Distributed Algorithms for Solving a Class of Convex Feasibility Problems

TL;DR

This paper develops distributed algorithms for multi-agent systems to solve convex feasibility problems using local interactions, ensuring convergence to a common feasible solution.

Contribution

It introduces novel subgradient and projection-based algorithms for both continuous and discrete systems with connectivity conditions for convergence.

Findings

Agents reach consensus asymptotically

Consensus state lies in the solution set

Algorithms are effective as shown by simulations

Abstract

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.