The Configurable SAT Solver Challenge (CSSC)
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Abstract (arXiv)
It is well known that different solution strategies work well for different types of instances of hard combinatorial problems. As a consequence, most solvers for the propositional satisfiability problem (SAT) expose parameters that allow them to be customized to a particular family of instances. In the international SAT competition series, these parameters are ignored: solvers are run using a single default parameter setting (supplied by the authors) for all benchmark instances in a given track. While this competition format rewards solvers with robust default settings, it does not reflect the situation faced by a practitioner who only cares about performance on one particular application and can invest some time into tuning solver parameters for this application. The new Configurable SAT Solver Competition (CSSC) compares solvers in this latter setting, scoring each solver by the performance it achieved after a fully automated configuration step. This article describes the CSSC in more detail, and reports the results obtained in its two instantiations so far, CSSC 2013 and 2014.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 May 2015.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 3; 2024: 1; 2025: 1.
9 papers.
- 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 229
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- 2023Self-Correcting Bayesian Optimization through Bayesian Active Learningcited by 22
- 2021Reproducibility in Evolutionary Computationcited by 12
- 2022On the Configuration of More and Less Expressive Logic Programscited by 1
- 2023Competitions in AI -- Robustly Ranking Solvers Using Statistical Resamplingcited by 1
- 2024Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networkscited by 1
- 2023Can LLMs Configure Software Toolscited by 0
- 2025Optimization Problem Solving Can Transition to Evolutionary Agentic Workflowscited by 0
- 2025Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows7 May 2025
- 2024Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks14 Jun 2024
- 2023Can LLMs Configure Software Tools11 Dec 2023
- 2023Competitions in AI -- Robustly Ranking Solvers Using Statistical Resampling9 Aug 2023
- 2023Self-Correcting Bayesian Optimization through Bayesian Active Learning21 Apr 2023
- 2022On the Configuration of More and Less Expressive Logic Programs2 Mar 2022
- 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization20 Sep 2021
- 2021DACBench: A Benchmark Library for Dynamic Algorithm Configuration18 May 2021
- 2021Reproducibility in Evolutionary Computation5 Feb 2021