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authorRémi Flamary <remi.flamary@gmail.com>2018-06-11 12:03:58 +0200
committerRémi Flamary <remi.flamary@gmail.com>2018-06-11 12:03:58 +0200
commit77d80f8e3d9df9d838fa19685f2a5fea2ce888fb (patch)
tree8d14e7fb7a829f61d05c76c1504626ceb2babb57 /docs
parent927ec792baefbf7ee587df872aba16e6f54001b4 (diff)
update documentation
Diffstat (limited to 'docs')
-rw-r--r--docs/source/readme.rst46
1 files changed, 30 insertions, 16 deletions
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