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DOI: 10.4121/45406971-2516-4306-a69f-b2ad68e6eaea

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

Categories

• Computer Software
• Computation Theory and Mathematics
• Engineering
• Information and Computing Sciences

Keywords

Dynamic Programming Global Optimisation Multi-step Scenario-based Optimisation Nonmyopic Acquisition Functions Rollout

Licence

MIT

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by Filippo Airaldi

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

References

• https://github.com/FilippoAiraldi/global-optimization
• https://arxiv.org/abs/2412.04882

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