  In recent years, by using Bregman distance, the Lipschitz gradient continuity
and strong convexity were lifted and replaced by relative smoothness and
relative strong convexity. Under the mild assumptions, it was proved that
gradient methods with Bregman regularity converge linearly for single-objective
optimization problems (SOPs). In this paper, we extend the relative smoothness
and relative strong convexity to vector-valued functions and analyze the
convergence of an interior Bregman gradient method for vector optimization
problems (VOPs). Specifically, the global convergence rates are
$\mathcal{O}(\frac{1}{k})$ and $\mathcal{O}(r^{k})(0<r<1)$ for convex and
relative strongly convex VOPs, respectively. Moreover, the proposed method
converges linearly for VOPs that satisfy a vector Bregman-PL inequality.
