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].
Efficient Operations On MDDs for Building Constraint Programming Models
Authors: Guillaume Perez, Jean-Charles RĆ©gin
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our approach to the regular constraint and obtain competitive results with dedicated algorithms. We also experiment our technique on a large scale problem: the phrase generation problem and we show that our approach gives equivalent results to those of a speciļ¬c algorithm computing a complex automaton. Before concluding, we present some experiments showing some strong improvements brought by our algorithms. |
| Researcher Affiliation | Academia | Guillaume Perez and Jean-Charles RƩgin UniversitƩ Nice-Sophia Antipolis, I3S UMR 7271, CNRS, France EMAIL; EMAIL |
| Pseudocode | Yes | Algorithm 1 p Reduce of an MDD., Algorithm 2 Complementary Operation., Algorithm 3 Generic Apply Function. |
| Open Source Code | No | The paper states 'The algorithms have been implemented on the top of ortools solver from Google [Perron, 2013] version 3158.', but does not provide a link or explicit statement about the availability of their own source code. |
| Open Datasets | Yes | We select some problems from the Solver Competition archive [Lecoutre, 2009]. |
| Dataset Splits | No | The paper does not explicitly state any training/validation/test dataset splits. It only mentions test sets or results on problems. |
| Hardware Specification | Yes | The experiments have been made on a 6 cores server (Intel 3930) having 64GB of memory and running under Windows 7. |
| Software Dependencies | Yes | The algorithms have been implemented on the top of ortools solver from Google [Perron, 2013] version 3158. |
| Experiment Setup | Yes | More precisely, ļ¬rst we deļ¬ne mdd4 the MDD containing all the sequences of 4 words from the contracted corpus...Then, we repeatedly deļ¬ne mdda for each sequence of 4 variables in the ordered set: x1, ...x20. That is we deļ¬ne 16 MDDs. Next, we successively intersect the MDDs. This means that we intersect the MDD deļ¬ned on x1, .., xi with the MDD deļ¬ned on xi 2, ..., xi+1 for obtaining the MDD deļ¬ned on x1, .., xi+1. For intersecting a pair of MDDs deļ¬ned on different variables we modify them by adding variables accepting all the possibles values. More precisely, the MDD deļ¬ned on x1, .., xi is transformed into the MDD deļ¬ned on x1, .., xi+1 where each values of xi+1 is compatible with any path of the ļ¬rst MDD. This corresponds to a duplication of the last layer. |