Answer:
Step-by-step explanation:
Consider the matrix
. We will calculate the correspondent eigenvalues and eigen vector of the matrix. REcall that, to calculate the eigenvalues of a square matrix A, we must solve the following equation
where I is the identity matrix. In this case we have the following

which gives us the following polynomial (known as the characteristic polynomial of the matrix A).
.
Hence, the only eigenvalue of the given matrix is
.
For us to calculate the eigenvalue, we want to find a base for the Kernel of matrix
replacing the value of lambda with the value of the eigen value. REcall that finding the base for the Kernel is solving the associated homogeneus system of the matrix
![\left[\begin{matrix} -2 & -2 \\ 2 & 2 \end{matrix}\right]= \left[\begin{matrix} 0 \\ 0 \end{matrix}\right]](https://tex.z-dn.net/?f=%5Cleft%5B%5Cbegin%7Bmatrix%7D%20-2%20%26%20-2%20%5C%5C%202%20%26%202%20%5Cend%7Bmatrix%7D%5Cright%5D%3D%20%5Cleft%5B%5Cbegin%7Bmatrix%7D%200%20%5C%5C%200%20%5Cend%7Bmatrix%7D%5Cright%5D)
which lead us to the equation
or equivalently,
. Hence, the solution of the system is of the form (x,y) = (x,-x) = x(1,-1). So the correspondent eigenvector is the vector (1,-1).