Distributed Algorithms for Solving a Class of Convex Feasibility Problems
It provides a distributed solution for convex feasibility problems in multi-agent systems, which is relevant for networked control and optimization but is incremental given existing work on distributed optimization.
This paper proposes distributed subgradient and projection algorithms for multi-agent systems to solve convex feasibility problems, achieving asymptotic consensus to a feasible solution under mild connectivity conditions on directed graphs.
In this paper, a class of convex feasibility problems (CFPs) are studied for multi-agent systems through local interactions. The objective is to search a feasible solution to the convex inequalities with some set constraints in a distributed manner. The distributed control algorithms, involving subgradient and projection, are proposed for both continuous- and discrete-time systems, respectively. Conditions associated with connectivity of the directed communication graph are given to ensure convergence of the algorithms. It is shown that under mild conditions, the states of all agents reach consensus asymptotically and the consensus state is located in the solution set of the CFP. Simulation examples are presented to demonstrate the effectiveness of the theoretical results.