Verifying Integer Programming Results
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Abstract (arXiv)
Software for mixed-integer linear programming can return incorrect results for a number of reasons, one being the use of inexact floating-point arithmetic. Even solvers that employ exact arithmetic may suffer from programming or algorithmic errors, motivating the desire for a way to produce independently verifiable certificates of claimed results. Due to the complex nature of state-of-the-art MILP solution algorithms, the ideal form of such a certificate is not entirely clear. This paper proposes such a certificate format, illustrating its capabilities and structure through examples. The certificate format is designed with simplicity in mind and is composed of a list of statements that can be sequentially verified using a limited number of simple yet powerful inference rules. We present a supplementary verification tool for compressing and checking these certificates independently of how they were created. We report computational results on a selection of mixed-integer linear programming instances from the literature. To this end, we have extended the exact rational version of the MIP solver SCIP to produce such certificates.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Nov 2016.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 2.
5 papers.
- 2022Certified Symmetry and Dominance Breaking for Combinatorial Optimisationcited by 13
- 2025Exact Verification of Graph Neural Networks with Incremental Constraint Solvingcited by 4
- 2024How To Discover Short, Shorter, and the Shortest Proofs of Unsatisfiability: A Branch-and-Bound Approach for Resolution Proof Length Minimizationcited by 0
- 2026ModelEquivBench: Certifying Multi-Relational Evaluation of LLM-Generated Optimization Modelscited by 0
- 2026The Refutation Gap: Certifying Both Halves of an Optimality Claimcited by 0
- 2026The Refutation Gap: Certifying Both Halves of an Optimality Claim16 Sep 2026
- 2026ModelEquivBench: Certifying Multi-Relational Evaluation of LLM-Generated Optimization Models31 Jul 2026
- 2025Exact Verification of Graph Neural Networks with Incremental Constraint Solving12 Aug 2025
- 2024How To Discover Short, Shorter, and the Shortest Proofs of Unsatisfiability: A Branch-and-Bound Approach for Resolution Proof Length Minimization12 Nov 2024
- 2022Certified Symmetry and Dominance Breaking for Combinatorial Optimisation23 Mar 2022