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].
Revisiting Counting Solutions for the Global Cardinality Constraint
Authors: Giovanni Lo Bianco, Xavier Lorca, Charlotte Truchet, Gilles Pesant
JAIR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5: Experimental Analysis : E๏ฌciency of the New Upper Bound within Counting-Based Search. We generated several random CSP, with the method described above with the following parameters : n = 20, m = 20, an edge density p {0.33, 0.66, 1.0}, ฯ [0.1, 1] by steps of 0.1 and n C [2, 10] by steps of 1. For each couple (ฯ, n C), we generate 50 random instances for a total of 4500 instances. |
| Researcher Affiliation | Academia | Giovanni Lo Bianco EMAIL IMT Atlantique, 4 Rue Alfred Kastler, 44300 Nantes, France; Xavier Lorca EMAIL IMT Mines Albi, All ee des Sciences, 81000 Albi, France; Charlotte Truchet EMAIL UFR de Sciences et Techniques, 2, rue de la Houssini ere, BP 92208, 44322 NANTES CEDEX 3, France; Gilles Pesant EMAIL Polytechnique Montr eal, 2900 Boulevard Edouard-Montpetit, Montr eal, QC H3T 1J4, Canada. All listed affiliations correspond to academic institutions. |
| Pseudocode | Yes | Algorithm 1 Compute UBIP(X, ฯ) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its own source code, nor does it provide a link to a code repository for the methodology described. It only mentions using a third-party solver: "The instances and the strategies are implemented in Choco solver (Prud homme, Fages, & Lorca, 2017)". |
| Open Datasets | No | The paper describes generating its own random instances for the experiments: "We generated several random CSP, with the method described above with the following parameters : n = 20, m = 20, an edge density p {0.33, 0.66, 1.0}, ฯ [0.1, 1] by steps of 0.1 and n C [2, 10] by steps of 1. For each couple (ฯ, n C), we generate 50 random instances for a total of 4500 instances." There is no indication that these generated instances are made publicly available or that existing public datasets were used. |
| Dataset Splits | No | The paper describes generating random instances for experiments but does not mention any training/test/validation splits for these instances or any other dataset. The text states: "For each couple (ฯ, n C), we generate 50 random instances for a total of 4500 instances." |
| Hardware Specification | Yes | The instances and the strategies are implemented in Choco solver (Prud homme, Fages, & Lorca, 2017) and we set, for each resolution, the time limit to 1 min and run on a 2.2GHz Intel Core i7 with 2.048GB. |
| Software Dependencies | No | The paper mentions the use of "Choco solver (Prud homme, Fages, & Lorca, 2017)", but this citation points to "Choco Documentation" and does not provide a specific version number for the Choco solver itself. No other software dependencies with version numbers are listed. |
| Experiment Setup | Yes | We generated several random CSP, with the method described above with the following parameters : n = 20, m = 20, an edge density p {0.33, 0.66, 1.0}, ฯ [0.1, 1] by steps of 0.1 and n C [2, 10] by steps of 1. For each couple (ฯ, n C), we generate 50 random instances for a total of 4500 instances. We solve each instance with two different strategies: max SD with the PQZ bound and max SD with the corrected bound. ... and we set, for each resolution, the time limit to 1 min. |