# A calculus of the absurd

##### 22.7.3 Orthogonality

Hopefully you know what perpendicular means. We would like to generalise this notion to a more abstract setting; we can say that

• Definition 22.7.1 Let $$\textsf {V}$$ be an inner product space over a field $$\textsf {F}$$, then two vectors are orthogonal if and only if their inner product is zero; that is if we let $$x, y \in \textsf {V}$$

$$\langle x, y \rangle = 0 \iff \text { x and y are orthogonal}$$

We can denote this as $$x \bot y$$ (read “$$x$$ and $$y$$ are orthogonal”).

This is not the most exciting definition, but it is a very useful one!

• Example 22.7.1 Consider the vector space $$\textsf {V} = \mathbb {R}^2$$. The two vectors

$$\begin{pmatrix} 1 \\ 0 \end {pmatrix}, \begin{pmatrix} 0 \\ 1 \end {pmatrix}$$

are orthogonal with respect to the dot product.

We can just apply the definition, recall that

\begin{align} \begin{pmatrix} 1 \\ 0 \end {pmatrix} \cdot \begin{pmatrix} 0 \\ 1 \end {pmatrix} &= 1 \times 0 + 0 \times 1 \\ &= 0 \end{align}

And therefore, the two vectors are orthogonal.