Reasoning about Connectivity Constraints
Authors: Christian Bessiere, Emmanuel Hebrard, George Katsirelos, Toby Walsh
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report results on several benchmark problems which demonstrate the efficiency of our propagation algorithms and the promise offered by reasoning globally about connectivity. |
| Researcher Affiliation | Academia | Christian Bessiere CNRS, U. Montpellier Montpellier, France bessiere@lirmm.fr Emmanuel Hebrard CNRS, U. Toulouse Toulouse, France hebrard@laas.fr George Katsirelos INRA Toulouse, France gkatsi@gmail.com Toby Walsh NICTA & UNSW Sydney, Australia toby.walsh@nicta.com.au |
| Pseudocode | Yes | Algorithm 1: DC on CONNECTIVITY (G, S) |
| Open Source Code | Yes | We implemented all techniques with minicsp 2http://www.inra.fr/mia/T/katsirelos/minicsp.html |
| Open Datasets | No | The paper describes generating instances for experiments (e.g., "We used the generator of Gomes et al. to create 20 random instances") but does not provide concrete access information (link, DOI, specific citation) for these generated instances or any other publicly available datasets used in a way that implies public access to the specific data splits. |
| Dataset Splits | No | The paper does not specify explicit train/validation/test dataset splits, sample counts for splits, or reference standard predefined splits for machine learning model training and validation. |
| Hardware Specification | Yes | We ran experiments on a cluster of 48-core Opteron 6176 nodes at 2.3 GHz with 378 GB RAM available. |
| Software Dependencies | No | The paper mentions "minicsp" as the software used for implementation but does not provide a specific version number for it. |
| Experiment Setup | Yes | We test a hybrid CP/SAT approach... We compare four models. The connectivity and cost minimization of the corridor is either decomposed with a CONNECTIVITY and a linear inequality (connect) or modeled directly with the weighted version (weighted). Then, in both cases, we try with or without face constraints (denoted +face ). We also report results without clause learning (denoted by ). |