Distributed Pareto Optimization via Diffusion Strategies

TL;DR

This paper introduces a distributed diffusion strategy for multi-objective optimization that enables a network of agents to collaboratively find Pareto optimal solutions with proven convergence properties.

Contribution

It develops a novel diffusion-based approach for distributed Pareto optimization, including convergence analysis and application to collaborative decision making.

Findings

Agents converge to Pareto optimal solutions within small MSE bounds

The diffusion process can be decomposed into three operators with a fixed point

The method applies to real-world scenarios like finance decision networks

Abstract

We consider solving multi-objective optimization problems in a distributed manner by a network of cooperating and learning agents. The problem is equivalent to optimizing a global cost that is the sum of individual components. The optimizers of the individual components do not necessarily coincide and the network therefore needs to seek Pareto optimal solutions. We develop a distributed solution that relies on a general class of adaptive diffusion strategies. We show how the diffusion process can be represented as the cascade composition of three operators: two combination operators and a gradient descent operator. Using the Banach fixed-point theorem, we establish the existence of a unique fixed point for the composite cascade. We then study how close each agent converges towards this fixed point, and also examine how close the Pareto solution is to the fixed point. We perform a detailed mean-square error analysis and establish that all agents are able to converge to the same Pareto optimal solution within a sufficiently small mean-square-error (MSE) bound even for constant step-sizes. We illustrate one application of the theory to collaborative decision making in finance by a network of agents.