Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Strong Bounds Consistencies and Their Application to Linear Constraints
Authors: Christian Bessiere, Anastasia Paparrizou, Kostas Stergiou
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show large differences in favor of our approaches. and Finally, we make an experimental study on various types of problems. Results demonstrate that both of our techniques can outperform BC, being exponentially better in many cases. |
| Researcher Affiliation | Academia | Christian Bessiere CNRS, University of Montpellier Montpellier, France EMAIL Anastasia Paparrizou CNRS, University of Montpellier Montpellier, France EMAIL Kostas Stergiou University of Western Macedonia Kozani, Greece EMAIL |
| Pseudocode | Yes | Algorithm 1: r BC2-A |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We ran experiments on randomly generated problems. All instances have 30 variables, domains being the interval [0..5], and constraints of arity 6 and 9. We compare the different techniques on large real instances from Web Services. Their problems have n = 100 to 1500 variables, domains size from 0 to 255, and constraints of arity k = 3 to 20. The example of 15-puzzle was presented by Malte Helmert in his ECAI 2014 invited talk. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, counts, or explicit train/validation/test splits). |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or memory) are provided for running the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned (e.g., 'implemented in Python' or 'used library X' without version). |
| Experiment Setup | Yes | All instances have 30 variables, domains being the interval [0..5], and constraints of arity 6 and 9. The parameter b belongs to a randomly selected value in [10..20] and [15..30] for arities 6 and 9 respectively. ... We solved 30 instances per class, with a cutoff limit of 1800 seconds. |