# Orthogonal Bases

Let $V$ be a vector space with a positive definite scalar product $\langle\ ,\ \rangle$. A basis $\{v_1,\cdots,v_n\}$ of $V$ is said to be orthogonal if $\langle v_i,v_j\rangle=0$ if $i\ne j$. In addition, if $||v_i||=1$ for all $i=1,\cdots,n$, then the basis is said to be orthonormal.

Example. $E_1,\cdots,E_n$ of $\mathbb{R}^n$ form an orthonormal basis of $\mathbb{R}^n$.

Why having a orthonormal basis is big deal? To answer this question, let us suppose that $e_1,\cdots,e_n$ is an orthonormal basis of a vector space $V$. Let $v,w\in V$. Then
\begin{align*}
v&=v_1e_1+\cdots+v_ne_n,\\
w&=w_1e_1+\cdots+w_ne_n.
\end{align*}
Since $\langle e_i,e_j\rangle=\delta_{ij}$,
$$\langle v,w\rangle=v_1w_1+\cdots+v_nw_n=v\cdot w.$$
Hence, once an orthonormal basis is given, the scalar product $\langle\ ,\ \rangle$ is identified with the dot product. Next question is then, can we always come up with an orthonormal basis? The answer is affirmative. Given a basis, we can construct a new basis which is orthonormal through a process called the Gram-Schmidt orthogonalization process. Here is how it works. Let $w_1,\cdots,w_n$ be a basis of a vector space $V$. Let $v_1=w_1$ and
$$v_2=w_2-\frac{\langle w_2,v_1\rangle}{\langle v_1,v_1\rangle}v_1.$$
Then $v_2$ is perpendicular to $v_1$. Note that if $w_2$ is already perpendicular to $v_1=w_1$, then $v_2=w_2$.

Gram-Schmidt Orthogonalization Process

Let
$$v_3=w_3-\frac{\langle w_3,v_1\rangle}{\langle v_1,v_1\rangle}v_1-\frac{\langle w_3,v_2\rangle}{\langle v_2,v_2\rangle}v_2.$$
Then $v_3$ is perpendicular both $v_1$ and $v_2$ as seen in the following figure.
Continuing this process, we have
$$v_n=w_n-\frac{\langle w_n,v_1\rangle}{\langle v_1,v_1\rangle}v_1-\cdots-\frac{\langle w_n,v_{n-1}\rangle}{\langle v_{n-1},v_{n-1}\rangle}v_{n-1}$$
and $v_1,\cdots,v_n$ are mutually perpendicular.

Gram-Schmidt Orthogonalization Process

Therefore, we have the following theorem holds.

Theorem. Let $V\ne\{O\}$ be a finite dimensional vector space with a positive definite scalar product. Then $V$ has an orthonormal basis.

Example. Find an orthonormal basis for the vector space generated by
$$A=(1,1,0,1),\ B=(1,-2,0,0),\ C=(1,0,-1,2).$$
Here the scalar product is the dot product.

Solution. Let
\begin{align*}
A’&=A,\\
B’&=B-\frac{B\cdot A’}{A’\cdot A’}A’\\
&=\frac{1}{3}(4,-5,0,1),\\
C’&=C-\frac{C\cdot A’}{A’\cdot A’}-\frac{C\cdot B’}{B’\cdot B’}B’\\
&=\frac{1}{7}(-4,-2,-7,6).
\end{align*}
Then $A’,B’,C’$ is an orthogonal basis. We obtain an orthonormal basis by normilizing each basis member:
\begin{align*}
\frac{A’}{||A’||}&=\frac{1}{\sqrt{3}}(1,1,0,1),\\
\frac{B’}{||B’||}&=\frac{1}{\sqrt{42}}(4,-5,0,1),\\
\frac{C’}{||C’||}&=\frac{1}{\sqrt{105}}(-4,-2,-7,6).
\end{align*}

Theorem. Let $V$ be a vector space of dimension $n$ with a positive definite scalar product $\langle\ ,\ \rangle$. Let $\{w_1,\cdots,w_r,u_1,\cdots,u_s\}$ with $r+s=n$ be an orthonormal basis of $V$. Let $W$ be a subspace generated by $w_1,\cdots,w_r$ and let $U$ be a subspace generated by $u_1,\cdots,u_s$. Then $U=W^{\perp}$ and $\dim V=\dim W+\dim W^{\perp}$.