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		<id>https://en.formulasearchengine.com/index.php?title=Choked_flow&amp;diff=11449</id>
		<title>Choked flow</title>
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		<summary type="html">&lt;p&gt;86.183.12.111: /* Minimum pressure ratio required for choked flow to occur */ one criteria is a criterion&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{for|the theorem in algebraic number theory|Bauer&#039;s theorem}}&lt;br /&gt;
In [[mathematics]], the &#039;&#039;&#039;Bauer–Fike theorem&#039;&#039;&#039; is a standard result in the [[perturbation theory]] of the [[eigenvalue]] of a complex-valued [[diagonalizable|diagonalizable matrix]]. In its substance, it states an absolute upper bound for the deviation of one perturbed matrix eigenvalue from a properly chosen eigenvalue of the exact matrix. Informally speaking, what it says is that &#039;&#039;the sensitivity of the eigenvalues is estimated by the condition number of the matrix of eigenvectors&#039;&#039;.&lt;br /&gt;
&lt;br /&gt;
==Theorem ([[Friedrich L. Bauer]], C.T.Fike – 1960)==&lt;br /&gt;
Let &amp;lt;math&amp;gt;A\in\mathbb{C}^{n,n}&amp;lt;/math&amp;gt; be a [[diagonalizable|diagonalizable matrix]], and &amp;lt;math&amp;gt;V\in\mathbb{C}^{n,n}&amp;lt;/math&amp;gt; be the non-singular [[eigenvector]] matrix such that &amp;lt;math&amp;gt;A=V\Lambda V^{-1}&amp;lt;/math&amp;gt;. Moreover, let &amp;lt;math&amp;gt;\mu&amp;lt;/math&amp;gt; be an eigenvalue of the matrix &amp;lt;math&amp;gt;A+\delta A&amp;lt;/math&amp;gt;; then an eigenvalue &amp;lt;math&amp;gt;\lambda\in\sigma(A)&amp;lt;/math&amp;gt; exists such that:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;|\lambda-\mu|\leq\kappa_p (V)\|\delta A\|_p&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where &amp;lt;math&amp;gt;\kappa_p(V)=\|V\|_p\|V^{-1}\|_p&amp;lt;/math&amp;gt; is the usual [[condition number]] in [[matrix norm|p-norm]].&lt;br /&gt;
&lt;br /&gt;
===Proof===&lt;br /&gt;
&lt;br /&gt;
If &amp;lt;math&amp;gt;\mu\in\sigma(A)&amp;lt;/math&amp;gt;, we can choose &amp;lt;math&amp;gt;\lambda=\mu&amp;lt;/math&amp;gt; and the thesis is trivially verified (since &amp;lt;math&amp;gt;\kappa_p(V)\geq 1&amp;lt;/math&amp;gt;).&lt;br /&gt;
&lt;br /&gt;
So, be &amp;lt;math&amp;gt;\mu\notin\sigma(A)&amp;lt;/math&amp;gt;. Then &amp;lt;math&amp;gt;\det(\Lambda-\mu I)\ \ne\  0&amp;lt;/math&amp;gt;. &amp;lt;math&amp;gt;\mu&amp;lt;/math&amp;gt; being an eigenvalue of &amp;lt;math&amp;gt;A+\delta A&amp;lt;/math&amp;gt;, we have &amp;lt;math&amp;gt;\det(A+\delta A-\mu I)=0&amp;lt;/math&amp;gt; and so&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;0=\det(V^{-1})\det(A+\delta A-\mu I)\det(V)=\det(\Lambda+V^{-1}\delta AV-\mu I)&amp;lt;/math&amp;gt;&lt;br /&gt;
:&amp;lt;math&amp;gt;=\det(\Lambda-\mu I)\det[(\Lambda-\mu I)^{-1}V^{-1}\delta AV +I]&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
and, since &amp;lt;math&amp;gt;\det(\Lambda-\mu I)\ \ne\  0&amp;lt;/math&amp;gt; as stated above, we must have&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\det[(\Lambda-\mu I)^{-1}V^{-1}\delta AV +I]=\ 0&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
