Boosting SBDS for Partial Symmetry Breaking in Constraint Programming
Authors: Jimmy Lee, Zichen Zhu
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimentations confirm the efficiency of Re SBDS, when compared against state of the art methods. We perform extensive experimentation on benchmarks of different natures and compare against state of the art static and dynamic methods. Results confirm the feasibility and competitiveness of our proposal. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering The Chinese University of Hong Kong |
| Pseudocode | No | The paper describes the steps of Recursive SBDS in prose with numbered points, but it does not present them in a structured pseudocode block or algorithm format. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | The ECCLD problem is prob036 in CSPLib (Gent and Walsh 1999). |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. The problems addressed (e.g., N-Queens, ECCLD) are constraint satisfaction problems where the goal is often to find all solutions or an optimal solution, rather than using traditional ML-style data splits. |
| Hardware Specification | Yes | All experiments are conducted using Gecode Solver 4.2.0 on Xeon E5620 2.4GHz processors. |
| Software Dependencies | Yes | All experiments are conducted using Gecode Solver 4.2.0 |
| Experiment Setup | Yes | Unless otherwise specified, the search order is defaulted to input variable order and minimum value order. For Re SBDS and LDSB, the variable ordering heuristic chooses the variable with the most constraints and breaks ties by the size of the partition containing the variables. |