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	<title>Comments on: Amplification, arbitrage, and the tensor power trick</title>
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	<description>Updates on my research and expository papers, discussion of open problems, and other maths-related topics.  By Terence Tao</description>
	<lastBuildDate>Wed, 19 Jun 2013 02:43:54 +0000</lastBuildDate>
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		<title>By: Strengthening inequalities: a mathematical trick &#124; Abstract Art</title>
		<link>http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-228788</link>
		<dc:creator><![CDATA[Strengthening inequalities: a mathematical trick &#124; Abstract Art]]></dc:creator>
		<pubDate>Sat, 11 May 2013 06:06:42 +0000</pubDate>
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		<description><![CDATA[[&#8230;] [1.9] Amplification, arbitrage, and the tensor power trick [&#8230;]]]></description>
		<content:encoded><![CDATA[<p>[&#8230;] [1.9] Amplification, arbitrage, and the tensor power trick [&#8230;]</p>
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		<title>By: Bird&#8217;s-eye views of Structure and Randomness (Series) &#124; Abstract Art</title>
		<link>http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-228782</link>
		<dc:creator><![CDATA[Bird&#8217;s-eye views of Structure and Randomness (Series) &#124; Abstract Art]]></dc:creator>
		<pubDate>Sat, 11 May 2013 06:05:08 +0000</pubDate>
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		<description><![CDATA[[&#8230;] Strengthening inequalities: a mathematical trick (adapted from &#8220;Amplification, arbitrage, and the tensor power trick&#8220;) [&#8230;]]]></description>
		<content:encoded><![CDATA[<p>[&#8230;] Strengthening inequalities: a mathematical trick (adapted from &#8220;Amplification, arbitrage, and the tensor power trick&#8220;) [&#8230;]</p>
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	<item>
		<title>By: Denis Serre</title>
		<link>http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-218555</link>
		<dc:creator><![CDATA[Denis Serre]]></dc:creator>
		<pubDate>Mon, 04 Mar 2013 14:45:22 +0000</pubDate>
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		<description><![CDATA[Hi Terry,

Great post! I like your style and try to follow it closely. 

Here are two examples of amplification, where the amplification trick gets rid of constants.
1. The maximum principle for holomorphic functions in a disk $latex D$ (which could be another domain). Let $latex z\in D$ and $latex f:D\rightarrow C$ be a holomorphic function. Then Cauchy&#039;s integral formula gives you an inequality $latex &#124;f(z)&#124;\leq C_z\&#124;f\&#124;$, with respect to the sup-norm over the boundary. Apply the inequality to $latex f^k$, then take the $latex k$-th root. The constant is changed into $latex C_z^{1/k}$, which tends to $latex 1$ as $latex k$ tends to infinity.
2. In matrix analysis, let $latex \&#124;\cdot\&#124;$ be an algebra norm over $latex M_n(R)$, that is $latex \&#124;AB\&#124;\leq\&#124;A\&#124;\cdot\&#124;B\&#124;$. Let $latex \rho(A)$ denote the spectral radius of a matrix $latex A$ (which involves the modulus of complex eigenvalues too). Then $latex \rho(A)\le\&#124;A\&#124;$. This would be obvious if the norm was subordinated to a complex norm (take an eigenvector associated with an eigenvalue of maximal modulus, bla-bla). If not, take any such subordinated norm $latex N$, we thus have $latex \rho(A)\le N(A)$. By equivalence of norms, we obtain $latex \rho(A)\le C\&#124;A\&#124;$. Apply this to $latex A^k$, use $latex \&#124;A^k\&#124;\le\&#124;A\&#124;^k$, then take the $latex k$-th root and let $latex k$ tend to infinity.

