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  • Distributed Optimization with Application to Power Systems and Control

    Alexander Engelmann

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    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.

    Umfang: XIII, 202 S.

    Preis: 43.00 €

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    Empfohlene Zitierweise
    Engelmann, A. 2022. Distributed Optimization with Application to Power Systems and Control. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000144792
    Engelmann, A., 2022. Distributed Optimization with Application to Power Systems and Control. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000144792
    Engelmann, A. Distributed Optimization with Application to Power Systems and Control. KIT Scientific Publishing, 2022. DOI: https://doi.org/10.5445/KSP/1000144792
    Engelmann, A. (2022). Distributed Optimization with Application to Power Systems and Control. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000144792
    Engelmann, Alexander. 2022. Distributed Optimization with Application to Power Systems and Control. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000144792




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    Weitere Informationen

    Veröffentlicht am 21. November 2022

    Sprache

    Englisch

    Seitenanzahl:

    226

    ISBN
    Paperback 978-3-7315-1180-9

    DOI
    https://doi.org/10.5445/KSP/1000144792