which reveals the value −1 to be an eigenvalue of the matrix &amp;lt;math&amp;gt;(\Lambda-\mu I)^{-1}V^{-1}\delta AV&amp;lt;/math&amp;gt;.&lt;br /&gt;
&lt;br /&gt;
For each [[matrix norm|consistent matrix norm]], we have &amp;lt;math&amp;gt;|\lambda|\leq\|A\|&amp;lt;/math&amp;gt;, so, all &#039;&#039;p&#039;&#039;-norms being consistent, we can write:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;1\leq\|(\Lambda-\mu I)^{-1}V^{-1}\delta AV\|_p\leq\|(\Lambda-\mu I)^{-1}\|_p\|V^{-1}\|_p\|V\|_p\|\delta A\|_p&amp;lt;/math&amp;gt;&lt;br /&gt;
:&amp;lt;math&amp;gt;=\|(\Lambda-\mu I)^{-1}\|_p\ \kappa_p(V)\|\delta A\|_p&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
But &amp;lt;math&amp;gt;(\Lambda-\mu I)^{-1}&amp;lt;/math&amp;gt; being a diagonal matrix, the &#039;&#039;p&#039;&#039;-norm is easily computed, and yields:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\|(\Lambda-\mu I)^{-1}\|_p\ =\max_{\|\mathbf{x}\|_p\ne 0} \frac{\|(\Lambda-\mu I)^{-1}\mathbf{x}\|_p}{\|\mathbf{x}\|_p}\ &amp;lt;/math&amp;gt;&lt;br /&gt;
:&amp;lt;math&amp;gt;=\max_{\lambda\in\sigma(A)}\frac{1}{|\lambda -\mu|}\ =\ \frac{1}{\min_{\lambda\in\sigma(A)}|\lambda-\mu|}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
whence:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\min_{\lambda\in\sigma(A)}|\lambda-\mu|\leq\ \kappa_p(V)\|\delta A\|_p.\,&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The theorem can also be reformulated to better suit numerical methods.&lt;br /&gt;
In fact, dealing with real eigensystem problems, one often has an exact matrix &amp;lt;math&amp;gt;A&amp;lt;/math&amp;gt;, but knows only an approximate eigenvalue-eigenvector couple, (&amp;lt;math&amp;gt;\tilde{\lambda}&amp;lt;/math&amp;gt;,&amp;lt;math&amp;gt;\tilde{\mathbf{v}}&amp;lt;/math&amp;gt;), and needs to bound the error. The following version comes in help.&lt;br /&gt;
&lt;br /&gt;
==Theorem ([[Friedrich L. Bauer]], C.T.Fike – 1960) (alternative statement)==&lt;br /&gt;
&lt;br /&gt;
Let &amp;lt;math&amp;gt;A\in\mathbb{C}^{n,n}&amp;lt;/math&amp;gt; be a [[diagonalizable|diagonalizable matrix]], and be &amp;lt;math&amp;gt;V\in\mathbb{C}^{n,n}&amp;lt;/math&amp;gt; the non singular [[eigenvector]] matrix such as &amp;lt;math&amp;gt;A=V\Lambda V^{-1}&amp;lt;/math&amp;gt;. Be moreover (&amp;lt;math&amp;gt;\tilde{\lambda}&amp;lt;/math&amp;gt;,&amp;lt;math&amp;gt;\mathbf{\tilde{v}}&amp;lt;/math&amp;gt;) an approximate eigenvalue-eigenvector couple, and &amp;lt;math&amp;gt;\mathbf{r}=A\mathbf{\tilde{v}}-\tilde{\lambda}\mathbf{\tilde{v}}&amp;lt;/math&amp;gt;; then an eigenvalue &amp;lt;math&amp;gt;\lambda\in\sigma(A)&amp;lt;/math&amp;gt; exists such that:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;|\lambda-\tilde{\lambda}|\leq\kappa_p (V)\frac{\|\mathbf{r}\|_p}{\|\mathbf{\tilde{v}}\|_p}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
where &amp;lt;math&amp;gt;\kappa_p(V)=\|V\|_p\|V^{-1}\|_p&amp;lt;/math&amp;gt; is the usual [[condition number]] in [[matrix norm|p-norm]].&lt;br /&gt;
&lt;br /&gt;
===Proof===&lt;br /&gt;
&lt;br /&gt;
We solve this problem with Tarık&#039;s method:&lt;br /&gt;
