handouts/Advanced/Linear Maps/parts/3 matrices.tex

\section{Matrices}

\definition{}
A \textit{matrix} is a two-dimensional array of numbers: \\
$$
A =
\begin{bmatrix}
	1 & 2 & 3 \\
	4 & 5 & 6
\end{bmatrix}
$$
The above matrix has two rows and three columns. It is thus a $2 \times 3$ matrix.

\problem{}
Draw a $3 \times 2$ matrix.

\vfill

\definition{Matrices as Transformations}
We can define the \say{product\footnotemark{}} of a matrix $A$ and a vector $v$:
\footnotetext{This is an uncommon word to use in this context. You will soon see why.}
$$
Av =
\begin{bmatrix}
	1 & 2 & 3 \\
	4 & 5 & 6
\end{bmatrix}
\begin{bmatrix}
	a \\ b \\ c
\end{bmatrix}
=
\begin{bmatrix}
	1a + 2b + 3c \\
	4a + 5b + 6c
\end{bmatrix}
$$
Look closely. Each element of the resulting $2 \times 1$ matrix is the dot product of a row of $A$ with $v$:

$$
Av =
\begin{bmatrix}
	\text{---} a_1 \text{---} \\
	\text{---} a_2 \text{---}
\end{bmatrix}
\begin{bmatrix}
	| \\
	v \\
	| \\
\end{bmatrix}
=
\begin{bmatrix}
	r_1v \\
	r_2v
\end{bmatrix}
$$

Naturally, a vector can only be multiplied by a matrix if the number of rows in the vector equals the number of columns in the matrix.

\problem{}
Compute the following \say{product}:

$$
\begin{bmatrix}
	2 & 9 \\
	7 & 5 \\
	3 & 4
\end{bmatrix}
\begin{bmatrix}
	5 \\ 3
\end{bmatrix}
$$

\vfill
\pagebreak


\generic{Remark:}
It is a bit more interesting to think of matrix-vector multiplication in the following way: \\

\begin{minipage}[t]{0.48\textwidth}\vspace{0pt}
	\begin{center}
		The problem:
		\vspace{2mm}

		$$
		\begin{bmatrix}
			2 & 9 \\
			7 & 5 \\
			3 & 4
		\end{bmatrix}
		\begin{bmatrix}
			5 \\ 3
		\end{bmatrix}
		=
		\begin{bmatrix}
			37 \\ 50 \\ 27
		\end{bmatrix}
		$$
	\end{center}
\end{minipage}%
\hfill
\begin{minipage}[t]{0.48\textwidth}\vspace{0pt}
	\begin{center}
	Top-input, right-output:
	\vspace{2mm}

	\begin{tikzpicture}[>=stealth,thick,baseline]
		\matrix [
			matrix of math nodes,
			left delimiter={[},
			right delimiter={]}
		] (A) {
			2 & 9 \\
			7 & 5 \\
			3 & 4 \\
		};

		\node[
			fit=(A-1-1)(A-1-1),
			inner xsep=0mm,inner ysep=3mm,
			label=above:5
		] (L) {};
		\draw[->, gray] (L.north) -- ([yshift=0mm]A-1-1.north);

		\node[
			fit=(A-1-2)(A-1-2),
			inner xsep=0mm,inner ysep=3mm,
			label=above:3
		] (R) {};
		\draw[->, gray] (R.north) -- ([yshift=0mm]A-1-2.north);


		\node[
			fit=(A-1-2)(A-1-2),
			inner xsep=8mm,inner ysep=0mm,
			label=right:{$10 + 27 = 37$}
		](Y) {};
		\draw[->, gray] ([xshift=3mm]A-1-2.east) -- (Y);

		\node[
			fit=(A-2-2)(A-2-2),
			inner xsep=8mm,inner ysep=0mm,
			label=right:{$35 + 15 = 50$}
		](H) {};
		\draw[->, gray] ([xshift=3mm]A-2-2.east) -- (H);

		\node[
			fit=(A-3-2)(A-3-2),
			inner xsep=8mm,inner ysep=0mm,
			label=right:{$15 + 12 = 27$}
		](N) {};
		\draw[->, gray] ([xshift=3mm]A-3-2.east) -- (N);
	\end{tikzpicture}
	\end{center}
\end{minipage}%

\vspace{2mm}

Be aware that this is only a model for intuition. \\
Make sure you understand the dot product definition on the previous page.

\vspace{5mm}

\theorem{}<thebigtheorem>
Any linear map $T: \mathbb{R}^n \to \mathbb{R}^m$ can be written as an $n \times m$ matrix. \\
Conversely, every $n \times m$ matrix represents a liner map $T: \mathbb{R}^n \to \mathbb{R}^m$ \\

\vspace{2mm}

In other words, \textbf{matrices are linear transformations}. \\
If you only learn only one thing today, this should be it.

\vfill

\problem{}<prooffwd>
Show that the transformation $T: \mathbb{R}^n \to \mathbb{R}^m$ defined by $T(v) = Av$ is linear. \\
Before you start, answer the following questions:
\begin{itemize}
	\item What is $A$?
	\item What is $v$?
	\item What are their sizes?
\end{itemize}

\vfill

\problem{}<proofback>
Show that any linear transformation can be written as a matrix.

\vfill
\pagebreak


\problem{}
Does \ref{thebigtheorem} hold in arbitrary vector spaces? \\
Repeat \ref{prooffwd} and \ref{proofback} using only axioms.

\vfill
\pagebreak