There and Back Again: On Applying Data Reduction Rules by Undoing Others

TL;DR

This paper introduces a novel approach of undoing data reduction rules to potentially obtain smaller irreducible instances of problems, demonstrated through Vertex Cover and real-world datasets.

Contribution

It proposes backward rules to undo data reduction effects, enabling further instance shrinking beyond traditional methods.

Findings

Smaller irreducible instances can be achieved by undoing data reduction rules.

The approach is effective on real-world graphs from SNAP and DIMACS datasets.

Backward rules can improve preprocessing outcomes in practice.

Abstract

Data reduction rules are an established method in the algorithmic toolbox for tackling computationally challenging problems. A data reduction rule is a polynomial-time algorithm that, given a problem instance as input, outputs an equivalent, typically smaller instance of the same problem. The application of data reduction rules during the preprocessing of problem instances allows in many cases to considerably shrink their size, or even solve them directly. Commonly, these data reduction rules are applied exhaustively and in some fixed order to obtain irreducible instances. It was often observed that by changing the order of the rules, different irreducible instances can be obtained. We propose to "undo" data reduction rules on irreducible instances, by which they become larger, and then subsequently apply data reduction rules again to shrink them. We show that this somewhat counter-intuitive approach can lead to significantly smaller irreducible instances. The process of undoing data reduction rules is not limited to "rolling back" data reduction rules applied to the instance during preprocessing. Instead, we formulate so-called backward rules, which essentially undo a data reduction rule, but without using any information about which data reduction rules were applied to it previously. In particular, based on the example of Vertex Cover we propose two methods applying backward rules to shrink the instances further. In our experiments we show that this way smaller irreducible instances consisting of real-world graphs from the SNAP and DIMACS datasets can be computed.