An algorithm achieves O(log d) alternating regret for online linear optimization over the probability simplex, which remains constant for any time horizon T

For OLO over the probability simplex $Δ_d$, we give an algorithm with $O(\log d)$ alternating regret that remains a constant for any time horizon $T$, and a matching lower bound.

http://arxiv.org/abs/2608.24731v1