Distributed Optimization with Application to Power Systems and Control

By:

Engelmann, Alexander [auth]

Material type: Article

ArticleLanguage: English Publication details: Karlsruhe KIT Scientific Publishing 2022Description: 1 electronic resource (226 p.)ISBN:

1000144792
9783731511809

Subject(s):

Online resources:

Description

Summary: 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.

List(s) this item appears in: Faculty Informational Technology

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Holdings
Item type	Current library	Collection	Shelving location	Call number	Status	Notes	Date due	Barcode
Electronic edition	Bucheon University Library	Computers	DOAB	004.7 D63	Not for loan	View (pdf)		1010781

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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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