Constraint acquisition needs better benchmarks
Quick Take
The paper highlights the need for better benchmarks in Constraint Acquisition (CA) to enhance reproducibility and comparability in Mathematical Programming (MP) models. It introduces MPMMine, a new benchmark suite that emphasizes consistency, standardization, and openness, providing diverse problem instances and solutions to support CA algorithms.
Key Points
- Current benchmarks are inadequate for assessing Constraint Acquisition algorithms.
- MPMMine offers a structured suite for discovering and validating MP models.
- The benchmark includes multiple models and thousands of solutions across domains.
- It utilizes open formats like MiniZinc, CommonMark, and JSON for accessibility.
- MPMMine aims to improve reproducibility and cross-study comparability in CA research.
Article Excerpt
From source RSS / original summaryarXiv:2605. 26279v1 Announce Type: new Abstract: Constraint Acquisition (CA) and related research on the validation and enhancement of Mathematical Programming (MP) models from domain knowledge artifacts are currently limited by inadequate benchmarks. This deficiency impedes reproducibility and cross-study comparability, slowing the maturation of CA methods. Existing benchmarks were designed for solver evaluation rather than for assessing CA algorithms.
They are loosely organized, treat individual problems inconsistently, and omit the domain knowledge artifacts required by CA methods. This work presents MPMMine, a benchmark suite designed to assess algorithms that discover, validate, and enhance MP models using diverse domain knowledge artifacts. MPMMine is guided by consistency, standardization, completeness, extensibility, openness, and version control. It adopts a uniform structure and relies on open formats: MiniZinc, CommonMark, and JSON.
It provides multiple models per problem, tens of instances per model, and thousands of solutions and non-solutions in both integer and continuous domains, alongside natural-language descriptions to support text-to-model methods.
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