awesome new readme

2 files changed, 117 insertions, 46 deletions

diff --git a/README.md b/README.md
index dff334e..ad0d810 100644
--- a/README.md
+++ b/README.md
@@ -16,39 +16,51 @@ learning.
 
 Website and documentation: [https://PythonOT.github.io/](https://PythonOT.github.io/)
 
-POT provides the following generic OT solvers:
-* OT Network Flow solver for the linear program/ Earth Movers Distance [1].
-* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
-* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2],
-  stabilized version [9] [10], greedy Sinkhorn [22] and Screening Sinkhorn [26] with optional GPU
-  implementation (requires cupy).
-* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4].
+Source Code (MIT): [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT)
+
+POT provides the following generic OT solvers (links to examples):
+
+* [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] .
+* [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7].
+* Entropic regularization OT solver with [Sinkhorn Knopp Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] , stabilized version [9] [10], greedy Sinkhorn [22] and [Screening Sinkhorn [26] ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html) with optional GPU implementation (requires cupy).
+* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/plot_convolutional_barycenter.html) [21]  and unmixing [4].
 * Sinkhorn divergence [23] and entropic regularization OT  from empirical data.
-* 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).
-* Gromov-Wasserstein distances and barycenters ([13] and regularized [12])
-* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
-* Non regularized free support Wasserstein barycenters [20].
-* Unbalanced OT with KL relaxation distance and barycenter [10, 25].
-* Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3]
+* [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17].
+* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale).
+* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/plot_gromov_barycenter.html)  (exact [13] and regularized [12])
+ * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/plot_barycenter_fgw.html) [24]
+* [Stochastic solver](https://pythonot.github.io/auto_examples/plot_stochastic.html) for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
+* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/plot_free_support_barycenter.html) [20].
+* [Unbalanced OT](https://pythonot.github.io/auto_examples/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/plot_UOT_barycenter_1D.html) [10, 25].
+* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
   formulations).
 
 POT provides the following Machine Learning related solvers:
-* Optimal transport for domain adaptation with group lasso regularization and   Laplacian regularization [5] [30].
-* Linear OT [14] and Joint OT matrix and mapping estimation [8].
-* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt).
-* JCPOT algorithm for multi-source domain adaptation with target shift [27].
+
+* [Optimal transport for domain
+  adaptation](https://pythonot.github.io/auto_examples/plot_otda_classes.html)
+  with [group lasso regularization](https://pythonot.github.io/auto_examples/plot_otda_classes.html),   [Laplacian regularization](https://pythonot.github.io/auto_examples/plot_otda_laplacian.html) [5] [30] and [semi
+  supervised setting](https://pythonot.github.io/auto_examples/plot_otda_semi_supervised.html).
+* [Linear OT mapping](https://pythonot.github.io/auto_examples/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/plot_otda_mapping.html) [8].
+* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/plot_WDA.html) [11] (requires autograd + pymanopt).
+* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/plot_otda_jcpot.html) [27].
 
 Some demonstrations  are available in the  [documentation](https://pythonot.github.io/auto_examples/index.html).
 
 #### 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:
+If you use this toolbox in your research and find it useful, please cite POT
+using the following bibtex reference:
+```
+Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, Website: https://pythonot.github.io/, 2017
+```
+
+In Bibtex format:
 ```
 @misc{flamary2017pot,
 title={POT Python Optimal Transport library},
 author={Flamary, R{'e}mi and Courty, Nicolas},
-url={https://github.com/rflamary/POT},
+url={https://pythonot.github.io/},
 year={2017}
 }
 ```
diff --git a/docs/source/readme.rst b/docs/source/readme.rst
index 76d37a4..4862523 100644
--- a/docs/source/readme.rst
+++ b/docs/source/readme.rst
@@ -10,31 +10,84 @@ machine learning.
 
