update README.md

Author: cedricvincentcuaz

README.md

@@ -36,6 +36,7 @@ POT provides the following generic OT solvers (links to examples):
 * [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
   formulations).
 * [Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance.html) [31, 32] and Max-sliced Wasserstein [35] that can be used for gradient flows [36].
+* [Graph Dictionary Learning solvers](https://pythonot.github.io/auto_examples/gromov/plot_gromov_wasserstein_dictionary_learning.html) [38].
 * [Several backends](https://pythonot.github.io/quickstart.html#solving-ot-with-multiple-backends) for easy use of POT with  [Pytorch](https://pytorch.org/)/[jax](https://github.com/google/jax)/[Numpy](https://numpy.org/)/[Cupy](https://cupy.dev/)/[Tensorflow](https://www.tensorflow.org/) arrays.
 
 POT provides the following Machine Learning related solvers:
@@ -198,6 +199,7 @@ The contributors to this library are
 * [Tanguy Kerdoncuff](https://hv0nnus.github.io/) (Sampled Gromov Wasserstein)
 * [Minhui Huang](https://mhhuang95.github.io) (Projection Robust Wasserstein Distance)
 * [Nathan Cassereau](https://github.com/ncassereau-idris) (Backends)
+* [Cédric Vincent-Cuaz](https://github.com/cedricvincentcuaz) (Graph Dictionary Learning)
 
 This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages):
 

test/test_gromov.py

@@ -3,6 +3,7 @@
 # Author: Erwan Vautier <erwan.vautier@gmail.com>
 #         Nicolas Courty <ncourty@irisa.fr>
 #         Titouan Vayer <titouan.vayer@irisa.fr>
+#         Cédric Vincent-Cuaz <cedric.vincent-cuaz@inria.fr>
 #
 # License: MIT License