Row reducing matrices to obtain the eigenvector

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Find all distinct (real or complex) eigenvalues of $A$. Then find the basic eigenvectors of $A$ corresponding to each eigenvalue. For each eigenvalue, specify the number of basic eigenvectors corresponding to that eigenvalue.

$A = \begin{bmatrix}-16 & -10\\17& 10\end{bmatrix}$

So $A -\lambda I = \begin{bmatrix}-16-\lambda & -10\\17& 10-\lambda\end{bmatrix}$

Which means $det(A-\lambda I) = \lambda^2 + 6\lambda - 10$

Therefore, using the quadratic formula, the eigenvalues should be:

$\lambda = -3\pm \sqrt(19)i$

So now I get the two eigenvectors which are:

$B = \begin{bmatrix}-13 - \sqrt19i& -10\\17& 13 - \sqrt19i\end{bmatrix}$

$C = \begin{bmatrix}-13 + \sqrt19i& -10\\17& 13 + \sqrt19i\end{bmatrix}$

Now the part I am struggling is finding out how to row reduce these matrices in order to obtain the answer:

For example, with $B$:

$Bx = 0$

$Bx = \begin{bmatrix}-13 - \sqrt19i& -10\\17& 13 - \sqrt19i\end{bmatrix} \begin{bmatrix}x1\\x2\end{bmatrix} = \begin{bmatrix}0\\0\end{bmatrix}$

I know I have to do row operations to first get zeroes in the bottom row, and then to get a 1 in the first row/column, but I don't know how. This requires dealing with complex numbers, and I am not sure how to do it in this scenario. Could someone give me a detailed walkthrough on how to do this?

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$$(\lambda - \lambda_1)(\lambda - \lambda_2) = \lambda^2-(\lambda_1+\lambda_2)\lambda+\lambda_1\lambda_2=\lambda^2-trace(A)\lambda +\det(A)$$

$$trace(A)=-16+10=-6$$

$$\det(A)=-160+170=10$$

The characteristic polynomial is $\lambda^2+6\lambda \color{red}{+} 10$.

$$\lambda = \frac{-6 \pm\sqrt{6^2-40}}{2}$$

Upon trying to solve for the equation $$(A-\lambda I)v=0$$

If $\lambda$ is indeed an eigenvalue, then the matrix is rank $1$ for this $2 \times 2$ matrix. You can just focus on a non-zero row and ignroe the other constraint as it doesn't give you extra information.