Back in the previous post, we wrote "similar" (but didn't define it), and we said the characteristic polynomials were the same for two similar matrices.

# Definition of Similar and Diagonalization

Let's say you have a matrix D. If D = $\begin{bmatrix}5 & 0 \\ 0 & 3\end{bmatrix}$, then $D^k = \begin{bmatrix}5^k & 0 \\ 0 & 3^k\end{bmatrix}$. So that's what the middle matrix is going to look like. In order for the characteristic polynomials to be the same, the elements in the diagonal must be the eigenvalues, with the same multiplicities.

## Diagonalization Theorem

The columns of P are the

**eigenvector**of A.

The columns of D are the corresponding

**eigenvalues**.

First, we have to find the eigenvalues. $$\begin{vmatrix}7-\lambda & 2\\-4 & 1-\lambda\end{vmatrix} = 0 \to (\lambda - 7)(\lambda - 1) + 8 = 0 \to (\lambda - 5)(\lambda - 3) = 0$$ So, our eigenvalues are $\boxed{3 \text{ and } 5}$.

When the eigenvalue is 5, the matrix is $\begin{bmatrix}2 & 2\\-4 & -4\end{bmatrix}$, so the eigenvector = $\begin{bmatrix}1\\-1\end{bmatrix}$.

When the eigenvalue is 3, the matrix is $\begin{bmatrix}4 & 2\\-4 & -2\end{bmatrix}$, so the eigenvector = $\begin{bmatrix}1\\-2\end{bmatrix}$. Now, we have our P matrix composed of eigenvectors and our D matrix composed of eigenvalues. The inverse of P is calculated with a simple formula. $$\begin{bmatrix}7 & 2\\-4 & 1\end{bmatrix} = \boxed{\begin{bmatrix}1 & 1\\-1 & -2\end{bmatrix} \begin{bmatrix}5 & 0\\0 & 3\end{bmatrix} \begin{bmatrix}2 & 1\\-1 & -1\end{bmatrix}}$$

# Theorem #1

An n x n matrix with n distinct eigenvalues is diagonalizable.

Note: It's important to note that it doesn't have to have n distinct eigenvalues though.

# Theorem #2

## Part One

## Part Two

A matrix is diagonalizable iff the sum of the dimensions of the eigenspaces equals n, and this happens **only **when the geometric multiplicity equals the algebraic multiplicity for each eigenvalue.

## Part Three

If A is diagonalizable, and B_k is the basis for the eigenspace corresponding to the k'th eigenvalue for each k, then collection of all of the vectors in those bases forms an eigenvector basis for R^n.