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Exploiting Chordality in Optimization Algorithms for Model Predictive Control
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
C3 IoTRedwood cityUSA.
2018 (English)In: Large-Scale and Distributed Optimization, Springer, 2018, 227, Vol. 2227, p. 11-32Chapter in book (Refereed)
Abstract [en]

In this chapter we show that chordal structure can be used to devise efficient optimization methods for many common model predictive control problems. The chordal structure is used both for computing search directions efficiently as well as for distributing all the other computations in an interior-point method for solving the problem. The chordal structure can stem both from the sequential nature of the problem as well as from distributed formulations of the problem related to scenario trees or other formulations. The framework enables efficient parallel computations.

Place, publisher, year, edition, pages
Springer, 2018, 227. Vol. 2227, p. 11-32
Series
Lecture Notes in Mathematics, ISSN 0075-8434 ; 2227
Keywords [en]
Model predictive control; Quadratic programming; Chordal graphs; Message passing; Dynamic programming; Parallel computations
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-154756DOI: 10.1007/978-3-319-97478-1_2ISI: 000458487300003ISBN: 978-3-319-97478-1 (electronic)ISBN: 978-3-319-97477-4 (print)OAI: oai:DiVA.org:liu-154756DiVA, id: diva2:1291944
Available from: 2019-02-26 Created: 2019-02-26 Last updated: 2019-03-04Bibliographically approved

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Hansson, Anders

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
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Language
  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf