  Bandit Convex Optimization (BCO) is a fundamental framework for modeling
sequential decision-making with partial information, where the only feedback
available to the player is the one-point or two-point function values. In this
paper, we investigate BCO in non-stationary environments and choose the
\emph{dynamic regret} as the performance measure, which is defined as the
difference between the cumulative loss incurred by the algorithm and that of
any feasible comparator sequence. Let $T$ be the time horizon and $P_T$ be the
path-length of the comparator sequence that reflects the non-stationarity of
environments. We propose a novel algorithm that achieves
$O(T^{3/4}(1+P_T)^{1/2})$ and $O(T^{1/2}(1+P_T)^{1/2})$ dynamic regret
respectively for the one-point and two-point feedback models. The latter result
is optimal, matching the $\Omega(T^{1/2}(1+P_T)^{1/2})$ lower bound established
in this paper. Notably, our algorithm is more adaptive to non-stationary
environments since it does not require prior knowledge of the path-length $P_T$
ahead of time, which is generally unknown.
