๐งฉ Model RB Benchmark Suite
Benchmarks with Hidden Optimum Solutions for
SAT, CSP, Graph Problems, Set Covering, Set Packing, and Winner Determination
๐ Overview
This repository hosts a curated collection of benchmark instances based on Model RB, a random model with exact phase transitions and provable hardness properties.
The benchmarks are designed with hidden optimum solutions, making them particularly suitable for evaluating both complete and incomplete algorithms on hard instances.
The benchmark families included here cover a broad range of classical NP-hard problems, including:
- Boolean Satisfiability (SAT)
- Constraint Satisfaction Problems (CSP)
- Maximum Independent Set (MIS)
- Minimum Vertex Cover (MVC)
- Maximum Clique and Vertex Coloring
- Minimum Set Covering (MSC)
- Maximum Set Packing (MSP)
- Winner Determination in Combinatorial Auctions
- Pseudo-Boolean Optimization
- Weighted Max-2-SAT
All benchmark constructions are derived from Model RB, whose theoretical properties have been extensively studied in the literature.
๐งช Benchmark Collections
The benchmark suite is organized into the following categories:
-
Forced Satisfiable CSP and SAT Benchmarks
Hard satisfiable instances generated at the exact phase transition of Model RB. -
Graph Benchmarks with Hidden Optimum Solutions (BHOSLIB)
Benchmarks for MIS, MVC, Maximum Clique, and Vertex Coloring, obtained via transformations from Model RB SAT instances. -
Set Covering, Set Packing, and Winner Determination Benchmarks
Instances transformed from graph benchmarks, with hidden optimal solutions preserved. -
Pseudo-Boolean (0โ1 Integer Programming) Benchmarks
Instances with known optimal solutions derived from Model RB constructions. -
Weighted Max-2-SAT Benchmarks
Weighted SAT instances with hidden optima for evaluating Max-SAT solvers.
Detailed descriptions, theoretical background, and downloadable instances for each category are provided on the documentation website.
๐ฐ News
Historical experimental results, solver performance records, and benchmark usage statistics are preserved in the documentation, including:
- SAT Competition and CSP Solver Competition usage
- Long-term hardness records on large graph instances
- Updates reported by researchers using these benchmarks
โฌ๏ธ Downloads
All benchmark instances are provided as compressed archives (.tar.gz, .gz) and are available through the documentation pages.
- SAT / CSP benchmarks in DIMACS CNF and CSP formats
- Graph benchmarks in ASCII DIMACS and binary DIMACS formats
- Set covering and packing benchmarks in a DIMACS-style set format
Benchmark files are stored under
docs/assets/bench/and linked directly from the documentation pages.
๐ Documentation
The full documentation is built with GitHub Pages and organized as a multi-page site:
๐ Documentation site:
https://RB-bench.github.io/
The documentation includes:
- Formal problem definitions
- Benchmark generation models
- File format descriptions
- Download links
- Lists of papers that use each benchmark family
๐ References
Selected Papers about Model RB
-
Ke Xu and Wei Li.
Exact Phase Transitions in Random Constraint Satisfaction Problems.
Journal of Artificial Intelligence Research, 12 (2000): 93โ103. -
Ke Xu and Wei Li.
Many Hard Examples in Exact Phase Transitions.
Theoretical Computer Science, 355 (2006): 291โ302.
Earlier version: CoRR Report cs.CC/0302001, Feb. 2003. -
Ke Xu, Frederic Boussemart, Fred Hemery and Christophe Lecoutre.
Random Constraint Satisfaction: Easy Generation of Hard (Satisfiable) Instances.
Artificial Intelligence, 171 (2007): 514โ534.
Earlier version appeared in Proc. of the 19th IJCAI, pp. 337โ342, Scotland, 2005. -
Ke Xu and Guangyan Zhou.
SAT Requires Exhaustive Search.
Frontiers of Computer Science, Volume 19, Article 1912405, 2025. -
Ke Xu and Guangyan Zhou.
Self-reference as key to proving extreme hardness โ Reply to the comment by Allender and Williams.
๐ฌ Contact
For questions, comments, or requests for additional benchmark instances, please contact: kexu AT buaa.edu.cn
This repository serves as a long-term, stable home for Model RB benchmark instances and related documentation.