  Distributed allocation finds applications in many scenarios including CPU
scheduling, distributed energy resource management, and networked coverage
control. In this paper, we propose a fast convergent optimization algorithm
with a tunable rate using the signum function. The convergence rate of the
proposed algorithm can be managed by changing two parameters. We prove
convergence over uniformly-connected multi-agent networks. Therefore, the
solution converges even if the network loses connectivity at some finite time
intervals. The proposed algorithm is all-time feasible, implying that at any
termination time of the algorithm, the resource-demand feasibility holds. This
is in contrast to asymptotic feasibility in many dual formulation solutions
(e.g., ADMM) that meet resource-demand feasibility over time and
asymptotically.
