### Section O Orthogonality

In this section we define a couple more operations with vectors, and prove a few theorems. At first blush these definitions and results will not appear central to what follows, but we will make use of them at key points in the remainder of the course (such as Section MINM, Section OD). Because we have chosen to use $\complexes$ as our set of scalars, this subsection is a bit more, uh, … complex than it would be for the real numbers. We will explain as we go along how things get easier for the real numbers ${\mathbb R}$. If you have not already, now would be a good time to review some of the basic properties of arithmetic with complex numbers described in Section CNO. With that done, we can extend the basics of complex number arithmetic to our study of vectors in $\complex{m}$.

#### Subsection CAV Complex Arithmetic and Vectors

We know how the addition and multiplication of complex numbers is employed in defining the operations for vectors in $\complex{m}$ (Definition CVA and Definition CVSM). We can also extend the idea of the conjugate to vectors.

##### Definition CCCV Complex Conjugate of a Column Vector

Suppose that $\vect{u}$ is a vector from $\complex{m}$. Then the conjugate of the vector, $\conjugate{\vect{u}}$, is defined by \begin{align*} \vectorentry{\conjugate{\vect{u}}}{i} &=\conjugate{\vectorentry{\vect{u}}{i}} &&\text{$1\leq i\leq m$} \end{align*}

With this definition we can show that the conjugate of a column vector behaves as we would expect with regard to vector addition and scalar multiplication.

##### Theorem CRVA Conjugation Respects Vector Addition

Suppose $\vect{x}$ and $\vect{y}$ are two vectors from $\complex{m}$. Then \begin{equation*} \conjugate{\vect{x}+\vect{y}}=\conjugate{\vect{x}}+\conjugate{\vect{y}} \end{equation*}

##### Theorem CRSM Conjugation Respects Vector Scalar Multiplication

Suppose $\vect{x}$ is a vector from $\complex{m}$, and $\alpha\in\complexes$ is a scalar. Then \begin{equation*} \conjugate{\alpha\vect{x}}=\conjugate{\alpha}\,\conjugate{\vect{x}} \end{equation*}

These two theorems together tell us how we can “push” complex conjugation through linear combinations.

#### Subsection IP Inner products

##### Definition IP Inner Product

Given the vectors $\vect{u},\,\vect{v}\in\complex{m}$ the *inner product* of $\vect{u}$ and $\vect{v}$ is the scalar quantity in $\complex{\null}$,
\begin{equation*}
\innerproduct{\vect{u}}{\vect{v}}=
\conjugate{\vectorentry{\vect{u}}{1}}\vectorentry{\vect{v}}{1}+
\conjugate{\vectorentry{\vect{u}}{2}}\vectorentry{\vect{v}}{2}+
\conjugate{\vectorentry{\vect{u}}{3}}\vectorentry{\vect{v}}{3}+
\cdots+
\conjugate{\vectorentry{\vect{u}}{m}}\vectorentry{\vect{v}}{m}
=
\sum_{i=1}^{m}\conjugate{\vectorentry{\vect{u}}{i}}\vectorentry{\vect{v}}{i}
\end{equation*}

This operation is a bit different in that we begin with two vectors but produce a scalar. Computing one is straightforward.

##### Example CSIP Computing some inner products

In the case where the entries of our vectors are all real numbers (as in the second part of Example CSIP), the computation of the inner product may look familiar and be known to you as a *dot product* or *scalar product*. So you can view the inner product as a generalization of the scalar product to vectors from $\complex{m}$ (rather than ${\mathbb R}^m$).

Note that we have chosen to conjugate the entries of the *first* vector listed in the inner product, while it is almost equally feasible to conjugate entries from the *second* vector instead. In particular, prior to Version 2.90, we did use the latter definition, and this has now changed to the former, with resulting adjustments propogated up through Section CB (only). However, conjugating the first vector leads to much nicer formulas for certain matrix decompositions and also shortens some proofs.

There are several quick theorems we can now prove, and they will each be useful later.

