re-org readme

Author: rflamary

README.md

@@ -16,27 +16,30 @@ learning.
 
 Website and documentation: [https://PythonOT.github.io/](https://PythonOT.github.io/)
 
-POT provides the following solvers:
-
+POT provides the following generic OT solvers:
 * OT Network Flow solver for the linear program/ Earth Movers Distance [1].
-* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy).
+* 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).
-* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4].
-* Optimal transport for domain adaptation with group lasso regularization and Laplacian regularization [5][30]
-* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
-* Linear OT [14] and Joint OT matrix and mapping estimation [8].
-* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt).
 * 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].
-* Screening Sinkhorn Algorithm for OT [26].
+* 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].
-* Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] formulations).
 
-Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.
+Some demonstrations  are available in the  [documentation](https://pythonot.github.io/auto_examples/index.html).
 
 #### Using and citing the toolbox
 

docs/source/readme.rst

@@ -10,42 +10,34 @@ machine learning.
 
 Website and documentation: https://PythonOT.github.io/
 
-POT 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],
-   stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU
-   implementation (requires cupy).
--  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).
--  Bregman projections for Wasserstein barycenter [3], convolutional
-   barycenter [21] and unmixing [4].
--  Optimal transport for domain adaptation with group lasso
-   regularization and Laplacian regularization [5][30]
--  Conditional gradient [6] and Generalized conditional gradient for
-   regularized OT [7].
--  Linear OT [14] and Joint OT matrix and mapping estimation [8].
--  Wasserstein Discriminant Analysis [11] (requires autograd +
-   pymanopt).
--  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].
--  Screening Sinkhorn Algorithm for OT [26].
--  JCPOT algorithm for multi-source domain adaptation with target shift
-   [27].
--  Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic
-   [3] formulations).
-
-Some demonstrations (both in Python and Jupyter Notebook format) are
-available in the examples folder.
+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].
+
+Some demonstrations are available in the
+`documentation <https://pythonot.github.io/auto_examples/index.html>`__.
 
 Using and citing the toolbox
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^