01797nam 2200409z- 450 991063408260332120231214133600.01000144792(CKB)5840000000218142(oapen)https://directory.doabooks.org/handle/20.500.12854/94466(EXLCZ)99584000000021814220202212d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierDistributed Optimization with Application to Power Systems and ControlKarlsruheKIT Scientific Publishing20221 electronic resource (226 p.)3-7315-1180-0 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.Maths for computer scientistsbicsscVerteilte OptimierungDezentrale OptimierungALADINADMMOptimal Power Flowdistributed optimizationdecentralized optimizationoptimal power flowMaths for computer scientistsEngelmann Alexanderauth1285816BOOK9910634082603321Distributed Optimization with Application to Power Systems and Control3019660UNINA