# 1.4 Orthogonality

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Definition of orthogonal vectors, sets, and subspaces; properties and benefits.

Recall that a set $S$ is a basis for a subspace $X$ if $\left[S\right]=X$ and $S$ has linearly independent elements. If $S$ is a basis for $X$ then each $x\in X$ is uniquely determined by $\left({a}_{1},{a}_{2},...,{a}_{n}\right)$ such that ${\sum }_{i=1}^{n}{a}_{i}{s}_{i}=x$ . In this sense, we could operate either with $x$ itself or with the vector $a=\left[{a}_{1},...{a}_{n}\right]={R}^{n}$ . One would wonder then whether particular operations can be performed with a representation $a$ instead of the original vector $x$ .

Example 1 Assume $x,y\in X$ have representations $a,b\in {R}^{n}$ in a basis for $X$ . Can we say that $〈x,,,y〉=〈a,,,b〉$ ?

For the particular example of $X={L}_{2}\left[0,1\right]$ , $S=\left\{1,t,{t}^{2}\right\}$ so that $\left[S\right]=Q$ , the set of all quadratic functions supported on $\left[0,1\right]$ . Pick $x=2+t+{t}^{2}$ and $y=1+2t+3{t}^{2}$ . One can see then that if we label ${s}_{1}=1$ , ${s}_{2}=t$ , ${s}_{3}={t}^{2}$ , then the coefficient vectors for $x$ and $y$ are $a=\left[2\phantom{\rule{3.33333pt}{0ex}}1\phantom{\rule{3.33333pt}{0ex}}1\right]$ and $b=\left[1\phantom{\rule{3.33333pt}{0ex}}2\phantom{\rule{3.33333pt}{0ex}}3\right]$ , respectively. Let us compute both inner products:

$\begin{array}{cc}\hfill 〈x,,,y〉& ={\int }_{0}^{1}x\left(t\right)y\left(t\right)dt={\int }_{0}^{1}\left(2+t+{t}^{2}\right)\left(1+2t+3{t}^{2}\right)dt=\frac{187}{20}\approx 9.35,\hfill \\ \hfill 〈a,,,b〉& =2+2+3=7.\hfill \end{array}$

Since $7\ne 9.35$ , we find that we fail to obtain the desired equivalence between vectors and their representations.

While this example was unsuccessful, simple conditions on the basis $S$ will yield this desired equivalence, plus many more useful properties.

Several definitions of orthogonality will be useful to us during the course.

Definition 1 A pair of vectors $x$ and $y$ in an inner product space are orthogonal (denoted $x\perp y$ ) if the inner product $〈x,,,y〉=0$ .

Note that 0 is immediately orthogonal to all vectors.

Definition 2 Let $X$ be an inner product space. A set of vectors $S\subseteq X$ is orthogonal if $〈x,,,y〉=0$ for all $x,y\in S,x\ne y$ .

Definition 3 Let $X$ be an inner product space. A set of vectors $S\subseteq X$ is orthonormal if $S$ is an orthogonal set and $\parallel s\parallel =\sqrt{⟨s,s⟩}=1$ for all $s\in S$ .

Definition 4 A vector $x$ in an inner product space $X$ is orthogonal to a set $S\subseteq X$ (denoted $x\perp S$ ) if $x\perp y$ for all $y\in S$ .

Definition 5 Let $X$ be an inner product space. Two sets ${S}_{1}\subseteq X$ and ${S}_{2}\subseteq X$ are orthogonal (denoted ${S}_{1}\perp {S}_{2}$ ) if $x\perp y$ for all $x\in {S}_{1}$ and $y\in {s}_{2}$ .

Definition 6 The orthogonal complement ${S}^{\perp }$ of a set $S$ is the set of all vectors that are orthogonal to $S$ .

