DECOMPOSITION
qz
QZ decomposition for generalized eigenvalues of a pair of matrices.
Example of usage
Factorise a labelled industrial sensor calibration matrix and verify reconstruction.
Usage / limitations
+Use when: Solve generalised eigenvalue problems for model pairs.
−Limitations: Matrix shape, symmetry, conditioning and numerical precision constrain validity.
Mathematical formula
QᵀAZ = S and QᵀBZ = T.
Sample dataShow
{
"variable": "measurement_matrix",
"domain": "Industrial sensor calibration",
"feature_names": [
"temperature_c",
"vibration_mm_s"
],
"matrix": [
[
2.0,
0.0
],
[
0.0,
3.0
]
],
"reference_matrix": [
[
1.0,
0.0
],
[
0.0,
1.0
]
]
}
Code example (Python)
# DataLens.ai sample data — industrial sensor calibration
import numpy as np
A = np.diag([2.,3.]); B = np.eye(2)
B = np.eye(2)
# DataLens.ai example
# Factorise the named two- or three-sensor calibration matrix.
from scipy.linalg import qz
AA, BB, Q, Z = qz(A,B)
assert np.allclose(Q @ AA @ Z.T,A) and np.allclose(Q @ BB @ Z.T,B)
print("Expected output verified")
# DataLens.ai example
# Factorise the named two- or three-sensor calibration matrix.
from scipy.linalg import qz
AA, BB, Q, Z = qz(A,B)
assert np.allclose(Q @ AA @ Z.T,A) and np.allclose(Q @ BB @ Z.T,B)
print("Expected output verified")