Modelling Satisfiability Problems: Theory and Practice
Authors: Valentin Mayer-Eichberger
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that there is no single best encoding and it depends on the benchmark which encodings work best. We are able to show theoretically and empirically that this has positive effect on CNF size and consistency and improves SAT solving. We compare our SAT approach to a sophisticated CP global propagator for this constraint and are able to show it is more efficient. We also close several open instances in the CSPLIB [Smith, ]. |
| Researcher Affiliation | Academia | Valentin Mayer-Eichberger NICTA and Data61 University of New South Wales Sydney, Australia |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | Yes | We also close several open instances in the CSPLIB [Smith, ]. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings in the main text. |