Resource Sharing through Multi-Round Matchings
Authors: Yohai Trabelsi, Abhijin Adiga, Sarit Kraus, S. S. Ravi, Daniel J. Rosenkrantz
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate our algorithms on synthetic networks and apply them to two real-world situations: shared office spaces and matching courses to classrooms. |
| Researcher Affiliation | Academia | Yohai Trabelsi1, Abhijin Adiga2, Sarit Kraus1, S. S. Ravi2, 3, Daniel J. Rosenkrantz2, 3 1 Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel 2 Biocomplexity Institute and Initiative, Univ. of Virginia, Charlottesville, VA, USA 3 Dept. of Computer Science, University at Albany SUNY, Albany, NY, USA |
| Pseudocode | Yes | Algorithm 1: ALG-MAXTB-MRM |
| Open Source Code | Yes | The data and the code for running the experiments are available at https://github.com/yohayt/Resource-Sharing-Through-Multi-Round-Matchings. |
| Open Datasets | Yes | The data and the code for running the experiments are available at https://github.com/yohayt/Resource-Sharing-Through-Multi-Round-Matchings. |
| Dataset Splits | No | The paper mentions data generation and replication but does not specify explicit train/validation/test dataset splits for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions solution approaches like an Integer Linear Program (ILP) but does not provide specific version numbers for software dependencies or solvers. |
| Experiment Setup | No | The paper describes the general experimental settings and scenarios (e.g., number of rounds, preference thresholds) but does not provide specific hyperparameters or detailed system-level training configurations. |