m&amp;lt;math&amp;gt;\tilde{\lambda}\notin\sigma(A)&amp;lt;/math&amp;gt; (otherwise, we can choose &amp;lt;math&amp;gt;\lambda=\tilde{\lambda}&amp;lt;/math&amp;gt; and theorem is proven, since &amp;lt;math&amp;gt;\kappa_p(V)\geq 1&amp;lt;/math&amp;gt;).&lt;br /&gt;
Then &amp;lt;math&amp;gt;(A-\tilde{\lambda} I)^{-1}&amp;lt;/math&amp;gt; exists, so we can write:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\mathbf{\tilde{v}}=(A-\tilde{\lambda} I)^{-1}\mathbf{r}=V(D-\tilde{\lambda} I)^{-1}V^{-1}\mathbf{r}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
since &amp;lt;math&amp;gt;A&amp;lt;/math&amp;gt; is diagonalizable; taking the p-norm of both sides, we obtain:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\|\mathbf{\tilde{v}}\|_p=\|V(D-\tilde{\lambda} I)^{-1}V^{-1}\mathbf{r}\|_p \leq \|V\|_p \|(D-\tilde{\lambda} I)^{-1}\|_p \|V^{-1}\|_p \|\mathbf{r}\|_p&amp;lt;/math&amp;gt;&lt;br /&gt;
&amp;lt;math&amp;gt;=\kappa_p(V)\|(D-\tilde{\lambda} I)^{-1}\|_p \|\mathbf{r}\|_p.&lt;br /&gt;
&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
But, since &amp;lt;math&amp;gt;(D-\tilde{\lambda} I)^{-1}&amp;lt;/math&amp;gt; is a diagonal matrix, the p-norm is easily computed, and yields:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\|(D-\tilde{\lambda} I)^{-1}\|_p=\max_{\|\mathbf{x}\|_p \ne 0}\frac{\|(D-\tilde{\lambda} I)^{-1}\mathbf{x}\|_p}{\|\mathbf{x}\|_p}&amp;lt;/math&amp;gt;&lt;br /&gt;
:&amp;lt;math&amp;gt;=\max_{\lambda\in\sigma(A)} \frac{1}{|\lambda-\tilde{\lambda}|}=\frac{1}{\min_{\lambda\in\sigma(A)}|\lambda-\tilde{\lambda}|}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
whence:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\min_{\lambda\in\sigma(A)}|\lambda-\tilde{\lambda}|\leq\kappa_p(V)\frac{\|\mathbf{r}\|_p}{\|\mathbf{\tilde{v}}\|_p}.&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The Bauer–Fike theorem, in both versions, yields an absolute bound. The following corollary, which, besides all the hypothesis of Bauer–Fike theorem, requires also the non-singularity of A, turns out to be useful whenever a relative bound is needed.&lt;br /&gt;
&lt;br /&gt;
== Corollary ==&lt;br /&gt;
Be &amp;lt;math&amp;gt;A\in\mathbb{C}^{n,n}&amp;lt;/math&amp;gt; a non-singular, [[diagonalizable|diagonalizable matrix]], and be &amp;lt;math&amp;gt;V\in\mathbb{C}^{n,n}&amp;lt;/math&amp;gt; the non singular [[eigenvector]] matrix such as &amp;lt;math&amp;gt;A=V\Lambda V^{-1}&amp;lt;/math&amp;gt;. Be moreover &amp;lt;math&amp;gt;\mu&amp;lt;/math&amp;gt; an eigenvalue of the matrix &amp;lt;math&amp;gt;A+\delta A&amp;lt;/math&amp;gt;; then an eigenvalue &amp;lt;math&amp;gt;\lambda\in\sigma(A)&amp;lt;/math&amp;gt; exists such that:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\frac{|\lambda-\mu|}{|\lambda|}\leq\kappa_p (V)\|A^{-1}\delta A\|_p&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
(Note: &amp;lt;math&amp;gt;\|A^{-1}\delta A\|&amp;lt;/math&amp;gt;can be formally viewed as the &amp;quot;relative variation of A&amp;quot;, just as &amp;lt;math&amp;gt;|\lambda-\mu||\lambda|^{-1}&amp;lt;/math&amp;gt; is the relative variation of &amp;amp;lambda;.)&lt;br /&gt;
&lt;br /&gt;
