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Diagonalize parametric matrix #77
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Currently no. I am not even sure if I understand how that would work. In the governing equation If you know how to approach such a problem and know of reading matterial I can look at, I can let you know if it would be feasable to implement or not. |
I think both the eigenvalue matrix and the eigenvector matrix (and its inverse) are in general a function of polynomial scalars appearing inside the given matrix, so yes, if I understand your question correctly. Unfortunately, I have no idea about how to deal with the problem, it's just something that came out of my research. |
I figured a way to solve this, but it requires a few new functions that are not currently implemented. |
Great! Thank you. |
So here is working example that should work in version 1.1.3: import numpy as np
import numpoly as nu
# q1, q2 are parameters, q0 reserved for calculations as lambda
q0, q1, q2 = nu.variable(3)
# parametric matrix to find eigen vectors/values from
mat = nu.polynomial([[1, q1], [q2, 1]])
# characteristic eq == 0
char_expr = nu.det(mat-np.eye(len(mat))*q0)
# evaluate parameters and extract eigen values
eig_vals = nu.roots(nu.polynomial(char_expr(q1=3, q2=1))) Temporary caveates:
Let me know if this is what you were looking for. |
Yes, thank you, this is exactly was I was looking for! |
Hello,
is it possible to get a parametric eigendecomposition of a matrix whose elements are a mix of known numerical values and parametric variables, expressed as ndpoly entries?
Thank you.
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