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  1. POApproximated closest pair algorithm
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    <p>I have been thinking about a variation of the <a href="http://en.wikipedia.org/wiki/Closest_pair_of_points_problem" rel="nofollow">closest pair problem</a> in which the only available information is the set of distances already calculated (we are not allowed to sort points according to their x-coordinates).</p> <p>Consider 4 points (A, B, C, D), and the following distances:</p> <pre><code>dist(A,B) = 0.5 dist(A,C) = 5 dist(C,D) = 2 </code></pre> <p>In this example, I don't need to evaluate <code>dist(B,C)</code> or <code>dist(A,D)</code>, because it is guaranteed that these distances are greater than the current known minimum distance. </p> <ol> <li><p>Is it possible to use this kind of information to reduce the O(n²) to something like O(nlogn)?</p></li> <li><p>Is it possible to reduce the cost to something close to O(nlogn) if I accept a kind of approximated solution? In this case, I am thinking about some technique based on reinforcement learning that only converges to the real solution when the number of reinforcements go to infinite, but provides a great approximation for small n.</p></li> <li><p>Processing time (measured by the big O notation) is not the only issue. To keep a very large amount of previous calculated distances can also be an issue.</p></li> <li><p>Imagine this problem for a set with 10⁸ points.</p></li> </ol> <p>What kind of solution should I look for? Was this kind of problem solved before?</p> <p>This is not a classroom problem or something related. I have been just thinking about this problem. </p>
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