Divide and Conquer: Counting Inversions

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Stephanie Lawson
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1 Divide and Conquer: Counting Inversions

2 Rank Analysis Collaborative filtering matches your preference (books, music, movies, restaurants) with that of others finds people with similar tastes recommends new things to you based on purchases of these people Meta-search tools same query to many search engines synthesize result by looking for similarities of resulting rankings The basis: compare the similarity of two rankings

3 What's similar? Given numbers 1 to n (the things) rank these according to your preference You get some permutation of 1..n Compare to someone else's permutation Extreme similarity somebody else's ranking is exactly the same Extreme dissimilarity somebody else's ranking is exactly the opposite In the middle: count the number of out of place rankings

4 Simplify it Count the number of inversions of a ranking r 1, r 2,...,r n count the number of out of order pairs i<j r i >r j eg: inversions: (2,1) (4,3) Why is this synonymous with comparing two different rankings? Because we can re-number, such that one of the rankings becomes 1,2,...,n

5 Visualizing inversions zero inversions one inversion

6 Visualizing inversions how many? enumerate them how many?

7 How many inversions? What is the maximum number of inversions for a list of length n? reverse the numbers, all pairs are inversions " n % $ ' = # 2& n.(n 1) 2

8 Naive algorithm Suggestions? # test all pairs c=0 for i in 0 to n-1 for j in i+1 to n-1 compare r i and r j if inversion c++

9 Sort Does Bubble sort count inversions? Selection sort? Insertion sort? These are O(n 2 ) Do these sorts on: and see what happens

10 Do it to it

11 Can we do better? Notice: there are potentially n*(n-1)/2 inversions Bubble and insertion sort count each individual inversion To do better we must not count each individual inversion Think of merge sort in merge sort we do not swap all elements that are out of order with each other, we make larger distance "swaps" if we can merge sort and keep track of the number of inversions we may get an O(n logn) algorithm

12 Eg: [ ] sort [ ] sort LEFT: [4 2 3] sort left: [4 2] à [2 4]:1 inversion sort right: [3] merge(left,right) à [2 3 4] 1 inversions (3 jumps over 4) sort RIGHT: [5 1] à [1 5] 1 inversion merge(left,right) à[ ] 3 inversions (1 jumps over 2,3 & 4) Total inversions: =6 (go check the visualization)

13 The algorithm While merging in merge sort keep track of the number of inversions. When merging an element from left: no inversions added When merging an element from right: how many inversions added? left i... merge result right j... As many elements as are remaining in left, because the element from the right jumps over them

14 Counting Inversions: Algorithm Sort-and-Count(L) if list L has one element return 0 and the list L divide the list into two halves A and B (r A, A) Sort-and-Count(A) (r B, B) Sort-and-Count(B) (r B, R) Merge-and-Count(A, B) return r = r A + r B + r and the sorted list R Merge-and-Count(L,R) count = 0 while L and R not empty: append smallest of Li and Rj to result if Rj smallest add number of elements remaining in L to count if one list empty append the other one to result return count, result 14

15 Running time Just like merge sort, the sort and count algorithm running time satisfies: T(n) = 2 T(n / 2) + cn Running time is therefore O(n log n) 15

16 Repeated substitution Claim. If T(n) satisfies this recurrence, then T(n) = cn log 2 n.! # T(n) = " # $ c if n =1 2T(n / 2) + cn otherwise sorting both halves merging For n > 1: T(n) = 2T(n / 2) + cn = 4T(n / 4) + cn + 2n / 2 = 8T(n / 8) + cn + cn + 4cn / 4 = 2 log 2 n T(1) + cn ++ cn = O(nlog 2 n) log 2 n 16

17 mergesort: Recurrence Analysis f (n) = a f (n /b) + cn d a = b = d = O(?) f (n) = " O n d $ # O n d logn $ O n log b a % ( ) if a < b d ( ) if a = b d ( ) if a > b d & $ ' $ ( 17

18 i = auxiliary array Total: 6 18

19 i = auxiliary array Total: 6 19

20 i = auxiliary array Total: 6 20

21 i = auxiliary array Total: 6 21

22 i = auxiliary array Total: 6 22

23 i = auxiliary array Total: 6 23

24 i = auxiliary array Total: 6 24

25 i = auxiliary array Total: 6 25

26 i = auxiliary array Total:

27 i = auxiliary array Total:

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31 i = auxiliary array Total:

32 i = auxiliary array Total:

33 i = auxiliary array Total:

34 i = auxiliary array Total:

35 i = auxiliary array Total:

36 i = auxiliary array Total:

37 first half exhausted i = auxiliary array Total:

38 i = auxiliary array Total:

39 i = auxiliary array Total:

40 i = auxiliary array Total:

41 i = auxiliary array Total: = 13 41

Closest Pair Problem

Closest Pair Problem Given n points in d-dimensions, find two whose mutual distance is smallest. Fundamental problem in many applications as well as a key step in many algorithms. p q A naive algorithm