Improved Results for Minimum Constraint Removal
Authors: Eduard Eiben, Jonathan Gemmell, Iyad Kanj, Andrew Youngdahl
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We describe several exact algorithms and approximation algorithms that leverage heuristics and discuss their performance in an extensive empirical simulation. In this section, we experimentally evaluate the performance of Hauser s algorithm (Hauser 2014), several exponential time algorithms and greedy approaches. |
| Researcher Affiliation | Academia | Eduard Eiben Algorithms and Complexity Group, TU Wien, Vienna, Austria & Dept. of Informatics, Univ. of Bergen, Norway eduard.eiben@uib.no Jonathan Gemmell, Iyad Kanj, Andrew Youngdahl School of Computing, De Paul University, Chicago, USA, {jgemmell,ikanj,ayoungda}@cdm.depaul.edu |
| Pseudocode | No | The paper describes algorithmic approaches but does not include any explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper mentions implementing algorithms but does not provide any statement about making the source code available or a link to a repository. |
| Open Datasets | No | The paper states: "The above algorithms were evaluated on three types of input instances: (1) instances in which the obstacles are polygons; (2) instances in which the obstacles are axes-parallel rectangles; and (3) instances in which the obstacles are line segments. Shapes were generated by selecting random points from a uniform distribution." This describes synthetic data generation, not the use or provision of a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not specify dataset splits (training, validation, testing proportions or counts) for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the names of some algorithms and search strategies but does not list specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers with their versions). |
| Experiment Setup | No | The paper describes the general implementation approaches (e.g., heuristics combined with algorithms) but does not provide specific experimental setup details such as hyperparameter values (learning rates, batch sizes), specific optimizer settings, or other system-level training configurations. |