update readme

3 files changed, 15 insertions, 3 deletions

diff --git a/README.md b/README.md
index 466c09c..b3068ee 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,7 @@ 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).
+* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularization [17].
 * Non regularized Wasserstein barycenters [16] with LP solver.
 * Bregman projections for Wasserstein barycenter [3] and unmixing [4].
 * Optimal transport for domain adaptation with group lasso regularization [5]
@@ -213,3 +214,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t
 [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).
diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py
index 4927617..dec84db 100644
--- a/examples/plot_OT_1D_smooth.py
+++ b/examples/plot_OT_1D_smooth.py
@@ -1,8 +1,8 @@
 # -*- coding: utf-8 -*-
 """
-====================
-1D optimal transport
-====================
+===========================
+1D smooth optimal transport 
+===========================
 
 This example illustrates the computation of EMD, Sinkhorn and smooth OT plans
 and their visualization.
diff --git a/ot/smooth.py b/ot/smooth.py
index 1a9972b..b3649e9 100644
--- a/ot/smooth.py
+++ b/ot/smooth.py
@@ -22,6 +22,7 @@
 #OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 #THE POSSIBILITY OF SUCH DAMAGE.
 
+
 # Author: Mathieu Blondel
 #         Remi Flamary <remi.flamary@unice.fr>
 
@@ -31,6 +32,13 @@ Smooth and Sparse Optimal Transport.
 Mathieu Blondel, Vivien Seguy, Antoine Rolet.
 In Proc. of AISTATS 2018.
 https://arxiv.org/abs/1710.06276
+
+[17] Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal 
+Transport. Proceedings of the Twenty-First International Conference on 
+Artificial Intelligence and Statistics (AISTATS).
+
+Original code from https://github.com/mblondel/smooth-ot/
+
 """
 
 import numpy as np
@@ -402,6 +410,7 @@ def get_plan_from_semi_dual(alpha, b, C, regul):
 
 def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9,
                    numItermax=500, log=False):
+    
 
     if reg_type.lower() in ['l2', 'squaredl2']:
         regul = SquaredL2(gamma=reg)