9783731511809.pdf

9783731511809.pdf

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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Γλώσσα:English
Έκδοση: KIT Scientific Publishing 2022
Διαθέσιμο Online:https://doi.org/10.5445/KSP/1000144792
id oapen-20.500.12657-59833
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spelling oapen-20.500.12657-598332022-12-06T03:08:00Z Distributed Optimization with Application to Power Systems and Control Engelmann, Alexander Verteilte Optimierung Dezentrale Optimierung ALADIN ADMM Optimal Power Flow distributed optimization decentralized optimization optimal power flow bic Book Industry Communication::U Computing & information technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists 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. 2022-12-05T15:40:35Z 2022-12-05T15:40:35Z 2022 book ONIX_20221205_9783731511809_6 9783731511809 https://library.oapen.org/handle/20.500.12657/59833 eng application/pdf n/a 9783731511809.pdf https://doi.org/10.5445/KSP/1000144792 KIT Scientific Publishing KIT Scientific Publishing 10.5445/KSP/1000144792 10.5445/KSP/1000144792 44e29711-8d53-496b-85cc-3d10c9469be9 9783731511809 KIT Scientific Publishing 226 Karlsruhe open access
institution OAPEN
collection DSpace
language English
description 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.
title 9783731511809.pdf
spellingShingle 9783731511809.pdf
title_short 9783731511809.pdf
title_full 9783731511809.pdf
title_fullStr 9783731511809.pdf
title_full_unstemmed 9783731511809.pdf
title_sort 9783731511809.pdf
publisher KIT Scientific Publishing
publishDate 2022
url https://doi.org/10.5445/KSP/1000144792
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