Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Further Connections Between Contract-Scheduling and Ray-Searching Problems
Authors: Spyros Angelopoulos
IJCAI 2015 | Venue PDF | 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 EMAIL |
| 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. |