Consensus-based Distributed Optimization Enhanced by Integral Feedback

TL;DR

This paper introduces a distributed optimization algorithm for multi-agent networks that uses integral feedback to achieve exponential convergence, robustness, and efficiency in solving convex problems with constraints.

Contribution

The paper presents a novel distributed optimization algorithm based on integral feedback, improving convergence rate and robustness over existing methods.

Findings

Achieves exponential convergence to the optimal solution.

Requires low communication bandwidth due to integral feedback.

Demonstrates robustness against disturbances through simulations.

Abstract

Inspired and underpinned by the idea of integral feedback, a distributed constant gain algorithm is proposed for multi-agent networks to solve convex optimization problems with local linear constraints. Assuming agent interactions are modeled by an undirected graph, the algorithm is capable of achieving the optimum solution with an exponential convergence rate. Furthermore, inherited from the beneficial integral feedback, the proposed algorithm has attractive requirements on communication bandwidth and good robustness against disturbance. Both analytical proof and numerical simulations are provided to validate the effectiveness of the proposed distributed algorithms in solving constrained optimization problems.