Implementing Efficient All Solutions SAT Solvers
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Abstract (arXiv)
All solutions SAT (AllSAT for short) is a variant of propositional satisfiability problem. Despite its significance, AllSAT has been relatively unexplored compared to other variants. We thus survey and discuss major techniques of AllSAT solvers. We faithfully implement them and conduct comprehensive experiments using a large number of instances and various types of solvers including one of the few public softwares. The experiments reveal solver's characteristics. Our implemented solvers are made publicly available so that other researchers can easily develop their solver by modifying our codes and compare it with existing methods.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Oct 2015.
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Citing papers per year: 2022: 4; 2023: 1; 2024: 0; 2025: 1.
6 papers.
- 2022Semantic Probabilistic Layers for Neuro-Symbolic Learningcited by 103
- 2022Neuro-Symbolic Entropy Regularizationcited by 27
- 2022NSNet: A General Neural Probabilistic Framework for Satisfiability Problemscited by 18
- 2022DeepSAT: An EDA-Driven Learning Framework for SATcited by 11
- 2023Solving Projected Model Counting by Utilizing Treewidth and its Limitscited by 1
- 2025Model Enumeration of Two-Variable Logic with Quadratic Delay Complexitycited by 1
- 2025Model Enumeration of Two-Variable Logic with Quadratic Delay Complexity26 May 2025
- 2023Solving Projected Model Counting by Utilizing Treewidth and its Limits30 May 2023
- 2022NSNet: A General Neural Probabilistic Framework for Satisfiability Problems7 Nov 2022
- 2022Semantic Probabilistic Layers for Neuro-Symbolic Learning1 Jun 2022
- 2022DeepSAT: An EDA-Driven Learning Framework for SAT27 May 2022
- 2022Neuro-Symbolic Entropy Regularization25 Jan 2022