Source code for the publication: Nonmyopic Global Optimisation via Approximate Dynamic Programming
doi:10.4121/45406971-2516-4306-a69f-b2ad68e6eaea.v1
The doi 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: 10.4121/45406971-2516-4306-a69f-b2ad68e6eaea
doi: 10.4121/45406971-2516-4306-a69f-b2ad68e6eaea
Datacite citation style:
Filippo Airaldi (2025): Source code for the publication: Nonmyopic Global Optimisation via Approximate Dynamic Programming. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/45406971-2516-4306-a69f-b2ad68e6eaea.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software
Source code for the implementation and simulation of nonmyopic multistep lookahead strategies for the gradient-free optimisation of black-box functions.
history
- 2025-01-08 first online, published, posted
publisher
4TU.ResearchData
format
source code (.py) and simulation results (.csv)
associated peer-reviewed publication
Nonmyopic Global Optimisation via Approximate Dynamic Programming
code hosting project url
https://github.com/FilippoAiraldi/global-optimization
funding
- CLariNet (grant code 101018826) [more info...] European Research Council
organizations
TU Delft, Faculty of Mechanical Engineering, Delft Center for Systems and Control
DATA
To access the source code, use the following command:
git clone https://data.4tu.nl/v3/datasets/5b628284-f4c5-4756-bceb-baccad8aae20.git "global-optimization"
