LEADER 01812nam  2200421z- 450 
001 9910634082603321
005 20221206
010   $a1000144792
035   $a(CKB)5840000000218142
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/94466
035   $a(oapen)doab94466
035   $a(EXLCZ)995840000000218142
100   $a20202212d2022      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aDistributed Optimization with Application to Power Systems and Control
210   $aKarlsruhe$cKIT Scientific Publishing$d2022
215   $a1 online resource (226 p.)
311 08$a3-7315-1180-0 
330   $aMathematical 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.
606   $aMaths for computer scientists$2bicssc
610   $aADMM
610   $aALADIN
610   $adecentralized optimization
610   $aDezentrale Optimierung
610   $adistributed optimization
610   $aoptimal power flow
610   $aOptimal Power Flow
610   $aVerteilte Optimierung
615  7$aMaths for computer scientists
700   $aEngelmann$b Alexander$4auth$01285816
906   $aBOOK
912   $a9910634082603321
996   $aDistributed Optimization with Application to Power Systems and Control$93019660
997   $aUNINA