containing the set $\set{x,y,z}$.
If $R$ has a multiplicative identity (that is, a unit) element,
- then that element is denoted by $\unit{R}$.
+ then that element is denoted by $\unit{R}$. Its additive identity
+ element is $\zero{R}$.
\end{section}
\begin{section}{Algorithm}
superscript when that superscript would be one: $\Nn[1]$,
$\Zn[1]$, $\Qn[1]$, $\Rn[1]$, $\Cn[1]$. However, if the
superscript is (say) two, then it appears: $\Nn[2]$, $\Zn[2]$,
- $\Qn[2]$, $\Rn[2]$, $\Cn[2]$. Finally, we have the four standard
- types of intervals in $\Rn[1]$,
+ $\Qn[2]$, $\Rn[2]$, $\Cn[2]$. The symbols $\Fn[1]$, $\Fn[2]$,
+ et cetera, are available for use with a generic field.
+
+ Finally, we have the four standard types of intervals in $\Rn[1]$,
%
\begin{align*}
\intervaloo{a}{b} &= \setc{ x \in \Rn[1]}{ a < x < b },\\
\end{itemize}
\end{section}
+ \begin{section}{Hurwitz}
+ Here lies the Hurwitz algebras, like the quaternions
+ $\quaternions$ and octonions $\octonions$.
+ \end{section}
+
\begin{section}{Linear algebra}
The absolute value of $x$ is $\abs{x}$, or its norm is
$\norm{x}$. The inner product of $x$ and $y$ is $\ip{x}{y}$ and
matrices $A$ and $B$ is $\kp{A}{B}$. The adjoint of the operator
$L$ is $\adjoint{L}$, or if it's a matrix, then its transpose is
$\transpose{L}$. Its trace is $\trace{L}$, and its spectrum---the
- set of its eigenvalues---is $\spectrum{L}$. Another matrix-specific
- concept is the Moore-Penrose pseudoinverse of $L$, denoted by
- $\pseudoinverse{L}$. Finally, the rank of a matrix $L$ is
- $\rank{L}$. As far as matrix spaces go, we have the $n$-by-$n$
+ set of its eigenvalues---is $\spectrum{L}$. Another
+ matrix-specific concept is the Moore-Penrose pseudoinverse of $L$,
+ denoted by $\pseudoinverse{L}$. Finally, the rank of a matrix $L$
+ is $\rank{L}$. As far as matrix spaces go, we have the $n$-by-$n$
real-symmetric and complex-Hermitian matrices $\Sn$ and $\Hn$
respectively; however $\Sn[1]$ and $\Hn[1]$ do not automatically
simplify because the ``$n$'' does not indicate the arity of a
- Cartesian product in this case.
+ Cartesian product in this case. A handy way to represent the
+ matrix $A \in \Rn[n \times n]$ whose only non-zero entries are on
+ the diagonal is $\diag{\colvec{A_{11},A_{22},\ldots,A_{nn}}}$.
The span of a set $X$ is $\spanof{X}$, and its codimension is
$\codim{X}$. The projection of $X$ onto $V$ is $\proj{V}{X}$. The
instead.
If you want to solve a system of equations, try Cramer's
- rule~\cite{ehrenborg}.
+ rule~\cite{ehrenborg}. Or at least the reduced row-echelon form of
+ the matrix, $\rref{A}$.
The direct sum of $V$ and $W$ is $\directsum{V}{W}$, of course,
but what if $W = V^{\perp}$? Then we wish to indicate that fact by