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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Counting-Based Search for Constraint Optimization Problems
Authors: Gilles Pesant
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents an empirical evaluation of the search guidance efficiency of a branching heuristic built from our cost-based solution densities on three benchmark problems, one for each of the optimization constraints considered in the previous section. |
| Researcher Affiliation | Academia | Gilles Pesant Ecole Polytechnique de Montr eal, Montreal, Canada CIRRELT, Universit e de Montr eal, Montreal, Canada EMAIL |
| Pseudocode | Yes | Algorithm 1 describes how we compute cost-based solution densities from them. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Balanced Academic Curriculum Problem (BACP, problem 30 of the CSPlib) ... 21 small symmetric and asymmetric instances from TSPlib ... 20 instances from (Bofill et al. 2015) |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | Yes | All experiments were run on Dual core AMD 2.1 GHz processors with 8 GB of RAM |
| Software Dependencies | Yes | using IBM ILOG Solver 6.7 as the CP solver |
| Experiment Setup | Yes | Each experiment uses depth-first search and compares max SD (with ϵ = 0.1) to standard generic branching heuristics, namely smallest-domain first with lexicographic value selection (dom) and the solver s default impact-based search (IBS), and to some tailored heuristic when applicable. ... each instance was given a two-hour time limit. |