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Reduced Memory Footprint in Multiparametric Quadratic Programming by Exploiting Low Rank Structure
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Automatic Control.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-6957-2603
2016 (English)In: 55th Conference on Decision and Control (CDC), 2016 IEEE, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 3654-3661Conference paper, Published paper (Refereed)
Abstract [en]

In multiparametric programming an optimization problem which is dependent on a parameter vector is solved parametrically. In control, multiparametric quadratic programming (mp-QP) problems have become increasingly important since the optimization problem arising in Model Predictive Control (MPC) can be cast as an mp-QP problem, which is referred to as explicit MPC. One of the main limitations with mp-QP and explicit MPC is the amount of memory required to store the parametric solution and the critical regions. In this paper, a method for exploiting low rank structure in the parametric solution of an mp-QP problem in order to reduce the required memory is introduced. The method is based on ideas similar to what is done to exploit low rank modifications in generic QP solvers, but is here applied to mp-QP problems to save memory. The proposed method has been evaluated experimentally, and for some examples of relevant problems the relative memory reduction is an order of magnitude compared to storing the full parametric solution and critical regions.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 3654-3661
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keyword [en]
Multiparametric quadratic programming, low rank, memory reduction
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-133641DOI: 10.1109/CDC.2016.7798819ISI: 000400048103132ISBN: 9781509018376 (electronic)ISBN: 9781509018444 (electronic)ISBN: 9781509018383 (print)OAI: oai:DiVA.org:liu-133641DiVA, id: diva2:1062078
Conference
55th IEEE Conference on Decision and Control, Las Vegas, NV, USA, December 12-14, 2016
Available from: 2017-01-04 Created: 2017-01-04 Last updated: 2017-06-13Bibliographically approved

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