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<record version="12" id="3018">
 <title>operator norm</title>
 <name>OperatorNorm</name>
 <created>2002-06-03 20:12:52</created>
 <modified>2008-09-17 20:00:13</modified>
 <type>Definition</type>
 <creator id="17536" name="asteroid"/>
 <author id="17536" name="asteroid"/>
 <author id="3771" name="CWoo"/>
 <author id="291" name="igor"/>
 <classification>
	<category scheme="msc" code="46A32"/>
	<category scheme="msc" code="47L25"/>
	<category scheme="msc" code="47A30"/>
 </classification>
 <defines>
	<concept>bounded linear map</concept>
	<concept>unbounded linear map</concept>
	<concept>bounded operator</concept>
	<concept>unbounded operator</concept>
 </defines>
 <synonyms>
	<synonym concept="operator norm" alias="induced norm"/>
 </synonyms>
 <related>
	<object name="VectorNorm"/>
	<object name="OperatorTopologies"/>
	<object name="HomomorphismsOfCAlgebrasAreContinuous"/>
	<object name="CAlgebra"/>
 </related>
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\def\V{{\mathsf V}}
\def\W{{\mathsf W}}
\def\R{{\mathbb R}}
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\def\op{{\rm op}}</preamble>
 <content>\subsection*{Definition}

Let $A\colon \V\to\W$ be a linear map between normed vector spaces $\V$ and
$\W$. To each such map (operator) $A$ we can assign a non-negative number
$\|A\|_{\op}$ defined by
\[
 \|A\|_{\op} := \mathop{\sup_{\v\in\V}}_{\v\ne{\bf 0}} \frac{\|A\v\|}{\|\v\|},
\]
where the supremum $\|A\|_{\op}$ could be finite or infinite.
Equivalently, the above definition can be written as
\[
 \|A\|_\op := \mathop{\sup_{\v\in\V}}_{\|\v\|=1} \|A\v\|
    = \mathop{\sup_{\v\in\V}}_{0&lt;\|\v\|\le1} \|A\v\|.
\]
By convention, if $\V$ is the zero vector space, any operator from $\V$
to $\W$ must be the zero operator and is assigned zero norm.

$\|A\|_{\op}$ is called the the \emph{operator norm} (or the \emph{induced norm})
of $A$, for reasons that will be clear in the next \PMlinkescapetext{section}.

\subsection*{Operator norm is in fact a norm}

{\bf Definition -} If $\|A\|_{\op}$ is finite, we say that $A$ is a
\emph{\PMlinkescapetext{bounded}}. Otherwise, we say that $A$ is \emph{\PMlinkescapetext{unbounded}}.


It turns out that, for bounded operators, $\|\cdot\|_{\op}$ satisfies all the properties of a norm
(hence the name \emph{operator norm}). The proof follows immediately from the definition:

\begin{description}
\item[Positivity:]
  Since $\|A\v\|\ge 0$, by definition
  $\|A\|_\op \ge 0$. Also, $\|A\v\| = 0$ identically only if $A=0$.
  Hence $\|A\|_\op = 0$ only if $A = 0$.
\item[Absolute homogeneity:]
  Since $\|\lambda A\v\|=|\lambda| \|A\v\|$, by definition
  $\|\lambda A\|_\op = |\lambda| \|A\|_\op$.
\item[Triangle inequality:]
  Since $\|(A+B)\v\|=\|A\v+B\v\|\le \|A\v\| + \|B\v\|$, by definition
  $\|A+B\|_\op \le \|A\|_\op + \|B\|_\op$.
\end{description}

The set $L(\V,\W)$ of bounded linear maps from ${\mathsf V}$ to ${\mathsf W}$ forms a vector space and $\|\cdot\|_{\op}$ defines a norm in it.


\subsection*{Example}
Suppose that $\V=(\R^n,\|\cdot\|_p)$ and $\W=(\R^n,\|\cdot\|_p)$, where
$\|\cdot\|_p$ is the vector p-norm. Then the operator norm
$\|\cdot\|_\op = \|\cdot\|_p$ is the matrix p-norm.</content>
</record>
