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.

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