Why does a symmetric matrix have a complete set of eigenvectors and eigenvalues? I am attempting to learn more about the adjacency matrix(graph theory) but given that I have forgotten a lot of linear algebra, I can't seem to know why this is true. Can someone give me a proof? 
 A: If a matrix $A$ is diagonalizable, then its minimal polynomial is
$$
            m(\lambda)=(\lambda-\lambda_1)(\lambda-\lambda_2)\cdots(\lambda-\lambda_k)
$$
where $\lambda_j$ are the distinct eigenvalues.
Conversely, suppose that the minimal polynomial $m$ for a matrix $A$ factors into distinct linear factors, as written above. Then
$$
    (A-\lambda_l I)\prod_{j\ne l}(A-\lambda_j I)=0.
$$
So the non-zero vectors in the range of $\prod_{j\ne l}(A-\lambda_j I)$ are eigenvectors of $A$ with eigenvalue $\lambda_l$. And every vector $x$ can be written as a sum of such vectors because
$$
         1 \equiv \sum_{l=1}^{k}\frac{\prod_{j\ne l}(\lambda-\lambda_j)}{\prod_{j\ne l}(\lambda_l-\lambda_j)}
$$
and, hence,
$$
        I = \sum_{l=1}^{k}\frac{1}{\prod_{j\ne l}(\lambda_l-\lambda_j)}
             \prod_{j\ne l}(A-\lambda_j I)
$$
Therefore, a matrix is diagonalizable (equivalently, has a basis of eigenvectors) iff the minimal polynomial for $A$ factors into the product of the distinct linear factors.
A symmetric $A$ has the property that its conjugate transpose $A^{\star}$ is equal to $A$. A symmetric $A$ is a special case of a normal $A$ for which $A^{\star}A=AA^{\star}$. A normal matrix has the property that
$$
           \|Ax\|^{2}=(Ax,Ax)=(A^{\star}Ax,x)=(AA^{\star}x,x)=\|A^{\star}x\|^{2}.
$$
Therefore, $A^{2}x=0$ iff $A^{\star}Ax=0$, which implies $Ax=0$ because
$$
     0 = (A^{\star}Ax,x)=(Ax,Ax)=\|Ax\|^{2}.
$$
Therefore, the minimal polynomial of a normal $A$ has no repeated factors because
$$
           (A-\lambda_{k}I)^{2}(\cdots)x = 0 \iff (A-\lambda_{k}I)(\cdots)x =0.
$$
(Note: $A-\lambda_{k}I$ is normal iff $A$ is normal.)
