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].
Finding Diverse Solutions of High Quality to Constraint Optimization Problems
Authors: Thierry Petit, Andrew C. Trapp
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Experiments |
| Researcher Affiliation | Academia | Thierry Petit Foisie School of Business, Worcester Polytechnic Institute, USA Mines de Nantes / LINA, France tpetit@{wpi.edu,mines-nantes.fr} Andrew C. Trapp Foisie School of Business, Worcester Polytechnic Institute, USA EMAIL |
| Pseudocode | Yes | Algorithm 1: k BESTOPT(N, k, xδ, x Q): Solutions set S |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We implemented the chords musical benchmark [Truchet and Codognet, 2004; Petit, 2012]. We used a graph-variable model [Fages and Lorca, 2012] for solving TSPLIB symmetric instances [Reinelt, 1991], which are state-of-the-art routing benchmarks. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | We used an Intel Xeon 2.27GHz machine and the Choco 3.2.1 solver. |
| Software Dependencies | Yes | We used an Intel Xeon 2.27GHz machine and the Choco 3.2.1 solver. |
| Experiment Setup | Yes | We systematically re-use the search strategy of the original model (e.g., DOM/WDEG [Boussemart et al., 2004]), and then assign the new variables in static order. For each instance, we give average results obtained with 20 randomly generated cost matrices, and we generate 20 solutions per matrix and ratio, with a time-limit of 15 seconds for each new solution. Table 2 shows the results for 20 solutions per generated set, with a 5 minute time-limit, using NS. |