  This paper focuses on a distributed coupled constrained convex optimization
problem over directed unbalanced and time-varying multi-agent networks, where
the global objective function is the sum of all agents' private local objective
functions, and decisions of all agents are subject to coupled equality and
inequality constraints and a compact convex subset. In the multi-agent
networks, each agent exchanges information with other neighboring agents.
Finally, all agents reach a consensus on decisions, meanwhile achieving the
goal of minimizing the global objective function under the given constraint
conditions. For the purpose of protecting the information privacy of each
agent, we first establish the saddle point problem of the constrained convex
optimization problem considered in this article, then based on the push-pull
method, develop a distributed primal-dual algorithm to solve the dual problem.
Under Slater's condition, we will show that the sequence of points generated by
the proposed algorithm converges to a saddle point of the Lagrange function.
Moreover, we analyze the iteration complexity of the algorithm.
