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Nov 4, 2012 at 16:06 answer added A.Schulz timeline score: 1
May 29, 2012 at 19:19 comment added user742 @Steven10172, I misunderstood your problem, I thought may be you looking too minimize total sum of weights (I thought may be by variance you mean this), I don't have anything in hand right now, but may be euclidean matching algorithms give you some idea around it.
May 26, 2012 at 16:35 comment added Steven10172 The distance is just the distance formula for a 2D plain(a graph). The square Root of((x1-x2)^2 + (y1-y2)^2)
May 26, 2012 at 12:13 comment added Raphael To rephrase: please define your notion of "distance" rigorously (in a general way, not by example). I don't understand the second part of your comment.
May 26, 2012 at 9:49 history edited Steven10172 CC BY-SA 3.0
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May 26, 2012 at 9:48 comment added Steven10172 I would have a function that takes List1 and List2 and finds the distance between them and stores in an array similar to the "Calculated Distance between Coords:" List and then the Algorithm would have to go through and select the best fit option from each section without re-using a coordinate from List 2. I've added more code to the bottom to hopefully clarify
May 26, 2012 at 9:36 comment added Raphael Please specify formally what exactly the desired result is. It looks like you want a) a pairing of two coordinate lists that b) has minimal sample variance among all pairings. Is that correct?
May 26, 2012 at 9:28 history edited Raphael
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May 26, 2012 at 7:30 comment added edA-qa mort-ora-y Any algorithm? There's a relatively simple brute force $O(N!)$ algorithm. ;)
May 26, 2012 at 2:16 history tweeted twitter.com/#!/StackCompSci/status/206207115713462272
May 25, 2012 at 20:52 history asked Steven10172 CC BY-SA 3.0