  Optimal transport is an important tool in machine learning, allowing to
capture geometric properties of the data through a linear program on transport
polytopes. We present a single-loop optimization algorithm for minimizing
general convex objectives on these domains, utilizing the principles of
Sinkhorn matrix scaling and mirror descent. The proposed algorithm is robust to
noise, and can be used in an online setting. We provide theoretical guarantees
for convex objectives and experimental results showcasing it effectiveness on
both synthetic and real-world data.
