Imitation learning model and datasets: "A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIP"
DOI:10.4121/14054330.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: 10.4121/14054330
DOI: 10.4121/14054330
Datacite citation style:
Yilmaz, Kaan; Yorke-Smith, Neil (2021): Imitation learning model and datasets: "A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIP". Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/14054330.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software
Usage statistics
997
views
2
citations
473
downloads
Geolocation
Delft, The Netherlands
Licence CC BY-NC-SA 4.0
Imitation learning model and datasets corresponding to the AI article "A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIP".
History
- 2021-05-18 first online, published, posted
Publisher
4TU.ResearchDataAssociated peer-reviewed publication
A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIPFunding
- Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization (grant code 952215) [more info...] European Commission
- Dutch Research Council Groot project OPTIMAL
Organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer ScienceDATA
Files (2)
- 108,417 bytesMD5:
b4d813be60da61a08281f9848cda9f38
code.zip - 374,253,631 bytesMD5:
4e987faca1d2bd7e341d0a5e8f9e4b75
data.zip -
download all files (zip)
374,362,048 bytes unzipped