Counting-Based Search for Constraint Optimization Problems
Authors: Gilles Pesant
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 gilles.pesant@polymtl.ca |
| 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. |