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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.

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DETAILS

  • Distributed Optimization with Application to Power Systems and Control
  • Engelmann, Alexander
  • Kartoniert, 226 S.
  • graph. Darst.
  • Sprache: Englisch
  • 210 mm
  • ISBN-13: 978-3-7315-1180-9
  • Titelnr.: 96215920
  • Gewicht: 420 g
  • KIT Scientific Publishing (2022)
  • Herstelleradresse

    KIT Scientific Publishing

    Strasse am Forum 2

    76131 - DE Karlsruhe

    E-Mail: info@ksp.kit.edu

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