Proteus: A Hierarchical Portfolio of Solvers and Transformations
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Abstract (arXiv)
In recent years, portfolio approaches to solving SAT problems and CSPs have become increasingly common. There are also a number of different encodings for representing CSPs as SAT instances. In this paper, we leverage advances in both SAT and CSP solving to present a novel hierarchical portfolio-based approach to CSP solving, which we call Proteus, that does not rely purely on CSP solvers. Instead, it may decide that it is best to encode a CSP problem instance into SAT, selecting an appropriate encoding and a corresponding SAT solver. Our experimental evaluation used an instance of Proteus that involved four CSP solvers, three SAT encodings, and six SAT solvers, evaluated on the most challenging problem instances from the CSP solver competitions, involving global and intensional constraints. We show that significant performance improvements can be achieved by Proteus obtained by exploiting alternative view-points and solvers for combinatorial problem-solving.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2013.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 2.
4 papers.
- 2023Learning to Select SAT Encodings for Pseudo-Boolean and Linear Integer Constraintscited by 2
- 2024Automatic Feature Learning for Essence: a Case Study on Car Sequencingcited by 0
- 2026Learning Splitting Heuristics for Parallel String Solverscited by 0
- 2026Partition Scores Are Not System Scores: Deployment-Fidelity Gaps in Decomposed Algorithm Selectioncited by 0
- 2026Partition Scores Are Not System Scores: Deployment-Fidelity Gaps in Decomposed Algorithm Selection12 Sep 2026
- 2026Learning Splitting Heuristics for Parallel String Solvers9 Jun 2026
- 2024Automatic Feature Learning for Essence: a Case Study on Car Sequencing23 Sep 2024
- 2023Learning to Select SAT Encodings for Pseudo-Boolean and Linear Integer Constraints18 Jul 2023