Datasets for the dissertation "Leveraging Data in Algorithm Design"

DOI:10.4121/0b943091-fcc4-4acb-920a-a1080b8790b3.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/0b943091-fcc4-4acb-920a-a1080b8790b3
Datacite citation style:
Julien, Esther (2025): Datasets for the dissertation "Leveraging Data in Algorithm Design". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/0b943091-fcc4-4acb-920a-a1080b8790b3.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

The contents of this repository are the source code and datasets for each of the chapters of the dissertation by Esther Julien. This dissertation explores the use of machine-learning techniques to improve the performance of various algorithms. These algorithms solve problems appearing within the field of operations research, with a focus on bilevel optimization and adaptable robust optimization, and for the construction of phylogenetic networks. The data consists of source code for each of these projects and data for test instances and self-generated training data for the various supervised machine-learning models.

History

  • 2025-02-21 first online, published, posted

Publisher

4TU.ResearchData

Format

.py for source code/ .txt for instance data/ .csv for results/ .pkl (pickle file) for data/ .joblib for trained random forest models

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft Institute of Applied Mathematics

DATA

Files (1)