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| author | Rémi Flamary <remi.flamary@gmail.com> | 2018-06-11 13:13:33 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2018-06-11 13:13:33 +0200 |
| commit | 327b0c6e0ccb0c9453179eb316021c34bcdffec4 (patch) | |
| tree | 7d6f87bbbe776b7194a8fd1469094d1ce9506f97 /docs/source | |
| parent | 530dc93a60e9b81fb8d1b44680deea77dacf660b (diff) | |
| parent | ecb093b95ddbeaeb800b443d3a5c9d5c24c5897c (diff) | |
Merge pull request #50 from rflamary/smooth_ot
Smooth and Sparse OT
Diffstat (limited to 'docs/source')
| -rw-r--r-- | docs/source/all.rst | 5 | ||||
| -rw-r--r-- | docs/source/readme.rst | 46 |
2 files changed, 35 insertions, 16 deletions
diff --git a/docs/source/all.rst b/docs/source/all.rst index c84d968..bbb9833 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -19,6 +19,11 @@ ot.bregman .. automodule:: ot.bregman :members: + +ot.smooth +----- +.. automodule:: ot.smooth + :members: ot.gromov ---------- diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 725c207..5d37f64 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -14,7 +14,11 @@ It provides the following solvers: [1]. - Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation - (required cudamat). + (requires cudamat). +- Smooth optimal transport solvers (dual and semi-dual) for KL and + squared L2 regularizations [17]. +- Non regularized Wasserstein barycenters [16] with LP solver (only + small scale). - Bregman projections for Wasserstein barycenter [3] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] @@ -29,6 +33,21 @@ It provides the following solvers: Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. +Using and citing the toolbox +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If you use this toolbox in your research and find it useful, please cite +POT using the following bibtex reference: + +:: + + @misc{flamary2017pot, + title={POT Python Optimal Transport library}, + author={Flamary, R{'e}mi and Courty, Nicolas}, + url={https://github.com/rflamary/POT}, + year={2017} + } + Installation ------------ @@ -37,7 +56,7 @@ C++ compiler for using the EMD solver and relies on the following Python modules: - Numpy (>=1.11) -- Scipy (>=0.17) +- Scipy (>=1.0) - Cython (>=0.23) - Matplotlib (>=1.5) @@ -212,20 +231,6 @@ languages): - `Marco Cuturi <http://marcocuturi.net/>`__ (Sinkhorn Knopp in Matlab/Cuda) -Using and citing the toolbox ----------------------------- - -If you use this toolbox in your research and find it useful, please cite -POT using the following bibtex reference: - -:: - - @article{flamary2017pot, - title={POT Python Optimal Transport library}, - author={Flamary, R{\'e}mi and Courty, Nicolas}, - year={2017} - } - Contributions and code of conduct --------------------------------- @@ -320,6 +325,15 @@ Journal of Optimization Theory and Applications Vol 43. [15] Peyré, G., & Cuturi, M. (2018). `Computational Optimal Transport <https://arxiv.org/pdf/1803.00567.pdf>`__ . +[16] Agueh, M., & Carlier, G. (2011). `Barycenters in the Wasserstein +space <https://hal.archives-ouvertes.fr/hal-00637399/document>`__. SIAM +Journal on Mathematical Analysis, 43(2), 904-924. + +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). `Smooth and Sparse +Optimal Transport <https://arxiv.org/abs/1710.06276>`__. Proceedings of +the Twenty-First International Conference on Artificial Intelligence and +Statistics (AISTATS). + .. |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 |