# How to Diagonalize a Matrix. Step by Step Explanation.

## Problem 211

In this post, we explain how to diagonalize a matrix if it is diagonalizable.

As an example, we solve the following problem.

Diagonalize the matrix

\[A=\begin{bmatrix}

4 & -3 & -3 \\

3 &-2 &-3 \\

-1 & 1 & 2

\end{bmatrix}\]
by finding a nonsingular matrix $S$ and a diagonal matrix $D$ such that $S^{-1}AS=D$.

(Update 10/15/2017. A new example problem was added.)

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Contents

The process can be summarized as follows. A concrete example is provided below, and several exercise problems are presented at the end of the post.

## Diagonalization Procedure

Let $A$ be the $n\times n$ matrix that you want to diagonalize (if possible).

- Find the characteristic polynomial $p(t)$ of $A$.
- Find eigenvalues $\lambda$ of the matrix $A$ and their algebraic multiplicities from the characteristic polynomial $p(t)$.
- For each eigenvalue $\lambda$ of $A$, find a basis of the eigenspace $E_{\lambda}$.

If there is an eigenvalue $\lambda$ such that the geometric multiplicity of $\lambda$, $\dim(E_{\lambda})$, is less than the algebraic multiplicity of $\lambda$, then the matrix $A$ is not diagonalizable. If not, $A$ is diagonalizable, and proceed to the next step. - If we combine all basis vectors for all eigenspaces, we obtained $n$ linearly independent eigenvectors $\mathbf{v}_1, \mathbf{v}_2, \dots, \mathbf{v}_n$.
- Define the nonsingular matrix

\[S=[\mathbf{v}_1 \mathbf{v}_2 \dots \mathbf{v}_n] .\] - Define the diagonal matrix $D$, whose $(i,i)$-entry is the eigenvalue $\lambda$ such that the $i$-th column vector $\mathbf{v}_i$ is in the eigenspace $E_{\lambda}$.
- Then the matrix $A$ is diagonalized as \[ S^{-1}AS=D.\]

## Example of a matrix diagonalization

Now let us examine these steps with an example.

Let us consider the following $3\times 3$ matrix.

\[A=\begin{bmatrix}

4 & -3 & -3 \\

3 &-2 &-3 \\

-1 & 1 & 2

\end{bmatrix}.\]
We want to diagonalize the matrix if possible.

### Step 1: Find the characteristic polynomial

The characteristic polynomial $p(t)$ of $A$ is

\[p(t)=\det(A-tI)=\begin{vmatrix}

4-t & -3 & -3 \\

3 &-2-t &-3 \\

-1 & 1 & 2-t

\end{vmatrix}.\]
Using the cofactor expansion, we get

\[p(t)=-(t-1)^2(t-2).\]

### Step 2: Find the eigenvalues

From the characteristic polynomial obtained in Step 1, we see that eigenvalues are

\[\lambda=1 \text{ with algebraic multiplicity } 2\]
and

\[\lambda=2 \text{ with algebraic multiplicity } 1.\]

### Step 3: Find the eigenspaces

Let us first find the eigenspace $E_1$ corresponding to the eigenvalue $\lambda=1$.

By definition, $E_1$ is the null space of the matrix

\[A-I=\begin{bmatrix}

3 & -3 & -3 \\

3 &-3 &-3 \\

-1 & 1 & 1

\end{bmatrix}

\rightarrow

\begin{bmatrix}

1 & -1 & -1 \\

0 &0 &0 \\

0 & 0 & 0

\end{bmatrix}\]
by elementary row operations.

Hence if $(A-I)\mathbf{x}=\mathbf{0}$ for $\mathbf{x}\in \R^3$, we have

\[x_1=x_2+x_3.\]
Therefore, we have

\begin{align*}

E_1=\calN(A-I)=\left \{\quad \mathbf{x}\in \R^3 \quad \middle| \quad \mathbf{x}=x_2\begin{bmatrix}

1 \\

1 \\

0

\end{bmatrix}+x_3\begin{bmatrix}

1 \\

0 \\

1

\end{bmatrix} \quad \right \}.

\end{align*}

From this, we see that the set

\[\left\{\quad\begin{bmatrix}

1 \\

1 \\

0

\end{bmatrix},\quad \begin{bmatrix}

1 \\

0 \\

1

\end{bmatrix}\quad \right\}\]
is a basis for the eigenspace $E_1$.

Thus, the dimension of $E_1$, which is the geometric multiplicity of $\lambda=1$, is $2$.

