A Primal Decomposition Approach to Globally Coupled Aggregative Optimization over Networks

TL;DR

This paper introduces a distributed primal decomposition algorithm based on Douglas-Rachford splitting for multi-agent optimization problems with coupled objectives, ensuring exact convergence and scalability.

Contribution

It proposes a novel distributed algorithm for globally coupled aggregative optimization problems, with convergence guarantees and linear growth of local estimates.

Findings

Algorithm guarantees exact convergence.

Scalable with local estimates growing linearly with neighbors.

Validated through numerical simulations on a transport network.

Abstract

We consider a class of multi-agent optimization problems, where each agent has a local objective function that depends on its own decision variables and the aggregate of others, and is willing to cooperate with other agents to minimize the sum of the local objectives. After associating each agent with an auxiliary variable and the related local estimates, we conduct primal decomposition to the globally coupled problem and reformulate it so that it can be solved distributedly. Based on the Douglas-Rachford method, an algorithm is proposed which ensures the exact convergence to a solution of the original problem. The proposed method enjoys desirable scalability by only requiring each agent to keep local estimates whose number grows linearly with the number of its neighbors. We illustrate our proposed algorithm by numerical simulations on a commodity distribution problem over a transport network.