remarques cédri

Author: rflamary

RELEASES.md

@@ -16,6 +16,7 @@
 + Add exact line-search for `gromov_wasserstein` and `fused_gromov_wasserstein` with KL loss (PR #556)
 + Add KL loss to all semi-relaxed (Fused) Gromov-Wasserstein solvers (PR #559)
 + Further upgraded unbalanced OT solvers for more flexibility and future use (PR #551)
++ New API function `ot.solve_sample` for solving OT problems from empirical samples (PR #563)
 
 #### Closed issues
 - Fix line search evaluating cost outside of the interpolation range (Issue #502, PR #504)

ot/bregman/_empirical.py

@@ -18,7 +18,10 @@
 
 
 def get_sinkhorn_lazytensor(X_a, X_b, f, g, metric='sqeuclidean', reg=1e-1, nx=None):
-    """ Get a LazyTensor of sinkhorn solution T = exp(f+g^T-C/reg)
+    r""" Get a LazyTensor of Sinkhorn solution from the dual potentials
+
+    The returned LazyTensor is
+    :math:`\mathbf{T} = exp(  \mathbf{f} \mathbf{1}_b^\top + \mathbf{1}_a \mathbf{g}^\top - \mathbf{C}/reg)`, where :math:`\mathbf{C}` is the pairwise metric matrix between samples :math:`\mathbf{X}_a` and :math:`\mathbf{X}_b`.
 
     Parameters
     ----------
@@ -41,7 +44,7 @@ def get_sinkhorn_lazytensor(X_a, X_b, f, g, metric='sqeuclidean', reg=1e-1, nx=N
     Returns
     -------
     T : LazyTensor
-        Lowrank tensor T = exp(f+g^T-C/reg)
+        Sinkhorn solution tensor
     """
 
     if nx is None: