  This paper presents a decentralized algorithm for a team of agents to track
time-varying fixed points that are the solutions to time-varying convex
optimization problems. The algorithm is first-order, and it allows for total
asynchrony in the communications and computations of all agents, i.e., all such
operations can occur with arbitrary timing and arbitrary (finite) delays.
Convergence rates are computed in terms of the communications and computations
that agents execute, without specifying when they must occur. These rates apply
to convergence to the minimum of each individual objective function, as well as
agents' long-run behavior as their objective functions change. Then, to improve
the usage of limited communication and computation resources, we optimize the
timing of agents' operations relative to changes in their objective functions
to minimize total fixed point tracking error over time. Simulation results are
presented to illustrate these developments in practice and empirically assess
their robustness to uncertainties in agents' update laws.
