From bffba0033fda3a45d7cbbde5165e09e886262ab2 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Wed, 22 Apr 2020 12:44:45 +0200
Subject: awesome new readme

---
 docs/source/readme.rst | 111 +++++++++++++++++++++++++++++++++++++------------
 1 file changed, 85 insertions(+), 26 deletions(-)

(limited to 'docs/source/readme.rst')

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>`__.
@@ -45,12 +98,18 @@ 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:
 
+::
+
+    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}
     }
 
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