Further Connections Between Contract-Scheduling and Ray-Searching Problems
Authors: Spyros Angelopoulos
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thus, we must resort to numerical methods. Figure 1 illustrates the performance of the randomized strategy β r(n) versus the deterministic optimal strategy, denoted by β (n). We observe that β r(n) 0.6β (n), for n = 1, . . . 80. |
| Researcher Affiliation | Academia | Spyros Angelopoulos Sorbonne Universit es, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France and CNRS, UMR 7606, LIP6, F-75005, Paris, France spyros.angelopoulos@lip6.fr |
| Pseudocode | No | The paper describes algorithms and strategies using prose and mathematical expressions, but it does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper refers to an arXiv preprint for the full version ('[Angelopoulos, 2015] S. Angelopoulos. Further connections between contract-scheduling and ray-searching problems. arxiv:1504.07168 [cs:AI], 2015.'), but it does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper focuses on theoretical analysis and optimization problems, using abstract problem definitions (e.g., 'm semi-infinite, concurrent rays') rather than specific, named public datasets for training. |
| Dataset Splits | No | The paper is theoretical in nature and does not describe experiments that would involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for any computations or numerical evaluations. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or version numbers used for its analysis or numerical evaluations. |
| Experiment Setup | No | The paper primarily presents theoretical analysis and does not detail an experimental setup with specific hyperparameters or system-level training settings. |