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...
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| Hoofdauteur: | |
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| Formaat: | Elektronisch Hoofdstuk |
| Taal: | Engels |
| Gepubliceerd in: |
Karlsruhe
KIT Scientific Publishing
2022
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| Onderwerpen: | |
| Online toegang: | OAPEN Library: download the publication OAPEN Library: description of the publication |
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| Samenvatting: | 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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| Fysieke beschrijving: | 1 electronic resource (226 p.) |
| ISBN: | KSP/1000144792 9783731511809 |
| Toegang: | Open Access |