3 research outputs found
Fast-Convergent Dynamics for Distributed Resource Allocation Over Sparse Time-Varying Networks
In this paper, distributed dynamics are deployed to solve resource allocation
over time-varying multi-agent networks. The state of each agent represents the
amount of resources used/produced at that agent while the total amount of
resources is fixed. The idea is to optimally allocate the resources among the
group of agents by reducing the total cost functions subject to fixed amount of
total resources. The information of each agent is restricted to its own state
and cost function and those of its immediate neighbors. This is motivated by
distributed applications such as in mobile edge-computing, economic dispatch
over smart grids, and multi-agent coverage control. The non-Lipschitz dynamics
proposed in this work shows fast convergence as compared to the linear and some
nonlinear solutions in the literature. Further, the multi-agent network
connectivity is more relaxed in this paper. To be more specific, the proposed
dynamics even reaches optimal solution over time-varying disconnected
undirected networks as far as the union of these networks over some bounded
non-overlapping time-intervals includes a spanning-tree. The proposed
convergence analysis can be applied for similar 1st-order resource allocation
nonlinear dynamics. We provide simulations to verify our results