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Scalable Positivity Preserving Model Reduction Using Linear Energy Functions

Sootla, Aivar LU and Rantzer, Anders LU (2012) 51st IEEE Conference on Decision and Control, 2012 In IEEE 51st Annual Conference on Decision and Control (CDC), 2012 p.4285-4290
Abstract
In this paper, we explore positivity preserving model reduction. The reduction is performed by truncating the states of the original system without balancing in the classical sense. This may result in conservatism, however, this way the physical meaning of the individual states is preserved. The reduced order models can be obtained using simple matrix operations or using distributed optimization methods. Therefore, the developed algorithms can be applied to sparse large-scale systems.
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
IEEE 51st Annual Conference on Decision and Control (CDC), 2012
pages
4285 - 4290
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
51st IEEE Conference on Decision and Control, 2012
external identifiers
  • scopus:84874275636
ISSN
0743-1546
ISBN
978-1-4673-2065-8
DOI
10.1109/CDC.2012.6427032
project
LCCC
language
English
LU publication?
yes
id
ffda5119-969a-46c8-a12b-279c9ce09db3 (old id 3625954)
date added to LUP
2013-03-27 11:17:21
date last changed
2017-06-04 04:09:34
@inproceedings{ffda5119-969a-46c8-a12b-279c9ce09db3,
  abstract     = {In this paper, we explore positivity preserving model reduction. The reduction is performed by truncating the states of the original system without balancing in the classical sense. This may result in conservatism, however, this way the physical meaning of the individual states is preserved. The reduced order models can be obtained using simple matrix operations or using distributed optimization methods. Therefore, the developed algorithms can be applied to sparse large-scale systems.},
  author       = {Sootla, Aivar and Rantzer, Anders},
  booktitle    = {IEEE 51st Annual Conference on Decision and Control (CDC), 2012},
  isbn         = {978-1-4673-2065-8},
  issn         = {0743-1546},
  language     = {eng},
  pages        = {4285--4290},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Scalable Positivity Preserving Model Reduction Using Linear Energy Functions},
  url          = {http://dx.doi.org/10.1109/CDC.2012.6427032},
  year         = {2012},
}