Eigenvalue of a matrix Let $A$ be an $n\times n$ matrix and let $I$ be the $n\times n$ identity matrix. Show that if $A^{2} = I$, and $A \neq I$, then $\lambda =-1$ is an eigenvalue of $A$. This problem doesn't seem that too hard to solve, but I am stuck near the end. Here is what I have done so far.
Since $A^{2}=I$, then by definition $Ax=\lambda x$, where $x$ is an eigenvector of $A$ and $\lambda$ is an eigenvalue of $A$. It follows that $x=Ix=A^{2}x=A(Ax)=A(\lambda x)= \lambda(Ax)=\lambda^{2}x$. (Now I was going to use the fact that since $A \neq I$, that $x\neq 0$, so we get that $\lambda = 1$ or $-1$). This is where I am stuck. 

If $A^2=I$, then $A$ satisfies the polynomial $t^2-1=(t-1)(t+1)$. Therefore, the minimal polynomial of $A$ divides $(t-1)(t+1)$.
If the minimal polynomial is $t-1$, then that means that $A-I=0$, so $A=I$; but we are assuming that $A\neq I$, so this is not the case.
That means that the minimal polynomial is divisible by $t+1$; since every irreducible factor of the minimal polynomial divides the characteristic polynomial, it follows that $t+1$ divides the characteristic polynomial of $A$, and hence that $-1$ is an eigenvalue of $A$, as desired. 
