Learning-Augmented Priority Queues
This work addresses a fundamental data structure problem in computer science, but it appears incremental as it applies an existing learning-augmented framework to priority queues.
The paper tackled the design of priority queues using learning-augmented algorithms with potentially inaccurate predictions to improve worst-case performance, showing optimality and discussing applications.
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.