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dc.contributor.authorEngelmann, Alexander
dc.date.accessioned2022-12-05T15:40:35Z
dc.date.available2022-12-05T15:40:35Z
dc.date.issued2022
dc.identifierONIX_20221205_9783731511809_6
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/59833
dc.description.abstractMathematical 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.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientistsen_US
dc.subject.otherVerteilte Optimierung
dc.subject.otherDezentrale Optimierung
dc.subject.otherALADIN
dc.subject.otherADMM
dc.subject.otherOptimal Power Flow
dc.subject.otherdistributed optimization
dc.subject.otherdecentralized optimization
dc.subject.otheroptimal power flow
dc.titleDistributed Optimization with Application to Power Systems and Control
dc.typebook
oapen.identifier.doi10.5445/KSP/1000144792
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9
oapen.relation.isbn9783731511809
oapen.imprintKIT Scientific Publishing
oapen.pages226
oapen.place.publicationKarlsruhe
peerreview.anonymityAll identities known
peerreview.id8ad5c235-9810-49eb-b358-27c8675324d9
peerreview.open.reviewNo
peerreview.publish.responsibilityScientific or Editorial Board
peerreview.review.stagePre-publication
peerreview.review.typeFull text
peerreview.reviewer.typeInternal editor
peerreview.reviewer.typeExternal peer reviewer
peerreview.titleDissertations (Dissertationen)


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