Eigenvalues are a key concept in linear algebra, especially in the study of linear transformations and matrices. They are the special values associated with a square matrix that indicate the scale at which eigenvectors (directional vectors) are stretched during the transformation represented by the matrix.

To calculate the eigenvalues for a given square matrix **A**, we follow these steps:

### Steps to Find Eigenvalues:
1. **Form the characteristic equation**: 
   \[
   \det(A - \lambda I) = 0
   \]
   where \(A\) is the matrix, \(\lambda\) represents the eigenvalue, and \(I\) is the identity matrix of the same dimensions as \(A\).

2. **Solve for \(\lambda\)**: The eigenvalues \(\lambda\) are found by solving the characteristic polynomial equation obtained from the determinant.

If you provide the matrix **A**, I can compute the eigenvalues for you.

Feel free to upload a photo of the matrix or type the matrix explicitly!

Would you like more details on any of these steps?

### Related Questions:
1. What are the properties of eigenvalues and eigenvectors?
2. How are eigenvalues applied in quantum mechanics or physics?
3. How can we interpret eigenvalues geometrically?
4. What is the significance of complex eigenvalues?
5. How are eigenvalues used in stability analysis?

### Tip:
Eigenvalues can be real or complex. Even for real matrices, the solutions to the characteristic equation can sometimes be complex numbers.

Learn how to calculate the eigenvalues of a square matrix using the characteristic equation method. This involves key concepts from linear algebra such as matrices, eigenvalues, and the determinant formula. Suitable for undergraduate students studying linear algebra.

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