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2 changes: 1 addition & 1 deletion ot/bregman/_geomloss.py
Original file line number Diff line number Diff line change
Expand Up @@ -87,7 +87,7 @@ def empirical_sinkhorn2_geomloss(X_s, X_t, reg, a=None, b=None, metric='sqeuclid

The algorithm used for solving the problem is the Sinkhorn-Knopp matrix
scaling algorithm as proposed in and computed in log space for
better stability and epsilon-scaling. The solution is computed ina lzy way
better stability and epsilon-scaling. The solution is computed in a lazy way
using the Geomloss [60] and the KeOps library [61].

Parameters
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2 changes: 1 addition & 1 deletion ot/solvers.py
Original file line number Diff line number Diff line change
Expand Up @@ -1272,7 +1272,7 @@ def solve_sample(X_a, X_b, a=None, b=None, metric='sqeuclidean', reg=None, reg_t
if not lazy0: # store plan if not lazy
plan = lazy_plan[:]

elif method.startswith('geomloss'): # Geomloss solver for entropi OT
elif method.startswith('geomloss'): # Geomloss solver for entropic OT

split_method = method.split('_')
if len(split_method) == 2:
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