Zeroth-order feedback optimization for cooperative multi-agent systems

We study a class of cooperative multi-agent optimization problems, where each agent is associated with a local action vector and a local cost, and the goal is to cooperatively find the joint action profile that minimizes the average of the local costs. We consider the setting where gradient information is not readily available, and the agents only observe their local costs incurred by their actions as a feedback to determine their new actions. We propose a zeroth-order feedback optimization scheme and provide explicit complexity bounds for the constrained convex setting with noiseless and noisy local cost observations. We also discuss briefly on the impacts of knowledge of local function dependence between agents. The algorithm's performance is justified by a numerical example of distributed routing control. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

We study a class of cooperative multi-agent optimization problems, where the goal is to cooperatively find the joint action profile that minimizes the average of the local costs. We propose a zeroth-order feedback optimization scheme and provide explicit complexity bounds for different scenarios. The algorithm's performance is justified by a numerical example.