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Projected Push-Sum Gradient Descent-Ascent for Convex Optimization with Application to Economic Dispatch Problems

Zimmermann, Jan ; Tatarenko, Tatiana ; Willert, Volker ; Adamy, Jürgen (2020)
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: 10.1109/CDC42340.2020.9304360
Konferenzveröffentlichung, Bibliographie

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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: 2020
Autor(en): Zimmermann, Jan ; Tatarenko, Tatiana ; Willert, Volker ; Adamy, Jürgen
Art des Eintrags: Bibliographie
Titel: Projected Push-Sum Gradient Descent-Ascent for Convex Optimization with Application to Economic Dispatch Problems
Sprache: Englisch
Publikationsjahr: 2020
Ort: New York, NY
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: 10.1109/CDC42340.2020.9304360
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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.

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: 02 Aug 2024 12:35
Letzte Änderung: 02 Aug 2024 12:35
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