Automating Reformulation of Essence Specifications via Graph Rewriting

TL;DR

This paper introduces an automated system that uses graph rewriting techniques to reformulate Essence constraint models, aiming to improve their solving performance by leveraging high-level variable structures.

Contribution

The work presents a novel graph rewriting approach integrated with Essence to automatically optimize constraint models for better efficiency.

Findings

Effective reformulation improves solving performance

System successfully translates solutions back to original models

Demonstrated with a detailed case study

Abstract

Formulating an effective constraint model of a parameterised problem class is crucial to the efficiency with which instances of the class can subsequently be solved. It is difficult to know beforehand which of a set of candidate models will perform best in practice. This paper presents a system that employs graph rewriting to reformulate an input model for improved performance automatically. By situating our work in the Essence abstract constraint specification language, we can use the structure in its high level variable types to trigger rewrites directly. We implement our system via rewrite rules expressed in the Graph Programs 2 language, applied to the abstract syntax tree of an input specification. We show how to automatically translate the solution of the reformulated problem into a solution of the original problem for verification and presentation. We demonstrate the efficacy of our system with a detailed case study.