Similarly, we find a basis of the eigenspace $E_2=\calN(A-2I)$ for the eigenvalue $\lambda=2$.

We have

\begin{align*}

A-2I=\begin{bmatrix}

2 & -3 & -3 \\

3 &-4 &-3 \\

-1 & 1 & 0

\end{bmatrix}

\rightarrow \cdots \rightarrow \begin{bmatrix}

1 & 0 & 3 \\

0 &1 &3 \\

0 & 0 & 0

\end{bmatrix}

\end{align*}

by elementary row operations.

Then if $(A-2I)\mathbf{x}=\mathbf{0}$ for $\mathbf{x}\in \R^3$, then we have

\[x_1=-3x_3 \text{ and } x_2=-3x_3.\]
Therefore we obtain

\begin{align*}

E_2=\calN(A-2I)=\left \{\quad \mathbf{x}\in \R^3 \quad \middle| \quad \mathbf{x}=x_3\begin{bmatrix}

-3 \\

-3 \\

1

\end{bmatrix} \quad \right \}.

\end{align*}

From this we see that the set

\[\left \{ \quad \begin{bmatrix}

-3 \\

-3 \\

1

\end{bmatrix} \quad \right \}\]
is a basis for the eigenspace $E_2$ and the geometric multiplicity is $1$.

Since for both eigenvalues, the geometric multiplicity is equal to the algebraic multiplicity, the matrix $A$ is not defective, and hence diagonalizable.

### Step 4: Determine linearly independent eigenvectors

From Step 3, the vectors

\[\mathbf{v}_1=\begin{bmatrix}

1 \\

1 \\

0

\end{bmatrix}, \mathbf{v}_2=\begin{bmatrix}

1 \\

0 \\

1

\end{bmatrix}, \mathbf{v}_3=\begin{bmatrix}

-3 \\

-3 \\

1

\end{bmatrix} \]
are linearly independent eigenvectors.

### Step 5: Define the invertible matrix $S$

Define the matrix $S=[\mathbf{v}_1 \mathbf{v}_2 \mathbf{v}_3]$. Thus we have

\[S=\begin{bmatrix}

1 & 1 & -3 \\

1 &0 &-3 \\

0 & 1 & 1

\end{bmatrix}\]
and the matrix $S$ is nonsingular (since the column vectors are linearly independent).

### Step 6: Define the diagonal matrix $D$

Define the diagonal matrix

\[D=\begin{bmatrix}

1 & 0 & 0 \\

0 &1 &0 \\

0 & 0 & 2

\end{bmatrix}.\]
Note that $(1,1)$-entry of $D$ is $1$ because the first column vector $\mathbf{v}_1=\begin{bmatrix}

1 \\

1 \\

0

\end{bmatrix}$ of $S$ is in the eigenspace $E_1$, that is, $\mathbf{v}_1$ is an eigenvector corresponding to eigenvalue $\lambda=1$.

Similarly, the $(2,2)$-entry of $D$ is $1$ because the second column $\mathbf{v}_2=\begin{bmatrix}

1 \\

0 \\

1

\end{bmatrix}$ of $S$ is in $E_1$.

The $(3,3)$-entry of $D$ is $2$ because the third column vector $\mathbf{v}_3=\begin{bmatrix}

-3 \\

-3 \\

1

\end{bmatrix}$ of $S$ is in $E_2$.

(The order you arrange the vectors $\mathbf{v}_1, \mathbf{v_2}, \mathbf{v}_3$ to form $S$ does not matter but once you made $S$, then the order of the diagonal entries is determined by $S$, that is, the order of eigenvectors in $S$.)

### Step 7: Finish the diagonalization

Finally, we can diagonalize the matrix $A$ as

\[S^{-1}AS=D,\]
where

\[S=\begin{bmatrix}

1 & 1 & -3 \\

1 &0 &-3 \\

0 & 1 & 1

\end{bmatrix} \text{ and } D=\begin{bmatrix}

1 & 0 & 0 \\

0 &1 &0 \\

0 & 0 & 2

\end{bmatrix}.\]
(Here you don’t have to find the inverse matrix $S^{-1}$ unless you are asked to do so.)

## Diagonalization Problems and Examples

Check out the following problems about the diagonalization of a matrix to see if you understand the procedure.

**Problem**. Diagonalize \[A=\begin{bmatrix}

1 & 2\\

4& 3

\end{bmatrix}\] and compute $A^{100}$.

