Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Hybrid 
Model Predictive Control

Zhu, Jia-Jie; Martius, Georg

doi:10.1016/j.ifacol.2020.12.1200

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Hybrid Model Predictive Control

MPS-Authors

/persons/resource/persons232844

Zhu, Jia-Jie
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons173588

Martius, Georg
Max Planck Research Group Autonomous Learning, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://doi.org/10.1016/j.ifacol.2020.12.1200
(Publisher version)

https://doi.org/10.48550/arXiv.1911.09214
(Preprint)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Zhu, J.-J., & Martius, G. (2021). Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Hybrid Model Predictive Control. IFAC-PapersOnLine, 53(2), 5239-5245. doi:10.1016/j.ifacol.2020.12.1200.

Cite as: https://hdl.handle.net/21.11116/0000-000B-2DF3-1

Abstract

There is no abstract available