Recent Advances in Practical Data Reduction

TL;DR

This paper surveys recent practical data reduction techniques for graph problems, highlighting the gap between theoretical advances and real-world applications, and discusses future directions and open challenges.

Contribution

It provides a comprehensive overview of recent data reduction methods in practice and suggests concrete techniques for implementation and future research.

Findings

Data reduction techniques improve efficiency in graph algorithms

Practical methods lag behind theoretical developments

Open problems in data reduction remain unsolved

Abstract

Over the last two decades, significant advances have been made in the design and analysis of fixed-parameter algorithms for a wide variety of graph-theoretic problems. This has resulted in an algorithmic toolbox that is by now well-established. However, these theoretical algorithmic ideas have received very little attention from the practical perspective. We survey recent trends in data reduction engineering results for selected problems. Moreover, we describe concrete techniques that may be useful for future implementations in the area and give open problems and research questions.