01812nam 2200421z- 450 9910634082603321202212061000144792(CKB)5840000000218142(oapen)https://directory.doabooks.org/handle/20.500.12854/94466(oapen)doab94466(EXLCZ)99584000000021814220202212d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierDistributed Optimization with Application to Power Systems and ControlKarlsruheKIT Scientific Publishing20221 online 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 scientistsbicsscADMMALADINdecentralized optimizationDezentrale Optimierungdistributed optimizationoptimal power flowOptimal Power FlowVerteilte OptimierungMaths for computer scientistsEngelmann Alexanderauth1285816BOOK9910634082603321Distributed Optimization with Application to Power Systems and Control3019660UNINA