How to Solve the Generalised Eigenvalue Problem in Python?

Estimated read time 2 min read

The Generalized Eigenvalue Problem is a mathematical problem that asks you to find the eigenvalues and eigenvectors of a pair of matrices. Here is an example of how to solve this problem in Python using the NumPy library:

import numpy as np

# create two matrices A and B
A = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
B = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]])

# compute the eigenvalues and eigenvectors of the generalized eigenvalue problem
eigenvalues, eigenvectors = np.linalg.eig(A, B)

# print the results
print("Eigenvalues:\n", eigenvalues)
print("Eigenvectors:\n", eigenvectors)

In this solution, we first create two matrices A and B as NumPy arrays. These matrices represent the coefficients of a pair of linear equations.

We then use the np.linalg.eig function to compute the eigenvalues and eigenvectors of the generalized eigenvalue problem. The function takes two input matrices and returns two output arrays: eigenvalues and eigenvectors.

The eigenvalues array contains the eigenvalues of the problem, while the eigenvectors array contains the corresponding eigenvectors. Each column of eigenvectors represents an eigenvector of the problem.

Finally, we print out the results of the computation using the print function.

To use this code, simply replace the matrices A and B with your own matrices and run the script. The output will be the eigenvalues and eigenvectors of the generalized eigenvalue problem.

You May Also Like

More From Author

+ There are no comments

Add yours

Leave a Reply