,8 @@ without errors: import ot -Note that for easier access the module is name ot instead of pot. +Note that for easier access the module is named ``ot`` instead of +``pot``. Dependencies ~~~~~~~~~~~~ @@ -222,7 +219,7 @@ Short examples .. code:: python - # a and b are 1D histograms (sum to 1 and positive) + # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix Wd = ot.emd2(a, b, M) # exact linear program Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT @@ -232,7 +229,7 @@ Short examples .. code:: python - # a and b are 1D histograms (sum to 1 and positive) + # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix T = ot.emd(a, b, M) # exact linear program T_reg = ot.sinkhorn(a, b, M, reg) # entropic regularized OT @@ -287,6 +284,10 @@ The contributors to this library are - `Ievgen Redko <https://ievred.github.io/>`__ (Laplacian DA, JCPOT) - `Adrien Corenflos <https://adriencorenflos.github.io/>`__ (Sliced Wasserstein Distance) +- `Tanguy Kerdoncuff <https://hv0nnus.github.io/>`__ (Sampled Gromov + Wasserstein) +- `Minhui Huang <https://mhhuang95.github.io>`__ (Projection Robust + Wasserstein Distance) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -476,6 +477,30 @@ of measures <https://perso.liris.cnrs.fr/nicolas.bonneel/WassersteinSliced-JMIV.pdf>`__, Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45 +[32] Huang, M., Ma S., Lai, L. (2021). `A Riemannian Block Coordinate +Descent Method for Computing the Projection Robust Wasserstein +Distance <http://proceedings.mlr.press/v139/huang21e.html>`__, +Proceedings of the 38th International Conference on Machine Learning +(ICML). + +[33] Kerdoncuff T., Emonet R., Marc S. `Sampled Gromov +Wasserstein <https://hal.archives-ouvertes.fr/hal-03232509/document>`__, +Machine Learning Journal (MJL), 2021 + +[34] Feydy, J., Séjourné, T., Vialard, F. X., Amari, S. I., Trouvé, A., +& Peyré, G. (2019, April). `Interpolating between optimal transport and +MMD using Sinkhorn +divergences <http://proceedings.mlr.press/v89/feydy19a/feydy19a.pdf>`__. +In The 22nd International Conference on Artificial Intelligence and +Statistics (pp. 2681-2690). PMLR. + +[35] Deshpande, I., Hu, Y. T., Sun, R., Pyrros, A., Siddiqui, N., +Koyejo, S., ... & Schwing, A. G. (2019). `Max-sliced wasserstein +distance and its use for +gans <https://openaccess.thecvf.com/content_CVPR_2019/papers/Deshpande_Max-Sliced_Wasserstein_Distance_and_Its_Use_for_GANs_CVPR_2019_paper.pdf>`__. +In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern +Recognition (pp. 10648-10656). + .. |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 |