  This work studies nonconvex distributed constrained optimization over
stochastic communication networks. We revisit the distributed dual averaging
algorithm, which is known to converge for convex problems. We start from the
centralized case, for which the change of two consecutive updates is taken as
the suboptimality measure. We validate the use of such a measure by showing
that it is closely related to stationarity. This equips us with a handle to
study the convergence of dual averaging in nonconvex optimization. We prove
that the squared norm of this suboptimality measure converges at rate
$\mathcal{O}(1/t)$. Then, for the distributed setup we show convergence to the
stationary point at rate $\mathcal{O}(1/t)$. Finally, a numerical example is
given to illustrate our theoretical results.
