Fully Online Matching with Stochastic Arrivals and Departures
Authors: Zihao Li, Hao Wang, Zhenzhen Yan
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive numerical studies to evaluate the performance of our algorithms. |
| Researcher Affiliation | Academia | Zihao Li, Hao Wang, Zhenzhen Yan Nanyang Technological University zihao004@e.ntu.edu.sg, hao wang@ntu.edu.sg, yanzz@ntu.edu.sg |
| Pseudocode | Yes | Algorithm 1: SAM(γ) Input: Online arrivals of agents Parameter: Scaling parameter γ (0, 1] |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the described methodology or state that the code will be made publicly available. It only mentions using Gurobi as a solver. |
| Open Datasets | No | The paper describes generating synthetic datasets for experiments but does not provide access information (link, citation, or repository) for these generated datasets to be publicly available. |
| Dataset Splits | No | The paper discusses evaluating performance over "realized sequences" and states "For each parameter setting, we test |R| = 50 realized sequences." However, it does not specify traditional train/validation/test dataset splits, which are not directly applicable to its online problem setup. |
| Hardware Specification | Yes | We use a computer with 2.2 GHz Intel Core i7 processor, 16 GB 1600 MHz DDR3 memory and Intel Iris Pro 1536 MB Graphics to run all the experiments. |
| Software Dependencies | No | The paper mentions "We use Gurobi (Gurobi Optimization 2022) as our solver." While Gurobi is a specific software, it provides the year of the reference (2022) but not a numerical version number (e.g., Gurobi 9.5). |
| Experiment Setup | Yes | The parameters are q = 2.5 and geometric distribution with P G = 0.5. We set T = 3000 which is much larger than the sojourn time of any vertex under any tested distribution. |