  We show that the Subgradient algorithm is universal for online learning on
the simplex in the sense that it simultaneously achieves $O(\sqrt N)$ regret
for adversarial costs and $O(1)$ pseudo-regret for i.i.d costs. To the best of
our knowledge this is the first demonstration of a universal algorithm on the
simplex that is not a variant of Hedge. Since Subgradient is a popular and
widely used algorithm our results have immediate broad application.
