# Find orthonormal basis

I'm trying to solve the following exercise in my book:

Find an orthonormal basis $\alpha$ for the vector space $\left(\mathbb{R},\mathbb{R}^{2 \times 2},+\right)$ (with default inner product, $\langle A,B \rangle = Tr(A \cdot B^T$)) such that the matrix representation $L_\alpha^\alpha$ of the linear transformation $$L : \mathbb{R}^{2 \times 2} \rightarrow \mathbb{R}^{2 \times 2} : \begin{pmatrix} x & y \\ z & t\end{pmatrix} \mapsto \begin{pmatrix} x+y+t & x+y+z \\ y+z+t & x+z+t \end{pmatrix}$$ with respect to the basis $\alpha$ is a diagonal matrix.

I started out by transforming the default bases, as such: $$L(e_1) = e_1 + e_2 + e_4$$ $$L(e_2) = e_1 + e_2 + e_3$$ $$L(e_3) = e_2 + e_3 + e_4$$ $$L(e_4) = e_1 + e_3 + e_4$$

With: $$e_1 = \begin{pmatrix}1&0\\0&0\end{pmatrix}, e_2 = \begin{pmatrix}0&1\\0&0\end{pmatrix}, e_3 = \begin{pmatrix}0&0\\1&0\end{pmatrix}, e_4 = \begin{pmatrix}0&0\\0&1\end{pmatrix}$$

Can you please elaborate how to procede with such a problem. Thanks in advance!

• What is the default inner product on $\mathbb{R}^{2 \times 2}$? – Jendrik Stelzner Jan 4 '16 at 14:51
• @JendrikStelzner $\langle A,B \rangle = A^T \cdot B$ Maybe this is not 'default' but my book names it this way. – Robin Haveneers Jan 4 '16 at 14:59
• But $A^T B$ is again a matrix, not a scalar. So I wouldn’t really call this an inner product. In particular, was does orthogonality mean in this context? – Jendrik Stelzner Jan 4 '16 at 15:01
• I'm very sorry, this of course is not the inner product for $\mathbb{R}^{m \times n}$ matrices, I meant this $\langle A,B \rangle = Tr(A \cdot B^T)$ – Robin Haveneers Jan 4 '16 at 15:03
• And, orthogonality (more precise, orthonormality) means that for each $v$ in my basis $\langle v_i,v_j \rangle = 0$ (for each $i \neq j$) and $\left\lVert v_i \right\rVert = 1$ (for every $i$). – Robin Haveneers Jan 4 '16 at 15:05

In vector notation your transformation is:

$$L \begin{bmatrix} x\\y\\z\\t \end{bmatrix}= \begin{bmatrix} x+y+t\\x+y+z\\y+z+t\\x+z+t \end{bmatrix}$$ so, by a simple inspection, you can see that it is represented by the matrix $$L= \begin{bmatrix} 1&1&0&1\\1&1&1&0\\0&1&1&1\\1&0&1&1 \end{bmatrix}$$

This is a symmetric real matrix so we know that can be diagonalized as: $$L=SDS^{-1}$$ where $S$ is an orthogonal matrix with as columns the eigenvectors of $L$ and $D$ is the diagonal matrix with the corresponding eigenvalues on the diagonal.

So, find the eigenvalues and eigenvectors of $L$ , note that these are orthogonal, normalize them and you have the searched basis, and $D$ is the diagonal matrix that represents the given transformation in this basis.

I haven’t done this in quite some time, so this solution is probably unnecessary complicated:

We identify $\mathbb{R}^{2 \times 2}$ with $\mathbb{R}^4$ via $$\mathbb{R}^{2 \times 2} \to \mathbb{R}^4, \, \begin{pmatrix} x & y \\ z & t \end{pmatrix} \mapsto (x,y,z,t)^T.$$ (So the “default basis” you used corresponds to the standard basis $(e_1, e_2, e_3, e_4)$ of $\mathbb{R}^4$.) If we understand $L$ as a linear map $\hat{L} \colon \mathbb{R}^4 \to \mathbb{R}^4$ then $\hat{L}$ is (with respect to the standard basis on both sides) given by the matrix $$A = \begin{pmatrix} 1 & 1 & 0 & 1 \\ 1 & 1 & 1 & 0 \\ 0 & 1 & 1 & 1 \\ 1 & 0 & 1 & 1 \end{pmatrix}.$$ Also notice that the inner product on $\mathbb{R}^{2 \times 2}$ corresponds to the standard scalar product on $\mathbb{R}^4$ because $$\left\langle \begin{pmatrix} a_{11} & a_{12} \\ a_{21} & a_{22} \end{pmatrix}, \begin{pmatrix} b_{11} & b_{12} \\ b_{21} & b_{22} \end{pmatrix} \right\rangle = a_{11} b_{11} + a_{12} b_{12} + a_{21} b_{21} + a_{22} b_{22}.$$ (This also justifies called is the default inner product.) So to find an orthonormal basis of $\mathbb{R}^{2 \times 2}$ with respect to which $L$ is diagonal is the same as finding an orthogonal basis of $\mathbb{R}^4$ with respect to which $\hat{L}$ is represented a diagonal matrix.

