  In this paper, we study the distributed optimization problem using
approximate first-order information. We suppose the agent can repeatedly call
an inexact first-order oracle of each individual objective function and
exchange information with its time-varying neighbors. We revisit the
distributed subgradient method in this circumstance and show its suboptimality
under square summable but not summable step sizes. We also present several
conditions on the inexactness of the local oracles to ensure an exact
convergence of the iterative sequences towards the global optimal solution. A
numerical example is given to verify the efficiency of our algorithm.