##### Theorem IPVA Inner Product and Vector Addition

Suppose $\vect{u},\,\vect{v},\,\vect{w}\in\complex{m}$. Then

- $\innerproduct{\vect{u}+\vect{v}}{\vect{w}}=\innerproduct{\vect{u}}{\vect{w}}+\innerproduct{\vect{v}}{\vect{w}}$
- $\innerproduct{\vect{u}}{\vect{v}+\vect{w}}=\innerproduct{\vect{u}}{\vect{v}}+\innerproduct{\vect{u}}{\vect{w}}$

##### Theorem IPSM Inner Product and Scalar Multiplication

Suppose $\vect{u},\,\vect{v}\in\complex{m}$ and $\alpha\in\complex{\null}$. Then

- $\innerproduct{\alpha\vect{u}}{\vect{v}}=\conjugate{\alpha}\innerproduct{\vect{u}}{\vect{v}}$
- $\innerproduct{\vect{u}}{\alpha\vect{v}}=\alpha\innerproduct{\vect{u}}{\vect{v}}$

##### Theorem IPAC Inner Product is Anti-Commutative

Suppose that $\vect{u}$ and $\vect{v}$ are vectors in $\complex{m}$. Then $\innerproduct{\vect{u}}{\vect{v}}=\conjugate{\innerproduct{\vect{v}}{\vect{u}}}$.

#### Subsection N Norm

If treating linear algebra in a more geometric fashion, the length of a vector occurs naturally, and is what you would expect from its name. With complex numbers, we will define a similar function. Recall that if $c$ is a complex number, then $\modulus{c}$ denotes its modulus (Definition MCN).

##### Definition NV Norm of a Vector

The *norm* of the vector $\vect{u}$ is the scalar quantity in $\complex{\null}$
\begin{equation*}
\norm{\vect{u}}=
\sqrt{
\modulus{\vectorentry{\vect{u}}{1}}^2+
\modulus{\vectorentry{\vect{u}}{2}}^2+
\modulus{\vectorentry{\vect{u}}{3}}^2+
\cdots+
\modulus{\vectorentry{\vect{u}}{m}}^2
}
=
\sqrt{\sum_{i=1}^{m}\modulus{\vectorentry{\vect{u}}{i}}^2}
\end{equation*}

Computing a norm is also easy to do.

##### Example CNSV Computing the norm of some vectors

Notice how the norm of a vector with real number entries is just the length of the vector. Inner products and norms are related by the following theorem.

##### Theorem IPN Inner Products and Norms

Suppose that $\vect{u}$ is a vector in $\complex{m}$. Then $\norm{\vect{u}}^2=\innerproduct{\vect{u}}{\vect{u}}$.

When our vectors have entries only from the real numbers Theorem IPN says that the dot product of a vector with itself is equal to the length of the vector squared.

##### Theorem PIP Positive Inner Products

Suppose that $\vect{u}$ is a vector in $\complex{m}$. Then $\innerproduct{\vect{u}}{\vect{u}}\geq 0$ with equality if and only if $\vect{u}=\zerovector$.

Notice that Theorem PIP contains *three* implications:
\begin{align*}
\vect{u}\in\complex{m}&\Rightarrow\innerproduct{\vect{u}}{\vect{u}}\geq 0\\
\vect{u}=\zerovector&\Rightarrow\innerproduct{\vect{u}}{\vect{u}}=0\\
\innerproduct{\vect{u}}{\vect{u}}=0&\Rightarrow\vect{u}=\zerovector
\end{align*}

The results contained in Theorem PIP are summarized by saying “the inner product is *positive definite*.”

##### Sage EVIC Exact Versus Inexact Computations

##### Sage CNIP Conjugates, Norms and Inner Products

#### Subsection OV Orthogonal Vectors

“Orthogonal” is a generalization of “perpendicular.” You may have used mutually perpendicular vectors in a physics class, or you may recall from a calculus class that perpendicular vectors have a zero dot product. We will now extend these ideas into the realm of higher dimensions and complex scalars.

##### Definition OV Orthogonal Vectors

A pair of vectors, $\vect{u}$ and $\vect{v}$, from $\complex{m}$ are *orthogonal* if their inner product is zero, that is, $\innerproduct{\vect{u}}{\vect{v}}=0$.

##### Example TOV Two orthogonal vectors

We extend this definition to whole sets by requiring vectors to be pairwise orthogonal. Despite using the same word, careful thought about what objects you are using will eliminate any source of confusion.

##### Definition OSV Orthogonal Set of Vectors

Suppose that $S=\set{\vectorlist{u}{n}}$ is a set of vectors from $\complex{m}$. Then $S$ is an *orthogonal set* if every pair of different vectors from $S$ is orthogonal, that is $\innerproduct{\vect{u}_i}{\vect{u}_j}=0$ whenever $i\neq j$.

We now define the prototypical orthogonal set, which we will reference repeatedly.

