Difference Between Cyclic and Distributed Approach in Stochastic Optimization for Multi-agent System

TL;DR

This paper compares cyclic and distributed approaches in stochastic optimization for multi-agent systems, reviewing four methods and analyzing their frameworks and update rules to understand their differences.

Contribution

It provides a comparative review of four existing methods for stochastic optimization in multi-agent systems, highlighting their frameworks and update mechanisms.

Findings

Different methods are suitable for different problem frameworks.

Update rules vary significantly among the methods.

The review clarifies the strengths and limitations of cyclic versus distributed approaches.

Abstract

Many stochastic optimization problems in multi-agent systems can be decomposed into smaller subproblems or reduced decision subspaces. The cyclic and distributed approaches are two widely used strategies for solving such problems. In this manuscript, we review four existing methods for addressing these problems and compare them based on their suitable problem frameworks and update rules.