debug sgd dual

1 files changed, 16 insertions, 12 deletions

diff --git a/README.md b/README.md
index 8e8dcd4..677a23b 100644
--- a/README.md
+++ b/README.md
@@ -15,7 +15,8 @@ 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] with optional GPU implementation (requires cudamat).
-* Non regularized Wasserstein barycenters [16] with LP solver.
+* 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]
 * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
@@ -26,12 +27,24 @@ 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
 
 The library has been tested on Linux, MacOSX and Windows. It requires a 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)
 
@@ -158,16 +171,7 @@ This toolbox benefit a lot from open source research and we would like to thank
 * [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD)
 * [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
 
 Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md).
@@ -216,7 +220,7 @@ You can also post bug reports and feature requests in Github issues. Make sure t
 
 [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/pdf/1710.06276.pdf). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS).
+[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).
 
 [18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](arXiv preprint arxiv:1605.08527). Advances in Neural Information Processing Systems (2016).