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Solving optimization problems in multi-agent networks where each agent onlyhas partial knowledge of the problem has become an increasingly importantproblem. 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. Weshow that Generalized Distributed ADMM converges Q-linearly to the solution ofthe mentioned optimization problem if the over all objective function isstrongly convex but the functions known by each agent are allowed to be onlyconvex. Establishing Q-linear convergence allows for tracking statements thatcan not be made if only R-linear convergence is guaranteed. Further, weestablish the equivalence between Generalized Distributed ADMM and P-EXTRA fora sub-set of mixing matrices. This equivalence yields insights in theconvergence of P-EXTRA when overshooting to accelerate convergence.

On the Q-linear convergence of Distributed Generalized ADMM under non-strongly convex function components