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[[Category:benchmark]] |
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[[Category:SLICOT]] |
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+ | [[Category:linear]] |
+ | [[Category:time invariant]] |
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Here is [https://www.python.org Python] code for loading the matrices (<math>A</math> is stored as a sparse matrix that is mostly full and <math>C</math> is stored as an array of 8-bit unsigned integers): |
Here is [https://www.python.org Python] code for loading the matrices (<math>A</math> is stored as a sparse matrix that is mostly full and <math>C</math> is stored as an array of 8-bit unsigned integers): |
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+ | :<syntaxhighlight lang="python"> |
import numpy as np |
import numpy as np |
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from scipy.io import loadmat |
from scipy.io import loadmat |
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B = mat['B'] |
B = mat['B'] |
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C = mat['C'].astype(np.float_) |
C = mat['C'].astype(np.float_) |
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+ | </syntaxhighlight> |
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− | </source> |
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The <math>(A, B, C)</math> represents a second-order system |
The <math>(A, B, C)</math> represents a second-order system |
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Here is [https://www.python.org Python] code for checking the structure and extracting the second-order matrices: |
Here is [https://www.python.org Python] code for checking the structure and extracting the second-order matrices: |
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+ | :<syntaxhighlight lang="python"> |
n = 48 |
n = 48 |
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n2 = n // 2 |
n2 = n // 2 |
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Bso = B[n2:] |
Bso = B[n2:] |
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Cso = C[:, n2:] |
Cso = C[:, n2:] |
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+ | </syntaxhighlight> |
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− | </source> |
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==Dimensions== |
==Dimensions== |
Revision as of 18:37, 29 August 2023
Description: Motion Problem in a Building
This benchmark models the displacement of a multi-story building for example during an Earthquake. More details can be found in [1] and [2], [3].
Earthquake Model
Origin
This benchmark is part of the SLICOT Benchmark Examples for Model Reduction[3].
Data
The system matrices ,
,
are available from the SLICOT benchmarks page: build.zip and are stored as MATLAB .mat file.
Here is Python code for loading the matrices ( is stored as a sparse matrix that is mostly full and
is stored as an array of 8-bit unsigned integers):
import numpy as np from scipy.io import loadmat mat = loadmat('build.mat') A = mat['A'].toarray() B = mat['B'] C = mat['C'].astype(np.float_)
The represents a second-order system
as
Here is Python code for checking the structure and extracting the second-order matrices:
n = 48 n2 = n // 2 assert np.all(A[:n2, :n2] == 0) assert np.all(A[:n2, n2:] == np.eye(n2)) assert np.all(B[:n2] == 0) assert np.all(C[:, :n2] == 0) Eso = -A[n2:, n2:] Kso = -A[n2:, :n2] Bso = B[n2:] Cso = C[:, n2:]
Dimensions
First differential order
System structure:
System dimensions:
,
,
.
Second differential order
System structure:
System dimensions:
,
,
.
Citation
To cite this benchmark, use the following references:
- For the benchmark itself and its data:
- Niconet e.V., SLICOT - Subroutine Library in Systems and Control Theory, http://www.slicot.org
@MANUAL{slicot_build, title = {{SLICOT} - Subroutine Library in Systems and Control Theory}, organization = {Niconet e.V.} address = {\url{http://www.slicot.org}}, key = {SLICOT} }
- For the background on the benchmark:
@ARTICLE{morAntSG01, author = {A.C. Antoulas, D.C. Sorensen and S. Gugercin}, title = {A survey of model reduction methods for large-scale systems}, journal = {Contemporary Mathematics}, volume = {280}, pages = {193--219}, year = {2001}, doi = {10.1090/conm/280} }
References
- ↑ A.C. Antoulas, D.C. Sorensen and S. Gugercin. A survey of model reduction methods for large-scale systems. Contemporary Mathematics, 280: 193--219, 2001.
- ↑ Y. Chahlaoui, P. Van Dooren, A collection of Benchmark examples for model reduction of linear time invariant dynamical systems, Working Note 2002-2: 2002.
- ↑ 3.0 3.1 Y. Chahlaoui, P. Van Dooren, Benchmark Examples for Model Reduction of Linear Time-Invariant Dynamical Systems, Dimension Reduction of Large-Scale Systems, Lecture Notes in Computational Science and Engineering, vol 45: 379--392, 2005.