=== Proof ===&lt;br /&gt;
Since &amp;amp;mu; is an eigenvalue of (A+&amp;amp;delta;A) and &amp;lt;math&amp;gt;det(A)\ne 0&amp;lt;/math&amp;gt;, we have, left-multiplying by &amp;lt;math&amp;gt;-A^{-1}&amp;lt;/math&amp;gt;:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;-A^{-1}(A+\delta A)\mathbf{v}=-\mu A^{-1}\mathbf{v}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
that is, putting&amp;lt;math&amp;gt;\tilde{A}=\mu A^{-1}&amp;lt;/math&amp;gt; and &amp;lt;math&amp;gt;\tilde{\delta A}=-A^{-1}\delta A&amp;lt;/math&amp;gt;:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;(\tilde{A}+\tilde{\delta A}-I)\mathbf{v}=\mathbf{0}&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
which means that&amp;lt;math&amp;gt;\tilde{\mu}=1&amp;lt;/math&amp;gt;is an eigenvalue of&amp;lt;math&amp;gt;(\tilde{A}+\tilde{\delta A})&amp;lt;/math&amp;gt;, with &amp;lt;math&amp;gt;\mathbf{v}&amp;lt;/math&amp;gt;eigenvector. Now, the eigenvalues of &amp;lt;math&amp;gt;\tilde{A}&amp;lt;/math&amp;gt;are &amp;lt;math&amp;gt;\frac{\mu}{\lambda_i}&amp;lt;/math&amp;gt;, while its eigenvector matrix is the same as A. Applying the Bauer–Fike theorem to the matrix&amp;lt;math&amp;gt;\tilde{A}+\tilde{\delta A}&amp;lt;/math&amp;gt; and to its eigenvalue&amp;lt;math&amp;gt;\tilde{\mu}=1&amp;lt;/math&amp;gt;, we obtain:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\min_{\lambda\in\sigma(A)}\left|\frac{\mu}{\lambda}-1\right|=\min_{\lambda\in\sigma(A)}\frac{|\lambda-\mu|}{|\lambda|}\leq\kappa_p (V)\|A^{-1}\delta A\|_p&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
== Remark ==&lt;br /&gt;
&lt;br /&gt;
If A is [[normal matrix|normal]], V is a [[unitary matrix]], and &amp;lt;math&amp;gt;\|V\|_2=\|V^{-1}\|_2=1&amp;lt;/math&amp;gt;, so that &amp;lt;math&amp;gt;\kappa_2(V)=1&amp;lt;/math&amp;gt;.&lt;br /&gt;
&lt;br /&gt;
The Bauer–Fike theorem then becomes:&lt;br /&gt;
&lt;br /&gt;
:&amp;lt;math&amp;gt;\exists\lambda\in\sigma(A): |\lambda-\mu|\leq\|\delta A\|_2&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
:( &amp;lt;math&amp;gt;\exists\lambda\in\sigma(A): |\lambda-\tilde{\lambda}|\leq\frac{\|\mathbf{r}\|_2}{\|\mathbf{\tilde{v}}\|_2}&amp;lt;/math&amp;gt; in the alternative formulation)&lt;br /&gt;
&lt;br /&gt;
which obviously remains true if A is a [[Hermitian matrix]]. In this case, however, a much stronger result holds, known as the [[Weyl&#039;s inequality|Weyl&#039;s theorem on eigenvalues]].&lt;br /&gt;
&lt;br /&gt;
== References ==&lt;br /&gt;
# F. L. Bauer and C. T. Fike. &#039;&#039;Norms and exclusion theorems&#039;&#039;. Numer. Math. 2 (1960), 137–141.&lt;br /&gt;
# S. C. Eisenstat and I. C. F. Ipsen. &#039;&#039;Three absolute perturbation bounds for matrix eigenvalues imply relative bounds&#039;&#039;. SIAM Journal on Matrix Analysis and Applications Vol. 20, N. 1 (1998), 149–158&lt;br /&gt;
&lt;br /&gt;
{{DEFAULTSORT:Bauer-Fike theorem}}&lt;br /&gt;
[[Category:Spectral theory]]&lt;br /&gt;
[[Category:Theorems in analysis]]&lt;br /&gt;
[[Category:Articles containing proofs]]&lt;/div&gt;</summary>
		<author><name>86.183.12.111</name></author>
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