  We present an efficient algorithm for regularized optimal transport. In
contrast to previous methods, we use the Douglas-Rachford splitting technique
to develop an efficient solver that can handle a broad class of regularizers.
The algorithm has strong global convergence guarantees, low per-iteration cost,
and can exploit GPU parallelization, making it considerably faster than the
state-of-the-art for many problems. We illustrate its competitiveness in
several applications, including domain adaptation and learning of generative
models.
