  This paper develops a distributed primal-dual algorithm via event-triggered
mechanism to solve a class of convex optimization problems subject to local set
constraints, coupled equality and inequality constraints. Different from some
existing distributed algorithms with the diminishing step-sizes, our algorithm
uses the constant step-sizes, and is shown to achieve an exact convergence to
an optimal solution with an $O(1/k)$ convergence rate for general convex
objective functions, where $k>0$ is the iteration number. Moreover, by applying
event-triggered communication mechanism, the proposed algorithm can effectively
reduce the communication cost without sacrificing the convergence rate.
Finally, a numerical example is presented to verify the effectiveness of the
proposed algorithm.
