Expand Up @@ -18,7 +18,7 @@ def weak_optimal_transport(Xa, Xb, a=None, b=None, verbose=False, log=False, G0=

.. math:: \gamma = \mathop{\arg \min}_\gamma \quad \|X_a-diag(1/a)\gammaX_b\|_F^2 \gamma = \mathop{\arg \min}_\gamma \quad \sum_i \mathbf{a}_i \left(\mathbf{X^a}_i - \frac{1}{\mathbf{a}_i} \sum_j \gamma_{ij} \mathbf{X^b}_j \right)^2
s.t. \ \gamma \mathbf{1} = \mathbf{a}
Expand All @@ -28,7 +28,7 @@ def weak_optimal_transport(Xa, Xb, a=None, b=None, verbose=False, log=False, G0=
where :
- :math:`X_a` :math:`X_b` are the sample matrices. - :math:`X^a` and :math:`X^b` are the sample matrices. - :math:`\mathbf{a}` and :math:`\mathbf{b}` are the sample weights

Expand All @@ -49,6 +49,8 @@ def weak_optimal_transport(Xa, Xb, a=None, b=None, verbose=False, log=False, G0= Source histogram (uniform weight if empty list) b : (nt,) array-like, float Target histogram (uniform weight if empty list)) G0 : (ns,nt) array-like, float initial guess (default is indep joint density) numItermax : int, optional Max number of iterations numItermaxEmd : int, optional Expand Down