  This paper considers distributed online convex constrained optimization, in
which various agents in a multi-agent system cooperate to minimize a global
cost function through communicating with neighbors over a time-varying network.
When the constraint set of optimization problem is high-dimensional and
complicated, the computational cost of the projection operation often becomes
prohibitive. To handle this problem, we develop a distributed online
Frank-Wolfe optimization algorithm combining with gradient tracking technique.
We rigorously establish the dynamic regret bound of the proposed optimization
algorithm as $\mathcal{O}(\sqrt{T(1+H_T)}+D_T)$, which explicitly depends on
the iteration round $T$, function variation $H_T$, and gradient variation
$D_T$. Finally, the theoretical results are verified and compared in the case
of distributed online ridge regression problems.