##### Definition SUV Standard Unit Vectors

Let $\vect{e}_j\in\complex{m}$, $1\leq j\leq m$ denote the column vectors defined by \begin{align*} \vectorentry{\vect{e}_j}{i} &= \begin{cases} 0&\text{if $i\neq j$}\\ 1&\text{if $i=j$} \end{cases} \end{align*}

Then the set
\begin{align*}
\set{\vectorlist{e}{m}}&=\setparts{\vect{e}_j}{1\leq j\leq m}
\end{align*}
is the set of *standard unit vectors* in $\complex{m}$.

Notice that $\vect{e}_j$ is identical to column $j$ of the $m\times m$ identity matrix $I_m$ (Definition IM) and is a pivot column for $I_m$, since the identity matrix is in reduced row-echelon form. These observations will often be useful. We will reserve the notation $\vect{e}_i$ for these vectors. It is not hard to see that the set of standard unit vectors is an orthogonal set.

##### Example SUVOS Standard Unit Vectors are an Orthogonal Set

##### Example AOS An orthogonal set

So far, this section has seen lots of definitions, and lots of theorems establishing un-surprising consequences of those definitions. But here is our first theorem that suggests that inner products and orthogonal vectors have some utility. It is also one of our first illustrations of how to arrive at linear independence as the conclusion of a theorem.

##### Theorem OSLI Orthogonal Sets are Linearly Independent

Suppose that $S$ is an orthogonal set of nonzero vectors. Then $S$ is linearly independent.

#### Subsection GSP Gram-Schmidt Procedure

The Gram-Schmidt Procedure is really a theorem. It says that if we begin with a linearly independent set of $p$ vectors, $S$, then we can do a number of calculations with these vectors and produce an orthogonal set of $p$ vectors, $T$, so that $\spn{S}=\spn{T}$. Given the large number of computations involved, it is indeed a procedure to do all the necessary computations, and it is best employed on a computer. However, it also has value in proofs where we may on occasion wish to replace a linearly independent set by an orthogonal set.

This is our first occasion to use the technique of “mathematical induction” for a proof, a technique we will see again several times, especially in Chapter D. So study the simple example described in Proof Technique I first.

##### Theorem GSP Gram-Schmidt Procedure

Suppose that $S=\set{\vectorlist{v}{p}}$ is a linearly independent set of vectors in $\complex{m}$. Define the vectors $\vect{u}_i$, $1\leq i\leq p$ by \begin{equation*} \vect{u}_i=\vect{v}_i -\frac{\innerproduct{\vect{u}_1}{\vect{v}_i}}{\innerproduct{\vect{u}_1}{\vect{u}_1}}\vect{u}_1 -\frac{\innerproduct{\vect{u}_2}{\vect{v}_i}}{\innerproduct{\vect{u}_2}{\vect{u}_2}}\vect{u}_2 -\frac{\innerproduct{\vect{u}_3}{\vect{v}_i}}{\innerproduct{\vect{u}_3}{\vect{u}_3}}\vect{u}_3 -\cdots -\frac{\innerproduct{\vect{u}_{i-1}}{\vect{v}_i}}{\innerproduct{\vect{u}_{i-1}}{\vect{u}_{i-1}}}\vect{u}_{i-1} \end{equation*}

Let $T=\set{\vectorlist{u}{p}}$. Then $T$ is an orthogonal set of nonzero vectors, and $\spn{T}=\spn{S}$.

##### Example GSTV Gram-Schmidt of three vectors

One final definition related to orthogonal vectors.

##### Definition ONS OrthoNormal Set

Suppose $S=\set{\vectorlist{u}{n}}$ is an orthogonal set of vectors such that $\norm{\vect{u}_i}=1$ for all $1\leq i\leq n$. Then $S$ is an *orthonormal* set of vectors.

Once you have an orthogonal set, it is easy to convert it to an orthonormal set — multiply each vector by the reciprocal of its norm, and the resulting vector will have norm 1. This scaling of each vector will not affect the orthogonality properties (apply Theorem IPSM).

##### Example ONTV Orthonormal set, three vectors

##### Example ONFV Orthonormal set, four vectors

We will see orthonormal sets again in Subsection MINM.UM. They are intimately related to unitary matrices (Definition UM) through Theorem CUMOS. Some of the utility of orthonormal sets is captured by Theorem COB in Subsection B.OBC. Orthonormal sets appear once again in Section OD where they are key in orthonormal diagonalization.