Equitable Scheduling on a Single Machine
Authors: Klaus Heeger, Dan Hermelin, George B. Mertzios, Hendrik Molter, Rolf Niedermeier, Dvir Shabtay11818-11825
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a thorough analysis of the computational complexity of three main variants of this problem, identifying both efficient algorithms and worst-case intractability results. |
| Researcher Affiliation | Academia | 1 TU Berlin, Faculty IV, Algorithmics and Computational Complexity, Germany 2 Ben Gurion University of the Negev, Beersheba, Israel 3 Department of Computer Science, Durham University, UK |
| Pseudocode | No | The paper describes algorithms in prose and mathematical formulations, but it does not contain structured pseudocode or algorithm blocks with formal labeling like 'Algorithm 1' or 'Pseudocode'. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that source code for the described methodology is publicly available. It references "Heeger et al. 2020. Equitable Scheduling on a Single Machine. Co RR abs/2010.04643. URL https://arxiv.org/abs/2010.04643" which points to an arXiv preprint, not source code. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets. Therefore, no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, thus no training/test/validation splits are discussed. |
| Hardware Specification | No | The paper is purely theoretical, focusing on computational complexity and algorithms. It does not describe any experiments that would require specific hardware, and therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and complexity analysis. It does not mention any specific software dependencies with version numbers for reproducing experiments. |
| Experiment Setup | No | The paper is theoretical and does not involve experimental setups, hyperparameters, or system-level training settings. Therefore, no such details are provided. |