[MRG] Add weak OT solver (#341)

* add info in release file

* update tests

* pep8

* add weak OT example

* update plot in doc

* correction ewample with empirical sinkhorn

* better thumbnail

* comment from review

* update documenation

1 files changed, 16 insertions, 0 deletions

diff --git a/ot/gromov.py b/ot/gromov.py
index 6544260..b7e7949 100644
--- a/ot/gromov.py
+++ b/ot/gromov.py
@@ -338,6 +338,10 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', log=False, armijo=F
     - :math:`\mathbf{q}`: distribution in the target space

     - `L`: loss function to account for the misfit between the similarity matrices

 

+    .. note:: This function is backend-compatible and will work on arrays

+        from all compatible backends. But the algorithm uses the C++ CPU backend

+        which can lead to copy overhead on GPU arrays.

+

     Parameters

     ----------

     C1 : array-like, shape (ns, ns)

@@ -436,6 +440,10 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun='square_loss', log=False, armijo=
     Note that when using backends, this loss function is differentiable wrt the

     marices and weights for quadratic loss using the gradients from [38]_.

 

+    .. note:: This function is backend-compatible and will work on arrays

+        from all compatible backends. But the algorithm uses the C++ CPU backend

+        which can lead to copy overhead on GPU arrays.

+

     Parameters

     ----------

     C1 : array-like, shape (ns, ns)

@@ -545,6 +553,10 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
     - :math:`\mathbf{p}` and :math:`\mathbf{q}` are source and target weights (sum to 1)

     - `L` is a loss function to account for the misfit between the similarity matrices

 

+    .. note:: This function is backend-compatible and will work on arrays

+        from all compatible backends. But the algorithm uses the C++ CPU backend

+        which can lead to copy overhead on GPU arrays.

+

     The algorithm used for solving the problem is conditional gradient as discussed in :ref:`[24] <references-fused-gromov-wasserstein>`

 

     Parameters

@@ -645,6 +657,10 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5
     The algorithm used for solving the problem is conditional gradient as

     discussed in :ref:`[24] <references-fused-gromov-wasserstein2>`

 

+    .. note:: This function is backend-compatible and will work on arrays

+        from all compatible backends. But the algorithm uses the C++ CPU backend

+        which can lead to copy overhead on GPU arrays.

+

     Note that when using backends, this loss function is differentiable wrt the

     marices and weights for quadratic loss using the gradients from [38]_.