A Scalable and Nearly Uniform Generator of SAT Witnesses
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Abstract (arXiv)
Functional verification constitutes one of the most challenging tasks in the development of modern hardware systems, and simulation-based verification techniques dominate the functional verification landscape. A dominant paradigm in simulation-based verification is directed random testing, where a model of the system is simulated with a set of random test stimuli that are uniformly or near-uniformly distributed over the space of all stimuli satisfying a given set of constraints. Uniform or near-uniform generation of solutions for large constraint sets is therefore a problem of theoretical and practical interest. For Boolean constraints, prior work offered heuristic approaches with no guarantee of performance, and theoretical approaches with proven guarantees, but poor performance in practice. We offer here a new approach with theoretical performance guarantees and demonstrate its practical utility on large constraint sets.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Apr 2013.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 1; 2025: 0; 2026: 2.
10 papers.
- 2024Efficient Exploration of the Rashomon Set of Rule Set Modelscited by 10
- 2021Arjun: An Efficient Independent Support Computation Technique and its Applications to Counting and Samplingcited by 9
- 2023Lifted Algorithms for Symmetric Weighted First-Order Model Samplingcited by 6
- 2023On Exact Sampling in the Two-Variable Fragment of First-Order Logiccited by 3
- 2021Projected Model Counting: Beyond Independent Supportcited by 2
- 2022CCDD: A Tractable Representation for Model Counting and Uniform Samplingcited by 1
- 2023Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration With Provable Guaranteescited by 1
- 2022Learning Markov Random Fields for Combinatorial Structures via Sampling through Lov'asz Local Lemmacited by 0
- 2026Fast Rerandomization for Balancing Covariates in Randomized Experiments: A Metropolis-Hastings Frameworkcited by 0
- 2026Enhancing Query Efficiency for d-DNNF Representations Through Preprocessingcited by 0
- 2026Enhancing Query Efficiency for d-DNNF Representations Through Preprocessing13 Jul 2026
- 2026Fast Rerandomization for Balancing Covariates in Randomized Experiments: A Metropolis-Hastings Framework7 Feb 2026
- 2024Efficient Exploration of the Rashomon Set of Rule Set Models5 Jun 2024
- 2023Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration With Provable Guarantees16 Sep 2023
- 2023Lifted Algorithms for Symmetric Weighted First-Order Model Sampling17 Aug 2023
- 2023On Exact Sampling in the Two-Variable Fragment of First-Order Logic6 Feb 2023
- 2022Learning Markov Random Fields for Combinatorial Structures via Sampling through Lov'asz Local Lemma1 Dec 2022
- 2022CCDD: A Tractable Representation for Model Counting and Uniform Sampling21 Feb 2022
- 2021Projected Model Counting: Beyond Independent Support18 Oct 2021
- 2021Arjun: An Efficient Independent Support Computation Technique and its Applications to Counting and Sampling18 Oct 2021