## Benefits of orthogonality

Why is orthonormality good? For many reasons. One of them is the equivalence of inner products that we desired in a previous example. Another is that having an orthonormal basis allows us to easily find the coefficients ${a}_{1},...{a}_{n}$ of $x$ in a basis $S$ .

Example 2 Let $x\in X$ and $S$ be a basis for $X$ (i.e., $\left[S\right]=X$ ). We wish to find ${a}_{1},...{a}_{n}$ such that $x={\sum }_{i=1}^{n}{a}_{i}{s}_{i}$ . Consider the inner products

$\begin{array}{cc}\hfill 〈x,,,{s}_{i}〉& =〈\sum _{j=1}^{n},{a}_{j},{s}_{j},,,{s}_{i}〉=\sum _{j=1}^{n}{a}_{j}〈{s}_{j},,,{s}_{i}〉,\hfill \end{array}$

due to the linearity of the inner product in the first term. If $S$ is orthonormal, then we have that for $i\ne j$ $〈{s}_{j},,,{s}_{i}〉=0$ . In that case the sum above becomes

$\begin{array}{cc}\hfill 〈x,,,{s}_{i}〉& ={a}_{i}〈{s}_{i},,,{s}_{i}〉={a}_{i}{\parallel {s}_{i}\parallel }^{2}={a}_{i},\hfill \end{array}$

due to the orthonormality of $S$ . In other words, for an orthonormal basis $S$ one can find the basis coefficients as ${a}_{i}=⟨x,{s}_{i}⟩$ .

If $S$ is not orthonormal, then we can rewrite the sum above as the product of a row vector and a column vector as follows:

$〈x,,,{s}_{i}〉=\left[\begin{array}{cccc}〈{s}_{1},,,{s}_{i}〉& 〈{s}_{2},,,{s}_{i}〉& \cdots & 〈{s}_{n},,,{s}_{i}〉\end{array}\right]\left[\begin{array}{c}{a}_{1}\\ {a}_{2}\\ ⋮\\ {a}_{n}\end{array}\right].$

We can then stack these equations for $i=1,...,n$ to obtain the following matrix-vector multiplication:

$\underset{\beta }{\underbrace{\left[\begin{array}{c}〈x,,,{s}_{1}〉\\ 〈x,,,{s}_{2}〉\\ ⋮\\ 〈x,,,{s}_{n}〉\end{array}\right]}}=\underset{G}{\underbrace{\left[\begin{array}{cccc}〈{s}_{1},,,{s}_{1}〉& 〈{s}_{2},,,{s}_{1}〉& \cdots & 〈{s}_{n},,,{s}_{1}〉\\ 〈{s}_{1},,,{s}_{2}〉& 〈{s}_{2},,,{s}_{3}〉& \cdots & 〈{s}_{n},,,{s}_{2}〉\\ ⋮& ⋮& \ddots & ⋮\\ 〈{s}_{1},,,{s}_{n}〉& 〈{s}_{2},,,{s}_{n}〉& \cdots & 〈{s}_{n},,,{s}_{n}〉\end{array}\right]}}\underset{a}{\underbrace{\left[\begin{array}{c}{a}_{1}\\ {a}_{2}\\ ⋮\\ {a}_{n}\end{array}\right]}}.$

The nomenclature given above provides us with the matrix equation $\beta =G·a$ , where $\beta$ and $G$ have entries ${\beta }_{i}=〈x,,,{s}_{i}〉$ and ${G}_{i,j}=〈{s}_{j},,,{s}_{i}〉$ , respectively.

Definition 7 The matrix $G$ above is called the Gram matrix (or Gramian) of the set $S$ .

In the particular case of orthonormal $S$ , it is easy to see that $G=I$ , the identity matrix, and so $a=\beta$ as given earlier. For invertible Gramians $G$ , one could compute the coefficients in vector form as $a={G}^{-1}\beta$ . For square matrices (like $G$ ), invertibility is linked to singularity.

Definition 8 A singular matrix is a non-invertible square matrix. A non-singular matrix is an invertible square matrix.

