Calculate Eigenvalues of a Matrix

Linear Algebra

Medium

Calculate the eigenvalues of a square matrix whose eigenvalues are guaranteed to be real.

Av = \lambda v

Here, $A$ is the input matrix, $v$ is a nonzero eigenvector, and $\lambda$ is its eigenvalue. Return all eigenvalues in ascending order as a NumPy array of floats.

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Examples

Input: matrix = [[4, 1], [2, 3]]

Output: [2.0, 5.0]

Explanation: Both values satisfy the characteristic equation of the matrix.

Input: matrix = [[5]]

Output: [5.0]

Hint 1

Use np.linalg.eigvals(matrix) to compute the eigenvalues.

Hint 2

Use .real followed by np.sort() for the guaranteed-real output.

Requirements

Compute the eigenvalues with NumPy
Sort them in ascending order
Return a NumPy array of floating-point values

Constraints

matrix is a nonempty square numeric list
Every eigenvalue is real
Matrix dimensions are at most $100 \times 100$
Use NumPy only

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