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  <title><![CDATA[ARC Colloquium: David Karger (MIT)]]></title>
  <body><![CDATA[<p style="color:maroon;">Video of this talk is available at: <a href="https://smartech.gatech.edu/handle/1853/55915">https://smartech.gatech.edu/handle/1853/55915</a></p>
Full collection of talk videos are available at:  
<a href="https://smartech.gatech.edu/handle/1853/46836">https://smartech.gatech.edu/handle/1853/46836</a>

<br>
<br>


<p  align="center"><strong>Algorithms &amp; Randomness Center (ARC)</strong></p>

<p align="center"><a href="http://people.csail.mit.edu/karger/"><strong>David Karger - MIT</strong></a></p>

<p align="center"><strong>Monday, September 26, 2016<br />
Klaus 1116 East - 11:00 am</strong></p>

<p><strong>Title:</strong><br />
A Fast and Simple Unbiased Estimator for Network (Un)reliability</p>

<p><strong>Abstract</strong>:<br />
The following procedure yields an unbiased estimator for the disconnection probability of an n-vertex graph with minimum cut c if every edge fails independently with probability p: (i) contract every edge independently with probability 1-n^{-2/c}, then (ii) recursively compute the disconnection probability of the resulting tiny graph if each edge fails with probability n^{2/c}p.&nbsp; We give a short, simple, self-contained proof that this estimator can be computed in linear time and has relative variance O(n^2).&nbsp; Combining these two facts with a relatively standard sparsification argument yields an O(n^3\log n)-time algorithm for estimating the (un)reliability of a network.&nbsp; We also show how the technique can be used to create unbiased samples of disconnected networks.</p>

<p>Speaker&#39;s webpage: <a href="http://people.csail.mit.edu/karger/">http://people.csail.mit.edu/karger/</a><br />
Fall 2016 ARC Seminar Schedule: &nbsp;<a href="http://arc.gatech.edu/node/114" target="_blank">http://arc.gatech.edu/node/114</a></p>

<p>&nbsp;</p>]]></body>
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      <value><![CDATA[<p>Dani Denton</p>

<p>denton at cc dot gatech dot edu</p>
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