Minimalistic Predictions to Schedule Jobs with Online Precedence Constraints
Authors: Alexandra Anna Lassota, Alexander Lindermayr, Nicole Megow, Jens Schlöter
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present lower bounds and algorithmic upper bounds for different precedence topologies, and thereby give a structured overview on which and how additional (possibly erroneous) information helps for designing better algorithms. Along the way, we also improve bounds on traditional competitive ratios for existing algorithms. |
| Researcher Affiliation | Academia | 1Institute of Mathematics, EPFL, Lausanne, Switzerland 2Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany. |
| Pseudocode | Yes | Algorithm 1 Weighted Round Robin on Chains; Algorithm 2 Adaptive weight order algorithm; Algorithm 3 Learning-augmented WRR on Chains; Algorithm 4 Weighted Round Robin on out-forests; Algorithm 5 WDEQ on Chains; Algorithm 6 Adaptive weight order algorithm for parallel identical machines |
| Open Source Code | No | The paper does not provide any statements or links regarding the public availability of its source code. |
| Open Datasets | No | This paper is theoretical and does not mention the use of any datasets for training or public dataset availability. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical validation with datasets, thus no dataset split information is provided. |
| Hardware Specification | No | This paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | This paper is theoretical and does not mention specific software dependencies or their version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe any experimental setup details such as hyperparameters or system-level training settings. |