  Solving optimization problems in multi-agent networks where each agent only
has partial knowledge of the problem has become an increasingly important
problem. In this paper we consider the problem of minimizing the sum of $n$
convex functions. We assume that each function is only known by one agent. We
show that Generalized Distributed ADMM converges Q-linearly to the solution of
the mentioned optimization problem if the over all objective function is
strongly convex but the functions known by each agent are allowed to be only
convex. Establishing Q-linear convergence allows for tracking statements that
can not be made if only R-linear convergence is guaranteed. Further, we
establish the equivalence between Generalized Distributed ADMM and P-EXTRA for
a sub-set of mixing matrices. This equivalence yields insights in the
convergence of P-EXTRA when overshooting to accelerate convergence.
