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Die Nachbarschaften sind dabei durch einen Kommunikationsgraphen festgelegt. Der Fokus liegt daher auf kommunikationsbasierten Verfahren, die schnelle Konvergenzraten aufweisen. Dabei m\u00fcssen die einzelnen Zielfunktionen der Agenten streng konvex und ihre Gradienten Lipschitz-stetig sein. In der Literatur wurden schnelle Methoden f\u00fcr die verteilte Optimierung bereits ausgiebig behandelt. Es bleiben jedoch viele offene Fragen im Bereich der spieltheoretischen Optimierung. Diese Arbeit verfolgt das Ziel, einen strukturierten Vergleich zwischen den bekannten Ergebnissen f\u00fcr diese Optimierungsprobleme zu schaffen und potentielle Richtungen f\u00fcr die zuk\u00fcnftige Forschung zu formulieren.<\/jats:p>","DOI":"10.1515\/auto-2019-0080","type":"journal-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T09:01:15Z","timestamp":1582621275000},"page":"166-175","source":"Crossref","is-referenced-by-count":0,"title":["Schnelle, verteilte Optimierungsmethoden und spieltheoretische Ans\u00e4tze in vernetzten Systemen"],"prefix":"10.1515","volume":"68","author":[{"given":"Tatiana","family":"Tatarenko","sequence":"first","affiliation":[{"name":"TU Darmstadt , Darmstadt , Germany"}]}],"member":"374","published-online":{"date-parts":[[2020,2,25]]},"reference":[{"key":"2023033110064754125_j_auto-2019-0080_ref_001_w2aab2b8b1b1b7b1ab2ab1Aa","unstructured":"D.P. Bertsekas. Nonlinear Programming. 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