Theorem 1 A matrix is singular if $G·x=0$ for some $x\ne 0$ . A matrix is non-singular if $G·x=0$ only for $x=0$ .

The link between this notion of singularity and invertibility is straightforward: if $G$ is singular, then there is some $a\ne 0$ for which $G·a=0$ . Consider the mapping $y=G·x$ ; we would also have $y=G\left(x+a\right)$ . Since $x\ne x+a$ , one cannot “invert” the mapping provided by $G$ into $y$ .

Theorem 2 $S$ is linearly independent if and only if $G$ is non-singular (i.e. $Gx=0$ if and only if $x=0$ ).

Proof: We will prove an equivalent statement: $S$ is linearly dependent if and only if $G$ is singular, i.e., if and only if there exists a vector $x\ne 0$ such that $Gx=0$ .

$\left(⇒\right)$ We first prove that if $S$ is linearly dependent then $G$ is singular. In this case there exist a set $\left\{{a}_{i}\right\}\subseteq R$ , with at least one nonzero, such that ${\sum }_{i=1}^{n}{a}_{i}{s}_{i}=0$ . We then can write $〈{\sum }_{i=1}^{n},{a}_{i},{s}_{i},,,{s}_{j}〉=⟨0,{s}_{j}⟩=0$ for each ${s}_{j}$ . Linearity allows us to take the sum and the scalar outside the inner product:

$\sum _{i=1}^{n}{a}_{i}〈{s}_{i},,,{s}_{j}〉=0.$

We can rewrite this equation in terms of the entries of the Gram matrix as ${\sum }_{i=1}^{n}{a}_{i}{G}_{ji}=0$ . This sum, in turn, can be written as the vector inner product

$\left[\begin{array}{ccc}{G}_{j1}& \cdots & {G}_{1n}\end{array}\right]\left[\begin{array}{c}{a}_{1}\\ ⋮\\ {a}_{n}\end{array}\right]=0,$

which is true for every value of $j$ . We can therefore collect these equations into a matrix-vector product:

$\begin{array}{c}\hfill \left[\begin{array}{ccc}{G}_{11}& \cdots & {G}_{1n}\\ ⋮& \ddots & ⋮\\ {G}_{n1}& \cdots & {G}_{nn}\end{array}\right]\left[\begin{array}{c}{a}_{1}\\ ⋮\\ {a}_{n}\end{array}\right]=\left[\begin{array}{c}0\\ ⋮\\ 0\end{array}\right].\end{array}$

Therefore we have found a nonzero vector $a$ for which $Ga=0$ , and therefore $G$ is singular. Since all statements here are equalities, we can backtrack to prove the opposite direction of the theorem $\left(⇐\right)$ .

## Pythagorean theorem

There are still more nice proper ties for orthogonal sets of vectors. The next one has well-known geometric applications.

Theorem 3 (Pythagorean theorem) If $x$ and $y$ are orthogonal ( $x\perp y$ ), then ${\parallel x\parallel }^{2}+{\parallel y\parallel }^{2}={\parallel x+y\parallel }^{2}$ .

Proof:

${\parallel x+y\parallel }^{2}=⟨x+y,x+y⟩=⟨x,x⟩+⟨x,y⟩+⟨y,x⟩+⟨y,y⟩$

Because $x$ and $y$ are orthogonal, $⟨x,y⟩=⟨y,x⟩=0$ and we are left with $⟨x,x⟩={\parallel x\parallel }^{2}$ and $⟨y,y⟩={\parallel y\parallel }^{2}$ . Thus: ${\parallel x+y\parallel }^{2}={\parallel x\parallel }^{2}+{\parallel y\parallel }^{2}.$

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Source:  OpenStax, Introduction to compressive sensing. OpenStax CNX. Mar 12, 2015 Download for free at http://legacy.cnx.org/content/col11355/1.4
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