Find a 3 x 3 orthogonal matrix Find a 3x3 orthogonal matrix whose first column is $[\frac1{\sqrt{3}},{- \frac 1{\sqrt{3}}}, \frac1{\sqrt{3}}]$.
I was thinking this has to do with: ${Q}^T•Q=I_{n}$, but I still don't understand how to go about this.

You need to find an orthonormal basis of $\mathbb{R}^3$ whose first vector is the vector $v_1 = \left( \frac{1}{\sqrt{3}}, -\frac{1}{\sqrt{3}}, \frac{1}{\sqrt{3}} \right)^T$ given to you. This can be done in several ways:


*

*Complete $v_1$ arbitrary to a basis $v_1,v_2,v_3$ of $\mathbb{R}^3$ and perform Gram-Schmidt to get $v_1,v_2',v_3'$. Then you can take $Q = (v_1, v_2, v_3)$.

*Find two linearly independent vectors $v_2,v_3$ that are orthogonal to $v_1$ and perform Gram-Schmidt on them to get $v_2',v_3'$. Then you can take $Q = (v_1,v_2,v_3)$.


Let me demonstrate the second method. Since normalization is not important for orthogonality, we need to find two linearly independent vectors that are orthogonal to $(1,-1,1)^T$. Just by playing with vectors whose entries are $\pm 1$, we can get two such vectors
$$ v_2 = \begin{pmatrix} 1 \\ 1 \\ 0 \end{pmatrix}, v_3 = \begin{pmatrix} 0 \\ 1 \\ 1 \end{pmatrix}. $$
If we perform Gram-Schmidt on $(v_2,v_3)$ we get
$$ v_2' = \frac{v_2}{\| v_2 \|} = \frac{1}{\sqrt{2}} \begin{pmatrix} 1 \\ 1 \\ 0 \end{pmatrix}, \\
v_3' = \frac{v_3 - \left< v_3, v_2 \right> v_2}{\| v_3 - \left< v_3, v_2 \right> v_2 \|} = \frac{\begin{pmatrix} 0 \\ 1 \\ 1 \end{pmatrix} - \frac{1}{2} \begin{pmatrix} 1 \\ 1 \\ 0 \end{pmatrix}}{\dots} = \frac{\begin{pmatrix} -\frac{1}{2} \\ \frac{1}{2} \\ 1 \end{pmatrix}}{\| \begin{pmatrix} -\frac{1}{2} \\ \frac{1}{2} \\ 1 \end{pmatrix} \|} = \begin{pmatrix} -\frac{\sqrt{3}}{\sqrt{10}} \\ \frac{\sqrt{3}}{\sqrt{10}} \\ \frac{\sqrt{4}}{\sqrt{10}} \end{pmatrix} $$
and so we can take $Q$ to be
$$ Q = \begin{pmatrix} \frac{1}{\sqrt{3}} & \frac{1}{\sqrt{2}} & -\frac{\sqrt{3}}{\sqrt{10}} \\
-\frac{1}{\sqrt{3}} & \frac{1}{\sqrt{2}} & \frac{\sqrt{3}}{\sqrt{10}} \\
\frac{1}{\sqrt{3}} & 0 & \frac{\sqrt{4}}{\sqrt{10}} \end{pmatrix}. $$