 Website and documentation: https://PythonOT.github.io/
 
-POT provides the following generic OT solvers: \* OT Network Flow solver
-for the linear program/ Earth Movers Distance [1]. \* Conditional
-gradient [6] and Generalized conditional gradient for regularized OT
-[7]. \* Entropic regularization OT solver with Sinkhorn Knopp Algorithm
-[2], stabilized version [9] [10], greedy Sinkhorn [22] and Screening
-Sinkhorn [26] with optional GPU implementation (requires cupy). \*
-Bregman projections for Wasserstein barycenter [3], convolutional
-barycenter [21] and unmixing [4]. \* Sinkhorn divergence [23] and
-entropic regularization OT from empirical data. \* 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). \* Gromov-Wasserstein distances and
-barycenters ([13] and regularized [12]) \* Stochastic Optimization for
-Large-scale Optimal Transport (semi-dual problem [18] and dual problem
-[19]) \* Non regularized free support Wasserstein barycenters [20]. \*
-Unbalanced OT with KL relaxation distance and barycenter [10, 25]. \*
-Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3]
-formulations).
-
-POT provides the following Machine Learning related solvers: \* Optimal
-transport for domain adaptation with group lasso regularization and
-Laplacian regularization [5] [30]. \* Linear OT [14] and Joint OT matrix
-and mapping estimation [8]. \* Wasserstein Discriminant Analysis [11]
-(requires autograd + pymanopt). \* JCPOT algorithm for multi-source
-domain adaptation with target shift [27].
+Source Code (MIT): https://github.com/PythonOT/POT
+
+POT provides the following generic OT solvers (links to examples):
+
+-  `OT Network Simplex
+   solver <https://pythonot.github.io/auto_examples/plot_OT_1D.html>`__
+   for the linear program/ Earth Movers Distance [1] .
+-  `Conditional
+   gradient <https://pythonot.github.io/auto_examples/plot_optim_OTreg.html>`__
+   [6] and `Generalized conditional
+   gradient <https://pythonot.github.io/auto_examples/plot_optim_OTreg.html>`__
+   for regularized OT [7].
+-  Entropic regularization OT solver with `Sinkhorn Knopp
+   Algorithm <https://pythonot.github.io/auto_examples/plot_OT_1D.html>`__
+   [2] , stabilized version [9] [10], greedy Sinkhorn [22] and
+   `Screening Sinkhorn
+   [26] <https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html>`__
+   with optional GPU implementation (requires cupy).
+-  Bregman projections for `Wasserstein
+   barycenter <https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html>`__
+   [3], `convolutional
+   barycenter <https://pythonot.github.io/auto_examples/plot_convolutional_barycenter.html>`__
+   [21] and unmixing [4].
+-  Sinkhorn divergence [23] and entropic regularization OT from
+   empirical data.
+-  `Smooth optimal transport
+   solvers <https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html>`__
+   (dual and semi-dual) for KL and squared L2 regularizations [17].
+-  Non regularized `Wasserstein barycenters
+   [16] <https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html>`__)
+   with LP solver (only small scale).
+-  `Gromov-Wasserstein
+   distances <https://pythonot.github.io/auto_examples/plot_gromov.html>`__
+   and `GW
+   barycenters <https://pythonot.github.io/auto_examples/plot_gromov_barycenter.html>`__
+   (exact [13] and regularized [12])
+-  `Fused-Gromov-Wasserstein distances
+   solver <https://pythonot.github.io/auto_examples/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py>`__
+   and `FGW
+   barycenters <https://pythonot.github.io/auto_examples/plot_barycenter_fgw.html>`__
+   [24]
+-  `Stochastic
+   solver <https://pythonot.github.io/auto_examples/plot_stochastic.html>`__
+   for Large-scale Optimal Transport (semi-dual problem [18] and dual
+   problem [19])
+-  Non regularized `free support Wasserstein
+   barycenters <https://pythonot.github.io/auto_examples/plot_free_support_barycenter.html>`__
+   [20].
+-  `Unbalanced
+   OT <https://pythonot.github.io/auto_examples/plot_UOT_1D.html>`__
+   with KL relaxation and
+   `barycenter <https://pythonot.github.io/auto_examples/plot_UOT_barycenter_1D.html>`__
+   [10, 25].
+-  `Partial Wasserstein and
+   Gromov-Wasserstein <https://pythonot.github.io/auto_examples/plot_partial_wass_and_gromov.html>`__
+   (exact [29] and entropic [3] formulations).
+
+POT provides the following Machine Learning related solvers:
+
+-  `Optimal transport for domain
+   adaptation <https://pythonot.github.io/auto_examples/plot_otda_classes.html>`__
+   with `group lasso
+   regularization <https://pythonot.github.io/auto_examples/plot_otda_classes.html>`__,
+   `Laplacian
+   regularization <https://pythonot.github.io/auto_examples/plot_otda_laplacian.html>`__
+   [5] [30] and `semi supervised
+   setting <https://pythonot.github.io/auto_examples/plot_otda_semi_supervised.html>`__.
+-  `Linear OT
+   mapping <https://pythonot.github.io/auto_examples/plot_otda_linear_mapping.html>`__
+   [14] and `Joint OT mapping
+   estimation <https://pythonot.github.io/auto_examples/plot_otda_mapping.html>`__
+   [8].
+-  `Wasserstein Discriminant
+   Analysis <https://pythonot.github.io/auto_examples/plot_WDA.html>`__
+   [11] (requires autograd + pymanopt).
+-  `JCPOT algorithm for multi-source domain adaptation with target
+   shift <https://pythonot.github.io/auto_examples/plot_otda_jcpot.html>`__
+   [27].
 
 Some demonstrations are available in the
 `documentation <https://pythonot.github.io/auto_examples/index.html>`__.
@@ -47,10 +100,16 @@ POT using the following bibtex reference:
 
 ::
 
+    Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, Website: https://pythonot.github.io/, 2017
+
+In Bibtex format:
+
+::
+
     @misc{flamary2017pot,
     title={POT Python Optimal Transport library},
     author={Flamary, R{'e}mi and Courty, Nicolas},
-    url={https://github.com/rflamary/POT},
+    url={https://pythonot.github.io/},
     year={2017}
     }