  In two earlier papers, we designed a distributed deterministic asynchronous
algorithm for minimizing the sum of subdifferentiable and proximable functions
and a regularizing quadratic on time-varying graphs based on Dykstra's
algorithm, or block coordinate dual ascent. Each node in the distributed
optimization problem is the sum of a known regularizing quadratic and a
function to be minimized. In this paper, we prove sublinear convergence rates
for the general algorithm, and a linear rate of convergence if the function on
each node is smooth with Lipschitz gradient.