There are now different ways to solve this problem. We will first calculate the eigenspaces of $\hat{L}$; because $A$ is symmetric we know that $\hat{L}$ is diagonalizable. Then we will use the following fact:

Proposition: Let $S \in \mathbb{R}^{n \times n}$ be symmetric and $x,y \in \mathbb{R}^n$ eigenvalues of $S$ to eigenvalues $\lambda \neq \mu$. Then $x$ and $y$ are orthogonal.

Proof: Notice that \begin{align*} \lambda \langle x,y \rangle &= \langle \lambda x, y \rangle = \langle Ax, y \rangle = (Ax)^T y = x^T A^T y = x^T A y \\ &= \langle x, A y \rangle = \langle x, \mu y \rangle = \mu \langle x, y \rangle. \end{align*} Because $\lambda \neq \mu$ it follows that $\langle x,y \rangle = 0$.

So the eigenspaces of different eigenvalues are orthogonal to each other. Therefore we can compute for each eigenspace an orthonormal basis and them put them together to get one of $\mathbb{R}^4$; then each basis vectors will in particular be an eigenvectors $\hat{L}$.

By some lengthy calculation it can be shown that the characteristic polynomial of $A$ is given by $$\chi_A(t) = t^4 - 4 t^3 + 2 t^2 + 4t - 3.$$ It is easy to guess the roots $1$ and $-1$, so we can factor $\chi_A$ and get $$\chi_A(t) = (t-1)^2 (t+1) (t-3).$$ The eigenspaces can now be calculated as usual, and we find that $$E_1 = \langle (0,-1,0,1)^T, (-1,0,1,0)^T \rangle, \; E_{-1} = \langle (-1,1,-1,1)^T \rangle, \; E_3 = \langle (1,1,1,1)^T \rangle,$$ where $E_\lambda$ denotes the eigenspace with respect to the eigenspace $\lambda$.

Next we need to find orthonormal basis for each eigenspace. We can always do this by picking some basis and then using Gram–Schmidt. But here we are pretty lucky:

We know the basis $((0,-1,0,1)^T, (-1,0,1,0)^T)$ of $E_1$. Because both basis vectors are already orthogonal to each other we only need to normalize them. So we get $b_1 = \frac{1}{\sqrt{2}}(0,-1,0,1)^T$ and $b_2 = \frac{1}{\sqrt{2}}(-1,0,1,0)^T$.

In the case of $E_{-1}$ and $E_3$ we are even luckier, as they are both one-dimensional. So here too we only need to normalize and thus get $b_3 = \frac{1}{2} (-1,1,-1,1)^T$ and $b_4 = \frac{1}{2}(1,1,1,1)^T$.

Putting these together we have now found a basis $(b_1, b_2, b_3, b_4)$ of $\mathbb{R}^4$ given by $$b_1 = \frac{1}{\sqrt{2}} \begin{pmatrix} 0 \\ -1 \\ 0 \\ 1 \end{pmatrix}, \; b_2 = \frac{1}{\sqrt{2}} \begin{pmatrix} -1 \\ 0 \\ 1 \\ 0 \end{pmatrix}, \; b_3 = \frac{1}{2} \begin{pmatrix} -1 \\ 1 \\ -1 \\ 1 \end{pmatrix}, \; b_4 = \frac{1}{2} \begin{pmatrix} 1 \\ 1 \\ 1 \\ 1 \end{pmatrix},$$ which is orthonormal and cosists of eigenvectors of $\hat{L}$. The corresponding $2 \times 2$ matrices are \begin{align*} B_1 &= \frac{1}{\sqrt{2}} \begin{pmatrix} 0 & -1 \\ 0 & 1 \end{pmatrix}, & B_2 &= \frac{1}{\sqrt{2}} \begin{pmatrix} -1 & 0 \\ 1 & 0 \end{pmatrix}, \\ B_3 &= \frac{1}{2} \begin{pmatrix} -1 & 1 \\ -1 & 1 \end{pmatrix}, & B_4 &= \frac{1}{2} \begin{pmatrix} 1 & 1 \\ 1 & 1 \end{pmatrix}. \end{align*}

Hint: Find the eigenvalues and eigenvectors for this map. Choose the eigenvectors in such a way that you obtain an orthonormal basis. Then find the matrix representation of your map with respect to this basis.