Distributed Optimization with Application to Power Systems and Control
Mathematical optimization techniques are among the most successful tools for controlling technical systems optimally with feasibility guarantees. Yet, they are often centralized-all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization ove...
I tiakina i:
| Kaituhi matua: | |
|---|---|
| Hōputu: | Tāhiko Wāhanga pukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
Karlsruhe
KIT Scientific Publishing
2022
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| Ngā marau: | |
| Urunga tuihono: | OAPEN Library: download the publication OAPEN Library: description of the publication |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| Whakarāpopototanga: | Mathematical optimization techniques are among the most successful tools for controlling technical systems optimally with feasibility guarantees. Yet, they are often centralized-all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization overcome this limitation. Classical approaches, however, are often not applicable due to non-convexities. This work develops one of the first frameworks for distributed non-convex optimization. |
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| Whakaahuatanga ōkiko: | 1 electronic resource (226 p.) |
| ISBN: | KSP/1000144792 9783731511809 |
| Urunga: | Open Access |