Zimmermann, Jan ; Tatarenko, Tatiana ; Willert, Volker ; Adamy, Jürgen (2021)
Projected Push-Sum Gradient Descent-Ascent for Convex Optimization with Application to Economic Dispatch Problems.
2020 59th IEEE Conference on Decision and Control (CDC). Jeju, South Korea (14.-18.12.2020)
doi: 10.26083/tuprints-00017573
Konferenzveröffentlichung, Zweitveröffentlichung, Postprint
Kurzbeschreibung (Abstract)
We propose a novel algorithm for solving convex, constrained and distributed optimization problems defined on multi-agent-networks, where each agent has exclusive access to a part of the global objective function. The agents are able to exchange information over a directed, weighted communication graph, which can be represented as a column-stochastic matrix. The algorithm combines an adjusted push-sum consensus protocol for information diffusion and a gradient descent-ascent on the local cost functions, providing convergence to the optimum of their sum. We provide results on a reformulation of the push-sum into single matrix updates and prove convergence of the proposed algorithm to an optimal solution, given standard assumptions in distributed optimization. The algorithm is applied to a distributed economic dispatch problem, in which the constraints can be expressed in local and global subsets.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Zimmermann, Jan ; Tatarenko, Tatiana ; Willert, Volker ; Adamy, Jürgen |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Projected Push-Sum Gradient Descent-Ascent for Convex Optimization with Application to Economic Dispatch Problems |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Ort: | New York, NY |
| Publikationsdatum der Erstveröffentlichung: | 2020 |
| Verlag: | IEEE |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Proceedings of the IEEE Conference on Decision & Control |
| Jahrgang/Volume einer Zeitschrift: | 59 |
| Buchtitel: | 2020 59th IEEE Conference on Decision and Control (CDC) |
| Kollation: | 8 Seiten |
| Veranstaltungstitel: | 2020 59th IEEE Conference on Decision and Control (CDC) |
| Veranstaltungsort: | Jeju, South Korea |
| Veranstaltungsdatum: | 14.-18.12.2020 |
| DOI: | 10.26083/tuprints-00017573 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/17573 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung |
| Kurzbeschreibung (Abstract): | We propose a novel algorithm for solving convex, constrained and distributed optimization problems defined on multi-agent-networks, where each agent has exclusive access to a part of the global objective function. The agents are able to exchange information over a directed, weighted communication graph, which can be represented as a column-stochastic matrix. The algorithm combines an adjusted push-sum consensus protocol for information diffusion and a gradient descent-ascent on the local cost functions, providing convergence to the optimum of their sum. We provide results on a reformulation of the push-sum into single matrix updates and prove convergence of the proposed algorithm to an optimal solution, given standard assumptions in distributed optimization. The algorithm is applied to a distributed economic dispatch problem, in which the constraints can be expressed in local and global subsets. |
| Status: | Postprint |
| URN: | urn:nbn:de:tuda-tuprints-175739 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Regelungsmethoden und Robotik (ab 01.08.2022 umbenannt in Regelungsmethoden und Intelligente Systeme) |
| Hinterlegungsdatum: | 23 Mär 2021 08:14 |
| Letzte Änderung: | 30 Mär 2021 09:54 |
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| Zugehörige Links: | |
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