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Distributed Optimization with Application to Power Systems and Control
Alexander Engelmann
2022
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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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Keywords
- ADMM
- ALADIN
- Computer science
- Computing & information technology
- decentralized optimization
- Dezentrale Optimierung
- distributed optimization
- Mathematical theory of computation
- Maths for computer scientists
- optimal power flow
- thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists
- Verteilte Optimierung
Links
DOI: 10.5445/KSP/1000144792Editions