Learning-Augmented Priority Queues

TL;DR

This paper explores how incorporating machine learning predictions into priority queue algorithms can improve their efficiency, analyzing different prediction models and demonstrating optimal solutions within the learning-augmented framework.

Contribution

It introduces a novel learning-augmented approach to priority queues, leveraging predictions to enhance performance and establishing the optimality of these methods.

Findings

Predictions can significantly improve priority queue operations.

The proposed methods are proven to be optimal.

Applications include various priority-based systems.

Abstract

Priority queues are one of the most fundamental and widely used data structures in computer science. Their primary objective is to efficiently support the insertion of new elements with assigned priorities and the extraction of the highest priority element. In this study, we investigate the design of priority queues within the learning-augmented framework, where algorithms use potentially inaccurate predictions to enhance their worst-case performance. We examine three prediction models spanning different use cases, and show how the predictions can be leveraged to enhance the performance of priority queue operations. Moreover, we demonstrate the optimality of our solution and discuss some possible applications.