All the best,

Denis]]></description>
		<content:encoded><![CDATA[<p>Hi Terry,</p>
<p>Great post! I like your style and try to follow it closely. </p>
<p>Here are two examples of amplification, where the amplification trick gets rid of constants.<br />
1. The maximum principle for holomorphic functions in a disk <img src='http://s0.wp.com/latex.php?latex=D&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='D' title='D' class='latex' /> (which could be another domain). Let <img src='http://s0.wp.com/latex.php?latex=z%5Cin+D&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='z&#92;in D' title='z&#92;in D' class='latex' /> and <img src='http://s0.wp.com/latex.php?latex=f%3AD%5Crightarrow+C&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='f:D&#92;rightarrow C' title='f:D&#92;rightarrow C' class='latex' /> be a holomorphic function. Then Cauchy&#8217;s integral formula gives you an inequality <img src='http://s0.wp.com/latex.php?latex=%7Cf%28z%29%7C%5Cleq+C_z%5C%7Cf%5C%7C&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='|f(z)|&#92;leq C_z&#92;|f&#92;|' title='|f(z)|&#92;leq C_z&#92;|f&#92;|' class='latex' />, with respect to the sup-norm over the boundary. Apply the inequality to <img src='http://s0.wp.com/latex.php?latex=f%5Ek&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='f^k' title='f^k' class='latex' />, then take the <img src='http://s0.wp.com/latex.php?latex=k&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='k' title='k' class='latex' />-th root. The constant is changed into <img src='http://s0.wp.com/latex.php?latex=C_z%5E%7B1%2Fk%7D&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='C_z^{1/k}' title='C_z^{1/k}' class='latex' />, which tends to <img src='http://s0.wp.com/latex.php?latex=1&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='1' title='1' class='latex' /> as <img src='http://s0.wp.com/latex.php?latex=k&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='k' title='k' class='latex' /> tends to infinity.<br />
2. In matrix analysis, let <img src='http://s0.wp.com/latex.php?latex=%5C%7C%5Ccdot%5C%7C&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='&#92;|&#92;cdot&#92;|' title='&#92;|&#92;cdot&#92;|' class='latex' /> be an algebra norm over <img src='http://s0.wp.com/latex.php?latex=M_n%28R%29&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='M_n(R)' title='M_n(R)' class='latex' />, that is <img src='http://s0.wp.com/latex.php?latex=%5C%7CAB%5C%7C%5Cleq%5C%7CA%5C%7C%5Ccdot%5C%7CB%5C%7C&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='&#92;|AB&#92;|&#92;leq&#92;|A&#92;|&#92;cdot&#92;|B&#92;|' title='&#92;|AB&#92;|&#92;leq&#92;|A&#92;|&#92;cdot&#92;|B&#92;|' class='latex' />. Let <img src='http://s0.wp.com/latex.php?latex=%5Crho%28A%29&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='&#92;rho(A)' title='&#92;rho(A)' class='latex' /> denote the spectral radius of a matrix <img src='http://s0.wp.com/latex.php?latex=A&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='A' title='A' class='latex' /> (which involves the modulus of complex eigenvalues too). Then <img src='http://s0.wp.com/latex.php?latex=%5Crho%28A%29%5Cle%5C%7CA%5C%7C&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='&#92;rho(A)&#92;le&#92;|A&#92;|' title='&#92;rho(A)&#92;le&#92;|A&#92;|' class='latex' />. This would be obvious if the norm was subordinated to a complex norm (take an eigenvector associated with an eigenvalue of maximal modulus, bla-bla). If not, take any such subordinated norm <img src='http://s0.wp.com/latex.php?latex=N&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='N' title='N' class='latex' />, we thus have <img src='http://s0.wp.com/latex.php?latex=%5Crho%28A%29%5Cle+N%28A%29&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='&#92;rho(A)&#92;le N(A)' title='&#92;rho(A)&#92;le N(A)' class='latex' />. By equivalence of norms, we obtain <img src='http://s0.wp.com/latex.php?latex=%5Crho%28A%29%5Cle+C%5C%7CA%5C%7C&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='&#92;rho(A)&#92;le C&#92;|A&#92;|' title='&#92;rho(A)&#92;le C&#92;|A&#92;|' class='latex' />. Apply this to <img src='http://s0.wp.com/latex.php?latex=A%5Ek&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='A^k' title='A^k' class='latex' />, use <img src='http://s0.wp.com/latex.php?latex=%5C%7CA%5Ek%5C%7C%5Cle%5C%7CA%5C%7C%5Ek&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='&#92;|A^k&#92;|&#92;le&#92;|A&#92;|^k' title='&#92;|A^k&#92;|&#92;le&#92;|A&#92;|^k' class='latex' />, then take the <img src='http://s0.wp.com/latex.php?latex=k&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='k' title='k' class='latex' />-th root and let <img src='http://s0.wp.com/latex.php?latex=k&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='k' title='k' class='latex' /> tend to infinity.</p>
<p>All the best,</p>
<p>Denis</p>
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		<title>By: Jorn</title>
		<link>http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-213578</link>
		<dc:creator><![CDATA[Jorn]]></dc:creator>
		<pubDate>Thu, 17 Jan 2013 14:15:28 +0000</pubDate>
