How can we achieve the scalar ^ Matrix functionality in python.

Like I have code in matlab

dt=0.005
A=[ 0	1; -394510	-62.831];
Kb=real(exp(1)^(A*dt));

I convert it in python as

kb=np.real(np.exp(1)**(A*dt))

But output is completely diff, np.exp(1) and (A*dt) gives the same output as in matlab exp(1) and (A*dt) gives. But when it comes to taking exponent ans will be different. I also tried the eigen value way which matlab uses when it calculate the scalar ^ matrix but still different outputs.

Eigen value way in python:

eigenvalues, eigenvectors = np.linalg.eig(A)
# Raise the eigenvalues to the power of the scalar
eigenvalues_raised = np.exp(1)**eigenvalues
# Multiply the resulting matrix by the inverse of the eigenvectors
result = eigenvectors.dot(np.diag(eigenvalues_raised)).dot(np.linalg.inv(eigenvectors))

Answer 1

[Walter Roberson]

Can you use https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.expm.html ?

[Arslan Saif]

Thank you so much, it worked