  An optimal transport problem on finite spaces is a linear program. Recently,
a relaxation of the optimal transport problem via strictly convex functions,
especially via the Kullback--Leibler divergence, sheds new light on data
sciences. This paper provides the mathematical foundations and an iterative
process based on a gradient descent for the relaxed optimal transport problem
via Bregman divergences.
