Distributed aggregative optimization with quantization communication

TL;DR

This paper introduces D-QAGT, a distributed quantized algorithm for aggregative optimization that efficiently minimizes communication while ensuring convergence to the optimal solution in networked multi-agent systems.

Contribution

The paper proposes a novel distributed quantized algorithm that achieves exact optimal solutions with finite-bit communication and linear convergence rate.

Findings

Algorithm achieves exact optimality despite quantization.

Finite bits communication reduces data transmission costs.

Simulation confirms theoretical convergence and effectiveness.

Abstract

In this paper, we focus on an aggregative optimization problem under the communication bottleneck. The aggregative optimization is to minimize the sum of local cost functions. Each cost function depends on not only local state variables but also the sum of functions of global state variables. The goal is to solve the aggregative optimization problem through distributed computation and local efficient communication over a network of agents without a central coordinator. Using the variable tracking method to seek the global state variables and the quantization scheme to reduce the communication cost spent in the optimization process, we develop a novel distributed quantized algorithm, called D-QAGT, to track the optimal variables with finite bits communication. Although quantization may lose transmitting information, our algorithm can still achieve the exact optimal solution with a linear convergence rate. Simulation experiments on an optimal placement problem is carried out to verify the correctness of the theoretical results.