  We design a novel algorithm for optimal transport by drawing from the
entropic optimal transport, mirror descent and conjugate gradients literatures.
Our scalable and GPU parallelizable algorithm is able to compute the
Wasserstein distance with extreme precision, reaching relative error rates of
$10^{-8}$ without numerical stability issues. Empirically, the algorithm
converges to high precision solutions more quickly in terms of wall-clock time
than a variety of algorithms including log-domain stabilized Sinkhorn's
Algorithm. We provide careful ablations with respect to algorithm and problem
parameters, and present benchmarking over upsampled MNIST images, comparing to
various recent algorithms over high-dimensional problems. The results suggest
that our algorithm can be a useful addition to the practitioner's optimal
transport toolkit.
