th percentile half the numbers are bigger than it and half are smaller For

Th percentile half the numbers are bigger than it and

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th percentile: half the numbers are bigger than it, and half are smaller. For instance, the median of [45 , 1 , 10 , 30 , 25] is 25 , since this is the middle element when the numbers are arranged in order. If the list has even length, there are two choices for what the middle element could be, in which case we pick the smaller of the two, say. The purpose of the median is to summarize a set of numbers by a single, typical value. The mean , or average, is also very commonly used for this, but the median is in a sense more typical of the data: it is always one of the data values, unlike the mean, and it is less sensitive to outliers. For instance, the median of a list of a hundred 1 ’s is (rightly) 1 , as is the mean. However, if just one of these numbers gets accidentally corrupted to 10 , 000 , the mean shoots up above 100 , while the median is unaffected. Computing the median of n numbers is easy: just sort them. The drawback is that this takes O ( n log n ) time, whereas we would ideally like something linear. We have reason to be hopeful, because sorting is doing far more work than we really need—we just want the middle element and don’t care about the relative ordering of the rest of them. When looking for a recursive solution, it is paradoxically often easier to work with a more general version of the problem—for the simple reason that this gives a more powerful step to recurse upon. In our case, the generalization we will consider is selection . S ELECTION Input: A list of numbers S ; an integer k Output: The k th smallest element of S For instance, if k = 1 , the minimum of S is sought, whereas if k = b| S | / 2 c , it is the median. A randomized divide-and-conquer algorithm for selection Here’s a divide-and-conquer approach to selection. For any number v , imagine splitting list S into three categories: elements smaller than v , those equal to v (there might be duplicates), and those greater than v . Call these S L , S v , and S R respectively. For instance, if the array S : 2 36 5 21 8 13 11 20 5 4 1 is split on v = 5 , the three subarrays generated are S L : 2 4 1 S v : 5 5 S R : 36 21 8 13 11 20 The search can instantly be narrowed down to one of these sublists. If we want, say, the eighth -smallest element of S , we know it must be the third -smallest element of S R since | S L | + | S v | = 5 . That is, selection ( S, 8) = selection ( S R , 3) . More generally, by checking k against the sizes of the subarrays, we can quickly determine which of them holds the desired element: selection ( S, k ) = selection ( S L , k ) if k ≤ | S L | v if | S L | < k ≤ | S L | + | S v | selection ( S R , k - | S L | - | S v | ) if k > | S L | + | S v | .
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S. Dasgupta, C.H. Papadimitriou, and U.V. Vazirani 65 The three sublists S L , S v , and S R can be computed from S in linear time; in fact, this compu- tation can even be done in place , that is, without allocating new memory (Exercise 2.15). We then recurse on the appropriate sublist. The effect of the split is thus to shrink the number of elements from | S | to at most max {| S L | , | S R |} .
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  • Fall '14
  • Complex number, Fast Fourier transform, S. Dasgupta, C.H. Papadimitriou, U.V. Vazirani

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