For a solution of this problem and related questions, see the post “Diagonalize a 2 by 2 Matrix $A$ and Calculate the Power $A^{100}$“.

**Problem**. Determine whether the matrix

\[A=\begin{bmatrix}

0 & 1 & 0 \\

-1 &0 &0 \\

0 & 0 & 2

\end{bmatrix}\] is diagonalizable. If it is diagonalizable, then find the invertible matrix $S$ and a diagonal matrix $D$ such that $S^{-1}AS=D$.

For a solution, check out the post “Diagonalize the 3 by 3 Matrix if it is Diagonalizable“.

**Problem**. Let

\[A=\begin{bmatrix}

2 & -1 & -1 \\

-1 &2 &-1 \\

-1 & -1 & 2

\end{bmatrix}.\] Determine whether the matrix $A$ is diagonalizable. If it is diagonalizable, then diagonalize $A$.

For a solution, see the post “Quiz 13 (Part 1) Diagonalize a matrix.“.

**Problem**. Diagonalize the matrix

\[A=\begin{bmatrix}

1 & 1 & 1 \\

1 &1 &1 \\

1 & 1 & 1

\end{bmatrix}.\]

In the solution given in the post “Diagonalize the 3 by 3 Matrix Whose Entries are All One“, we use an indirect method to find eigenvalues and eigenvectors.

The next problem is a diagonalization problem of a matrix with variables.

**Problem**.

Diagonalize the complex matrix

\[A=\begin{bmatrix}

a & b-a\\

0& b

\end{bmatrix}.\] Using the result of the diagonalization, compute $A^k$ for each $k\in \N$.

The solution is given in the post↴

Diagonalize the Upper Triangular Matrix and Find the Power of the Matrix

### A Hermitian Matrix can be diagonalized by a unitary matrix

**Theorem**. If $A$ is a Hermitian matrix, then $A$ can be diagonalized by a unitary matrix $U$.

This means that there exists a unitary matrix $U$ such that $U^{-1}AU$ is a diagonal matrix.

**Problem**.

Diagonalize the Hermitian matrix

\[A=\begin{bmatrix}

1 & i\\

-i& 1

\end{bmatrix}\] by a unitary matrix.

The solution is given in the post ↴

Diagonalize the $2\times 2$ Hermitian Matrix by a Unitary Matrix

### Related Question.

More Problems related to the diagonalization of a matrix:

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## 9 Responses

[…] the post how to diagonalize a matrix for a review of the diagonalization […]

[…] We give two solutions. The first solution is a standard method of diagonalization. For a review of the process of diagonalization, see the post “How to diagonalize a matrix. Step by step explanation.” […]

[…] For a general procedure of the diagonalization of a matrix, please read the post “How to Diagonalize a Matrix. Step by Step Explanation“. […]

[…] mathbf{v} end{bmatrix} =begin{bmatrix} -2 & 1\ 1& 1 end{bmatrix}.] Then by the general procedure of the diagonalization, we have begin{align*} S^{-1}AS=D, end{align*} where [D:=begin{bmatrix} -1 & 0\ 0& 5 […]

[…] For a procedure of the diagonalization, see the post “How to Diagonalize a Matrix. Step by Step Explanation.“. […]

[…] follows from the general procedure of the diagonalization that $P$ is a nonsingular matrix and [P^{-1}AP=D,] where $D$ is a diagonal matrix […]

[…] The solution is given in the post How to Diagonalize a Matrix. Step by Step Explanation […]

[…] When $a=b$, then $A$ is already diagonal matrix. So let us consider the case $aneq b$. In the previous parts, we obtained the eigenvalues $a, b$, and corresponding eigenvectors [begin{bmatrix} 1 \ 0 end{bmatrix} text{ and } begin{bmatrix} 1 \ 1 end{bmatrix}.] Let $S=begin{bmatrix} 1 & 1\ 0& 1 end{bmatrix}$ be a matrix whose column vectors are the eigenvectors. Then $S$ is invertible and we have [S^{-1}AS=begin{bmatrix} a & 0\ 0& b end{bmatrix}] by the diagonalization process. […]

[…] It follows that the matrix [U=begin{bmatrix} mathbf{u}_1 & mathbf{u}_2 end{bmatrix}=frac{1}{sqrt{2}}begin{bmatrix} 1 & 1\ i& -i end{bmatrix}] is unitary and [U^{-1}AU=begin{bmatrix} 0 & 0\ 0& 2 end{bmatrix}] by diagonalization process. […]