  We provide a lower bound showing that the $O(1/k)$ convergence rate of the
NoLips method (a.k.a. Bregman Gradient) is optimal for the class of functions
satisfying the $h$-smoothness assumption. This assumption, also known as
relative smoothness, appeared in the recent developments around the Bregman
Gradient method, where acceleration remained an open issue. On the way, we show
how to constructively obtain the corresponding worst-case functions by
extending the computer-assisted performance estimation framework of Drori and
Teboulle (Mathematical Programming, 2014) to Bregman first-order methods, and
to handle the classes of differentiable and strictly convex functions.