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		<description><![CDATA[Another great example of amplification is the proof of Chernoff&#039;s bound from Markov&#039;s inequality in probability theory, which stunned me when I first came across it. Markov&#039;s inequality states that $P(X \ge a) \le E(X)/a$ for non-negative random variables $X$. Using that $P(X \ge a) = P(\exp(\theta X) \ge \exp(\theta a))$ for any $\theta &gt; 0$, we find that $P(X \ge a) \le E(\exp(\theta X)) \exp(-\theta a)$, in which the parameter $\theta &gt; 0$ can be chosen freely.]]></description>
		<content:encoded><![CDATA[<p>Another great example of amplification is the proof of Chernoff&#8217;s bound from Markov&#8217;s inequality in probability theory, which stunned me when I first came across it. Markov&#8217;s inequality states that $P(X \ge a) \le E(X)/a$ for non-negative random variables $X$. Using that $P(X \ge a) = P(\exp(\theta X) \ge \exp(\theta a))$ for any $\theta &gt; 0$, we find that $P(X \ge a) \le E(\exp(\theta X)) \exp(-\theta a)$, in which the parameter $\theta &gt; 0$ can be chosen freely.</p>
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		<title>By: A mathematical formalisation of dimensional analysis &#171; What&#8217;s new</title>
		<link>http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-211409</link>
		<dc:creator><![CDATA[A mathematical formalisation of dimensional analysis &#171; What&#8217;s new]]></dc:creator>
		<pubDate>Sat, 29 Dec 2012 21:04:55 +0000</pubDate>
		<guid isPermaLink="false">http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-211409</guid>
		<description><![CDATA[[...] to amplify a hybrid inequality into a dimensionally pure one by optimising over all rescalings; see this previous blog post for a discussion of this trick (which, among other things, amplifies the inhomogeneous Sobolev [...]]]></description>
		<content:encoded><![CDATA[<p>[...] to amplify a hybrid inequality into a dimensionally pure one by optimising over all rescalings; see this previous blog post for a discussion of this trick (which, among other things, amplifies the inhomogeneous Sobolev [...]</p>
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	<item>
		<title>By: Terence Tao</title>
		<link>http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-135669</link>
		<dc:creator><![CDATA[Terence Tao]]></dc:creator>
		<pubDate>Thu, 29 Mar 2012 18:36:52 +0000</pubDate>
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		<description><![CDATA[Sorry, by &quot;constant&quot; I meant &quot;constant in i&quot; rather than &quot;absolute constant&quot;.  The point is that the arithmetic mean-geometric mean inequality $latex k^k x_1 \ldots x_k \leq (x_1 + \ldots + x_k)^k$ is close to equality when the $latex x_i$ are comparable, no matter how large the $latex x_i$ are.  In this particular application, we want to reverse the AM-GM inequality with $latex x_i := N_i &#124;A+B_i&#124;$, so the optimisation proceeds by setting the $latex x_i$ to be comparable, e.g. setting $latex N_i := \lfloor L / &#124;A+B_i&#124; \rfloor$ for some large parameter L.]]></description>
		<content:encoded><![CDATA[<p>Sorry, by &#8220;constant&#8221; I meant &#8220;constant in i&#8221; rather than &#8220;absolute constant&#8221;.  The point is that the arithmetic mean-geometric mean inequality <img src='http://s0.wp.com/latex.php?latex=k%5Ek+x_1+%5Cldots+x_k+%5Cleq+%28x_1+%2B+%5Cldots+%2B+x_k%29%5Ek&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='k^k x_1 &#92;ldots x_k &#92;leq (x_1 + &#92;ldots + x_k)^k' title='k^k x_1 &#92;ldots x_k &#92;leq (x_1 + &#92;ldots + x_k)^k' class='latex' /> is close to equality when the <img src='http://s0.wp.com/latex.php?latex=x_i&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='x_i' title='x_i' class='latex' /> are comparable, no matter how large the <img src='http://s0.wp.com/latex.php?latex=x_i&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='x_i' title='x_i' class='latex' /> are.  In this particular application, we want to reverse the AM-GM inequality with <img src='http://s0.wp.com/latex.php?latex=x_i+%3A%3D+N_i+%7CA%2BB_i%7C&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='x_i := N_i |A+B_i|' title='x_i := N_i |A+B_i|' class='latex' />, so the optimisation proceeds by setting the <img src='http://s0.wp.com/latex.php?latex=x_i&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='x_i' title='x_i' class='latex' /> to be comparable, e.g. setting <img src='http://s0.wp.com/latex.php?latex=N_i+%3A%3D+%5Clfloor+L+%2F+%7CA%2BB_i%7C+%5Crfloor&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='N_i := &#92;lfloor L / |A+B_i| &#92;rfloor' title='N_i := &#92;lfloor L / |A+B_i| &#92;rfloor' class='latex' /> for some large parameter L.</p>
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		<title>By: Tomer Shalev</title>
		<link>http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-135667</link>
		<dc:creator><![CDATA[Tomer Shalev]]></dc:creator>
		<pubDate>Thu, 29 Mar 2012 17:50:59 +0000</pubDate>
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		<description><![CDATA[Hi Prof Tao,

regarding amplification of (17).

