A CP-Based Approach for Popular Matching
Authors: Danuta Chisca, Mohamed Siala, Gilles Simonin, Barry O'Sullivan
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a constraint programming approach to the popular matching problem. In this paper we study this problem and propose the first CP formulation of it. We consider two cases of the problem of popular matching instances with and without ties in the preference lists and show that one can elegantly encode these problems using the global cardinality global constraint (R egin 1996). Also, the presence of Lemma 1, Theorem 1, Lemma 2, Theorem 2, and [Sketch] Proof. |
| Researcher Affiliation | Academia | Danuta Sorina Chisca, Mohamed Siala, Gilles Simonin, Barry O Sullivan Insight Centre for Data Analytics, Department of Computer Science, University College Cork, Ireland {sorina.chisca|mohamed.siala|gilles.simonin|barry.osullivan}@insight-centre.org |
| Pseudocode | No | The paper describes procedural steps in paragraph form, but does not contain a clearly labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | No | The paper does not contain any statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with datasets, thus there is no information about public datasets used. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments or data splits for validation or training. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments or hardware specifications. |
| Software Dependencies | No | The paper mentions "Constraint programming (CP)" and references "global cardinality constraint (gcc) (R egin 1996)" but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |