
# 9.3 Orthogonality

Using the inner product, we can now define the notion of orthogonality, prove that the Pythagorean theorem holds in any inner product space, and use the Cauchy-Schwarz inequality to prove the triangle inequality. In particular, this will show that $$\norm{v}=\sqrt{\inner{v}{v}}$$ does indeed define a norm.

Definition 9.3.1.  Two vectors $$u,v\in V$$ are orthogonal (denoted $$u\bot v$$) if $$\inner{u}{v}=0$$.

Note that the zero vector is the only vector that is orthogonal to itself. In fact, the zero vector is orthogonal to every vector $$v\in V$$.

Theorem 9.3.2. (Pythagorean Theorem). If $$u,v\in V$$, an inner product space,  with $$u\bot v$$, then $$\norm{\cdot}$$ defined by $$\norm{v}:=\sqrt{\inner{v}{v}}$$ obeys

$\norm{u+v}^2 = \norm{u}^2 + \norm{v}^2.$

Proof.  Suppose $$u,v\in V$$ such that $$u\bot v$$. Then
\begin{equation*}
\begin{split}
\norm{u+v}^2 &= \inner{u+v}{u+v}
= \norm{u}^2 + \norm{v}^2 + \inner{u}{v} + \inner{v}{u}\\
&= \norm{u}^2 + \norm{v}^2.
\end{split}
\end{equation*}

Note that the converse of the Pythagorean Theorem holds for real vector spaces since, in that case, $$\inner{u}{v}+\inner{v}{u}=2\mathrm{Re} \inner{u}{v} =0$$.

Given two vectors $$u,v\in V$$ with $$v\neq 0$$, we can uniquely decompose $$u$$ into two pieces: one piece parallel to $$v$$ and one piece orthogonal to $$v$$. This is called an orthogonal decomposition. More precisely, we have
\begin{equation*}
u=u_1+u_2,
\end{equation*}
where $$u_1=a v$$ and $$u_2\bot v$$ for some scalar $$a \in \mathbb{F}$$. To obtain such a decomposition, write $$u_2=u-u_1=u-av$$. Then, for $$u_2$$ to be
orthogonal to $$v$$, we need
\begin{equation*}
0 = \inner{u-av}{v} = \inner{u}{v} - a\norm{v}^2.
\end{equation*}
Solving for $$a$$ yields $$a=\inner{u}{v}/\norm{v}^2$$ so that
\label{eq:orthogonal decomp}
u=\frac{\inner{u}{v}}{\norm{v}^2} v + \left( u-\frac{\inner{u}{v}}{\norm{v}^2} v \right). \tag{9.3.1}

This decomposition is particularly useful since it allows us to provide a simple proof for the Cauchy-Schwarz inequality.

Theorem 9.3.3 (Cauchy-Schwarz inequality). Given any $$u,v\in V$$, we have

$|\inner{u}{v}| \le \norm{u} \norm{v}.$

Furthermore, equality holds if and only if $$u$$ and $$v$$ are linearly dependent, i.e., are scalar multiples of each other.

Proof. If $$v=0$$, then both sides of the inequality are zero. Hence, assume that $$v\neq 0$$, and consider the orthogonal decomposition
\begin{equation*}
u = \frac{\inner{u}{v}}{\norm{v}^2} v + w
\end{equation*}
where $$w\bot v$$. By the Pythagorean theorem, we have
\begin{equation*}
\norm{u}^2 = \left\| \frac{\inner{u}{v}}{\norm{v}^2}v \right\|^2 + \norm{w}^2
= \frac{|\inner{u}{v}|^2}{\norm{v}^2} + \norm{w}^2 \ge \frac{|\inner{u}{v}|^2}{\norm{v}^2}.
\end{equation*}
Multiplying both sides by $$\norm{v}^2$$ and taking the square root then yields the Cauchy-Schwarz inequality.
Note that we get equality in the above arguments if and only if $$w=0$$. But, by Equation (9.3.1), this means that $$u$$ and $$v$$ are linearly dependent.

The Cauchy-Schwarz inequality has many different proofs. Here is another one.

Alternate Proof of Theorem 9.3.3. Given $$u,v\in V$$, consider the norm square of the vector $$u+r e^{i\theta}v:$$
\begin{equation*}
0 \le \norm{u+re^{i\theta}v}^2
= \norm{u}^2 + r^2 \norm{v}^2 + 2 \mathrm{Re} (r e^{i\theta} \inner{u}{v}).
\end{equation*}
Since $$\inner{u}{v}$$ is a complex number, one can choose $$\theta$$ so that $$e^{i \theta} \inner{u}{v}$$ is real. Hence, the right hand side is a parabola $$a r^2 +b r +c$$ with real coefficients. It will lie above the real axis, i.e. $$ar^2+br+c\ge 0$$, if it does not have any real solutions for $$r$$. This is the case when the discriminant satisfies $$b^2-4ac\le 0$$. In our case this means
\begin{equation*}
4 | \inner{u}{v} |^2 -4 \norm{u}^2 \norm{v}^2\le 0.
\end{equation*}

Moreover, equality only holds if $$r$$ can be chosen such that $$u+r e^{i\theta} v =0$$, which means that $$u$$ and $$v$$ are scalar multiples.

Now that we have proven the Cauchy-Schwarz inequality, we are finally able to verify the triangle inequality.  This is the final step in showing that $$\norm{v}=\sqrt{\inner{v}{v}}$$ does indeed define a norm. We illustrate the triangle inequality in Figure 9.3.1.

Figure 9.3.1: The Triangle Inequality in $$\mathbb{R^2}$$

Theorem 9.3.4. (Triangle Inequality).  For all $$u,v\in V$$ we have

$\norm{u+v} \le \norm{u} + \norm{v}.$

Proof. By a straightforward calculation, we obtain
\begin{equation*}
\begin{split}
\norm{u+v}^2 &= \inner{u+v}{u+v} = \inner{u}{u} + \inner{v}{v} +\inner{u}{v} + \inner{v}{u}\\
&= \inner{u}{u} + \inner{v}{v} + \inner{u}{v} +\overline{\inner{u}{v}}
= \norm{u}^2 + \norm{v}^2 +2 \mathrm{Re} \inner{u}{v}.
\end{split}
\end{equation*}
Note that $$\mathrm{Re}\inner{u}{v}\le |\inner{u}{v}|$$ so that, using the Cauchy-Schwarz inequality, we obtain
\begin{equation*}
\norm{u+v}^2 \le \norm{u}^2 + \norm{v}^2 + 2\norm{u}\norm{v} = (\norm{u}+\norm{v})^2.
\end{equation*}
Taking the square root of both sides now gives the triangle inequality.

Remark 9.3.5. Note that equality holds for the triangle inequality if and only if $$v=ru$$ or $$u=rv$$ for some $$r\ge 0$$. Namely, equality in the proof happens only if $$\inner{u}{v} =\norm{u}\norm{v}$$, which is equivalent to $$u$$ and $$v$$ being scalar multiples of one another.

Theorem 9.3.6. (Parallelogram Law). Given any $$u,v\in V$$, we have
$\norm{u+v}^2 + \norm{u-v}^2 = 2(\norm{u}^2 + \norm{v}^2).$

Proof.  By direct calculation,
\begin{equation*}
\begin{split}
\norm{u+v}^2 + \norm{u-v}^2 &= \inner{u+v}{u+v} + \inner{u-v}{u-v}\\
&= \norm{u}^2 + \norm{v}^2 + \inner{u}{v} + \inner{v}{u} + \norm{u}^2 + \norm{v}^2
- \inner{u}{v} -\inner{v}{u}\\
&=2(\norm{u}^2 + \norm{v}^2).
\end{split}
\end{equation*}

Remark 9.3.7. We illustrate the parallelogram law in Figure 9.3.2.

Figure 9.3.2: The Parallelogram Law in $$\mathbb{R^2}$$

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