A Branch-and-Price Algorithm for Scheduling Observations on a Telescope
Authors: Nicolas Catusse, Hadrien Cambazard, Nadia Brauner, Pierre Lemaire, Bernard Penz, Anne-Marie Lagrange, Pascal Rubini
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real and realistic datasets show that the method provides optimal solutions very efficiently. |
| Researcher Affiliation | Academia | Nicolas Catusse, Hadrien Cambazard, Nadia Brauner, Pierre Lemaire, and Bernard Penz Univ. Grenoble Alpes, G-SCOP, F-38000 Grenoble, France CNRS, G-SCOP, F-38000 Grenoble, France Anne-Marie Lagrange and Pascal Rubini Univ. Grenoble Alpes, IPAG, F-38000 Grenoble, France CNRS, IPAG, F-38000 Grenoble, France |
| Pseudocode | Yes | Algorithm 1 returns whether the set S of targets can be scheduled in night j Require: Targets S sorted by non-decreasing meridians 1: ctime 0 2: for each target i 2 S do 3: ctime max(ctime, rj i 4: if ctime > dj i then 5: return false; 6: return true |
| Open Source Code | No | The paper does not provide any concrete access to source code (no repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | Since we only have one real dataset, we generated realistic random datasets. |
| Dataset Splits | No | The paper mentions using 'real and realistic datasets' but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | Yes | The experiments were performed on an Intel Xeon E52440 v2 @ 1.9 GHz processor and 32 GB of RAM, and ran with a memory limit of 4 GB and a time limit of 2 hours. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the general experimental environment (hardware, datasets) and evaluation metrics, but does not provide specific experimental setup details such as hyperparameter values or training configurations for the algorithms. |