Distributed Constrained Optimization Algorithms for Drones

The present study addresses a critical issue within the realm of drones: the challenge of distributed constrained optimization. Our research delves into an optimization scenario where the decision variable is confined to a closed convex set. The primary objective is to develop a distributed algorithm capable of tackling this optimization problem. To achieve this, we have crafted distributed algorithms for both balanced graphs and unbalanced graphs, with the method of feasible direction employed to address the considered constraint, and the method of estimating left eigenvector to address the unbalance, incorporating momentum elements. We have demonstrated that the algorithms exhibit linear convergence when the local objective functions are both smooth and strongly convex, and when the step-sizes are appropriately chosen. Additionally, the simulation outcomes validate the efficacy of our distributed algorithms.