their tensor product is $\tp{x}{y}$. The Kronecker product of
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 $\trace{L}$. Another matrix-specific
+ $\transpose{L}$. 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-$n$
% The orthogonal projection of its second argument onto the first.
\newcommand*{\proj}[2] { \operatorname{proj}\of{#1, #2} }
+% The set of all eigenvalues of its argument, which should be either a
+% matrix or a linear operator. The sigma notation was chosen instead
+% of lambda so that lambda can be reserved to denote the ordered tuple
+% (largest to smallest) of eigenvalues.
+\newcommand*{\spectrum}[1]{\sigma\of{{#1}}}
+\ifdefined\newglossaryentry
+ \newglossaryentry{spectrum}{
+ name={\ensuremath{\spectrum{L}}},
+ description={the set of all eigenvalues of $L$},
+ sort=s
+ }
+\fi
+
% The ``Automorphism group of'' operator.
\newcommand*{\Aut}[1]{ \operatorname{Aut}\of{{#1}} }