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Int Sta Ide

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If A is a symmetric matrix, the optimization problem \argmax_{x} \frac{x^T A x}{x^Tx} = us_{max}

is solved by finding the eigenvector u_{max} corresponding to the largest eigenvalue of A: \begin{equation} A u_i =\lambda_i u_i \end{equation} where $\frac{u_{max}^T A u_{max}}{u_{max}^Tu_{max}} = \lambda_{max}$ is the eigenvalue corresponding to the eigenvector u_{max}.

{$ \begin{equation}\label{generalEig} A u_i =\lambda_i u_i \end{equation}$} \huge{\mathbb{F}_2^4} Rayleigh-Ritz quotient maximization by the largest eigenvector

http://www.pmwiki.org/wiki/Cookbook/WikiStylesPlus

www.cs.brown.edu/~th/papers/Hofmann-SIGIR 99?.pdf

http://people.csail.mit.edu/torralba/iccv2005/

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a1 b1 c1 d1
a2 b2 c2 d2

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{$\array{CCC$\Large f=b_\0+\frac{a_\1}{b_\1+\frac{a_2}{b_2+\frac{a_3}{b_3+a_4}}} & \
  \hspace{10} {\small\rm{Notes and\atop mimeTeX Sandbox}} \hspace{10} } \
  \Large \scrJ^{ij}=\frac12\vareps_{ijk}\[\array{cc$\sig_k&0\\0&\sig_k}\] $}

gives you that:

\array{CCC$\Large f=b_\0+\frac{a_\1}{b_\1+\frac{a_2}{b_2+\frac{a_3}{b_3+a_4}}} &   \hspace{10} {\small\rm{Notes and\atop mimeTeX Sandbox}} \hspace{10} }   \Large \scrJ^{ij}=\frac12\vareps_{ijk}\[\array{cc$\sig_k&0\\0&\sig_k}\]

\frac{3}{5}

\frac{asd}{asd}

\frac{asd}{199}

S_{++}^n

\frac{123}{199}

The Parser?

Aloha and welcome to Richard Socher's wiki. Here is a short summary of what you can find on this site:

A section for IMPRS and other Computer Science Master students? in Saarbrücken to find information about all courses.

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