  This work presents a universal accelerated first-order primal-dual method for
affinely constrained convex optimization problems. It can handle both Lipschitz
and H\"{o}lder gradients but does not need to know the smoothness level of the
objective function. In line search part, it uses dynamically decreasing
parameters and produces approximate Lipschitz constant with moderate magnitude.
In addition, based on a suitable discrete Lyapunov function and tight decay
estimates of some differential/difference inequalities, a universal optimal
mixed-type convergence rate is established. Some numerical tests are provided
to confirm the efficiency of the proposed method.