can you elaborate on the optimization of  $latex N_1,...,N_k $
it seems strange to pick a near global constant when one
knows that  $latex &#124;A + B_i&#124; $ can be big.]]></description>
		<content:encoded><![CDATA[<p>Hi Prof Tao,</p>
<p>regarding amplification of (17).</p>
<p>can you elaborate on the optimization of  <img src='http://s0.wp.com/latex.php?latex=N_1%2C...%2CN_k+&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='N_1,...,N_k ' title='N_1,...,N_k ' class='latex' /><br />
it seems strange to pick a near global constant when one<br />
knows that  <img src='http://s0.wp.com/latex.php?latex=%7CA+%2B+B_i%7C+&amp;bg=ffffff&amp;fg=545454&amp;s=0' alt='|A + B_i| ' title='|A + B_i| ' class='latex' /> can be big.</p>
]]></content:encoded>
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		<title>By: Montgomery&#8217;s uncertainty principle &#171; What&#8217;s new</title>
		<link>http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-119928</link>
		<dc:creator><![CDATA[Montgomery&#8217;s uncertainty principle &#171; What&#8217;s new]]></dc:creator>
		<pubDate>Sun, 01 Jan 2012 20:16:03 +0000</pubDate>
		<guid isPermaLink="false">http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-119928</guid>
		<description><![CDATA[[...] which one dilates  to  (and replaces each frequency  by their  roots), and then sending  (cf. the tensor product trick). But we will not need this refinement [...]]]></description>
		<content:encoded><![CDATA[<p>[...] which one dilates  to  (and replaces each frequency  by their  roots), and then sending  (cf. the tensor product trick). But we will not need this refinement [...]</p>
]]></content:encoded>
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	<item>
		<title>By: AJ</title>
		<link>http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-42651</link>
		<dc:creator><![CDATA[AJ]]></dc:creator>
		<pubDate>Wed, 02 Dec 2009 22:48:42 +0000</pubDate>
		<guid isPermaLink="false">http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-42651</guid>
		<description><![CDATA[Are the techniques posted here of any relevance to entropy power inequalities arising in network information theory?]]></description>
		<content:encoded><![CDATA[<p>Are the techniques posted here of any relevance to entropy power inequalities arising in network information theory?</p>
]]></content:encoded>
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	<item>
		<title>By: Vorlesung Funktionalanalysis: Erste Etappe &#171; UGroh&#39;s Weblog</title>
		<link>http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-42419</link>
		<dc:creator><![CDATA[Vorlesung Funktionalanalysis: Erste Etappe &#171; UGroh&#39;s Weblog]]></dc:creator>
		<pubDate>Wed, 18 Nov 2009 14:31:52 +0000</pubDate>
		<guid isPermaLink="false">http://terrytao.wordpress.com/2007/09/05/amplification-arbitrage-and-the-tensor-power-trick/#comment-42419</guid>
		<description><![CDATA[[...] die Cauchy-Schwarz Ungleichung. In diesem Zusammenhang möchte ich auf den Interessanten Artikel Amplification, Arbitrage and the Tensor Power Trick von T. Tao hinweisen, in dem die Methodik des Beweises der Cauchy-Schwarz Ungleichung in [...]]]></description>
		<content:encoded><![CDATA[<p>[...] die Cauchy-Schwarz Ungleichung. In diesem Zusammenhang möchte ich auf den Interessanten Artikel Amplification, Arbitrage and the Tensor Power Trick von T. Tao hinweisen, in dem die Methodik des Beweises der Cauchy-Schwarz Ungleichung in [...]</p>
]]></content:encoded>
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