Balancing Scalability and Uniformity in SAT Witness Generator
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Abstract (arXiv)
Constrained-random simulation is the predominant approach used in the industry for functional verification of complex digital designs. The effectiveness of this approach depends on two key factors: the quality of constraints used to generate test vectors, and the randomness of solutions generated from a given set of constraints. In this paper, we focus on the second problem, and present an algorithm that significantly improves the state-of-the-art of (almost-)uniform generation of solutions of large Boolean constraints. Our algorithm provides strong theoretical guarantees on the uniformity of generated solutions and scales to problems involving hundreds of thousands of variables.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Mar 2014.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
5 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
- 2022Denoising Diffusion for Sampling SAT Solutionscited by 4
- 2026Generating from Discrete Distributions Using Diffusions: Insights from Random Constraint Satisfaction Problemscited by 3
- 2021Projected Model Counting: Beyond Independent Supportcited by 2
- 2026Generating from Discrete Distributions Using Diffusions: Insights from Random Constraint Satisfaction Problems21 Mar 2026
- 2024Efficient Exploration of the Rashomon Set of Rule Set Models5 Jun 2024
- 2022Denoising Diffusion for Sampling SAT Solutions30 Nov 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