Resource allocation in open multi-agent systems: an online optimization analysis

TL;DR

This paper analyzes the performance of the Random Coordinate Descent algorithm for resource allocation in open multi-agent systems, focusing on online optimization challenges due to agent replacements and budget variations.

Contribution

It provides an online optimization analysis of RCD in open multi-agent systems, deriving bounds on accumulated errors caused by agent replacements.

Findings

Derived bounds on accumulated errors in RCD performance

Analyzed impact of agent replacements on resource allocation

Compared RCD solutions with optimal and selfish strategies

Abstract

The resource allocation problem consists of the optimal distribution of a budget between agents in a group. We consider such a problem in the context of open systems, where agents can be replaced at some time instances. These replacements lead to variations in both the budget and the total cost function that hinder the overall network's performance. For a simple setting, we analyze the performance of the Random Coordinate Descent algorithm (RCD) using tools similar to those commonly used in online optimization. In particular, we study the accumulated errors that compare solutions issued from the RCD algorithm and the optimal solution or the non-collaborating selfish strategy and we derive some bounds in expectation for these accumulated errors.