re-org readme

1 files changed, 28 insertions, 36 deletions

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@@ -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
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^