868 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
Gather-and-broadcast frequency control in power systems
We propose a novel frequency control approach in between centralized and
distributed architectures, that is a continuous-time feedback control version
of the dual decomposition optimization method. Specifically, a convex
combination of the frequency measurements is centrally aggregated, followed by
an integral control and a broadcast signal, which is then optimally allocated
at local generation units. We show that our gather-and-broadcast control
architecture comprises many previously proposed strategies as special cases. We
prove local asymptotic stability of the closed-loop equilibria of the
considered power system model, which is a nonlinear differential-algebraic
system that includes traditional generators, frequency-responsive devices, as
well as passive loads, where the sources are already equipped with primary
droop control. Our feedback control is designed such that the closed-loop
equilibria of the power system solve the optimal economic dispatch problem
Uncertain Multi-Agent Systems with Distributed Constrained Optimization Missions and Event-Triggered Communications: Application to Resource Allocation
This paper deals with solving distributed optimization problems with equality
constraints by a class of uncertain nonlinear heterogeneous dynamic multi-agent
systems. It is assumed that each agent with an uncertain dynamic model has
limited information about the main problem and limited access to the
information of the state variables of the other agents. A distributed algorithm
that guarantees cooperative solving of the constrained optimization problem by
the agents is proposed. Via this algorithm, the agents do not need to
continuously broadcast their data. It is shown that the proposed algorithm can
be useful in solving resource allocation problems
A Primal Decomposition Method with Suboptimality Bounds for Distributed Mixed-Integer Linear Programming
In this paper we deal with a network of agents seeking to solve in a
distributed way Mixed-Integer Linear Programs (MILPs) with a coupling
constraint (modeling a limited shared resource) and local constraints. MILPs
are NP-hard problems and several challenges arise in a distributed framework,
so that looking for suboptimal solutions is of interest. To achieve this goal,
the presence of a linear coupling calls for tailored decomposition approaches.
We propose a fully distributed algorithm based on a primal decomposition
approach and a suitable tightening of the coupling constraints. Agents
repeatedly update local allocation vectors, which converge to an optimal
resource allocation of an approximate version of the original problem. Based on
such allocation vectors, agents are able to (locally) compute a mixed-integer
solution, which is guaranteed to be feasible after a sufficiently large time.
Asymptotic and finite-time suboptimality bounds are established for the
computed solution. Numerical simulations highlight the efficacy of the proposed
methodology.Comment: 57th IEEE Conference on Decision and Contro
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