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MORLAB - Model Order Reduction LABoratory

Authors: Benner, Peter; Werner, Steffen W. R.;

MORLAB - Model Order Reduction LABoratory

Abstract

{"references": ["P. Benner, S. W. R. Werner, MORLAB -- A Model Order Reduction Framework in MATLAB and Octave, In A. M. Bigatti, J. Carette, J. H. Davenport, M. Joswig, and T. de Wolff, editors, Mathematical Software -- ICMS 2020, volume 12097 of Lecture Notes in Comput. Sci., pages 432--441. Springer International Publishing, Cham, 2020.", "P. Benner, S. W. R. Werner, Model reduction of descriptor systems with the MORLAB toolbox, IFAC-PapersOnLine 9th Vienna International Conference on Mathematical Modelling MATHMOD 2018, Vienna, Austria, 21--23 February 2018 51 (2) (2018) 547--552. https://doi.org/10.1016/j.ifacol.2018.03.092", "P. Benner, S. W. R. Werner, MORLAB - Modellreduktion in MATLAB, in: T. Meurer, F. Woittennek (Eds.), Tagungsband GMA-FA 1.30 'Modellierung, Identifikation und Simulation in der Automatisierungstechnik' und GMA-FA 1.40 'Theoretische Verfahren der Regelungstechnik', Workshop in Anif, Salzburg, 18.-22.09.2017, 2017, pp. 508--517. URL http://www.control.tf.uni-kiel.de/files/gma/2017/Tagungsband2017.pdf", "P. Benner, A MATLAB repository for model reduction based on spectral projection, in: 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, 2006, pp. 19--24. https://doi.org/10.1109/CACSD-CCA-ISIC.2006.4776618", "P. Benner, J. Saak, A semi-discretized heat transfer model for optimal cooling of steel profiles, in: P. Benner, V. Mehrmann, D. Sorensen (Eds.), Dimension Reduction of Large-Scale Systems, Vol. 45 of Lect. Notes Comput. Sci. Eng., Springer-Verlag, Berlin/Heidelberg, Germany, 2005, pp. 353--356. https://doi.org/10.1007/3-540-27909-1_19", "P. Benner, S. W. R. Werner, Balancing related model reduction with the MORLAB toolbox, Proc. Appl. Math. Mech. 18 (1) (2018) e201800083. https://doi.org/10.1002/pamm. 201800083"]}

The Software is provided as a MATLAB toolbox (.mltbx) file, an Octave package (.tar.gz) and a zip-archive. The toolbox-file needs a recent MATLAB version (R2014a or later) to install properly. The Octave package was tested in Octave 4.0.0 and above. The content of the zip-archive is equipped with all functions to also run on older releases. In order to get all functions registered properly, change to the installation folder and run the ml_addpath function. This will set the searchpath correctly for the current session. For citing the software, take a look into the CITATION file of the toolbox.

The MORLAB, Model Order Reduction LABoratory, toolbox is a collection of MATLAB/Octave routines for model order reduction of dynamical systems based on the solution of matrix equations. The implementation is based on spectral projection methods, e.g., methods based on the matrix sign function and the matrix disk function.

Keywords

Descriptor Systems, MATLAB, Second-Order Systems, MOR, Matrix Sign Function, Spectral Projection Methods, Matrix Disk Function, Matrix Equations, Discrete-Time Systems, LTI Systems, Reduced-Order Modeling, System Theory, Octave, FOS: Mathematics, Model Order Reduction, Balanced Truncation, Model Reduction, Control Theory, Continuous-Time Systems, Mathematics

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download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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13
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18