Sorting Goodrich, Tamassia Sorting 1

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1 Sorting Put array A of n numbers in increasing order. A core algorithm with many applications. Simple algorithms are O(n 2 ). Optimal algorithms are O(n log n). We will see O(n) for restricted input in lab Goodrich, Tamassia Sorting 1

2 Insertion Sort 1) Scan A from left to right with index i. 2) Place A[i] into A[1..i] via swaps. 3) Running time Example: O(n 2 ) i = 2: (zero swaps) i = 3: (one swap) i = 4: (two swaps) 2004 Goodrich, Tamassia Sorting 2

3 Quick-Sort Quick-sort is a randomized sorting algorithm based on the divide-and-conquer paradigm. x n Divide: pick a random element x (called pivot) and partition S into L x w L elements less than x, w E elements equal x, and E G w G elements greater than x. n Recur: sort L and G. n Conquer: join L, E and G. x 2004 Goodrich, Tamassia Quick-Sort 3

4 Partition Algorithm 1) Create arrays L, E, and G. 2) Add each y in S to: L if y < p. E if y = p. G if y > p. This takes O(n) time Goodrich, Tamassia Quick-Sort 4

5 Quick-Sort Tree An execution of quick-sort is depicted by a binary tree. n n Each node represents a recursive call of quick-sort and stores w unsorted sequence before the execution and its pivot, and w sorted sequence at the end of the execution. The root is the initial call. n The leaves are calls on subsequences of size 0 or Goodrich, Tamassia Quick-Sort 5

6 Execution Example Pivot selection Goodrich, Tamassia Quick-Sort 6

7 Execution Example (cont.) Partition, recursive call, pivot selection Goodrich, Tamassia Quick-Sort 7

8 Execution Example (cont.) Partition, recursive call, base case Goodrich, Tamassia Quick-Sort 8

9 Execution Example (cont.) Recursive call,, base case, join Goodrich, Tamassia Quick-Sort 9

10 Execution Example (cont.) Recursive call, pivot selection Goodrich, Tamassia Quick-Sort 10

11 Execution Example (cont.) Partition,, recursive call, base case Goodrich, Tamassia Quick-Sort 11

12 Execution Example (cont.) Join, join Goodrich, Tamassia Quick-Sort 12

13 Worst-case Running Time The worst case for quick-sort occurs when the pivot is the unique minimum or maximum element. One of L and G has size n - 1 and the other has size 0. The running time is proportional to the sum n + (n - 1) Thus, the worst-case running time of quick-sort is O(n 2 ). depth time 0 n 1 n - 1 n Goodrich, Tamassia Quick-Sort 13

14 Expected Running Time Consider a recursive call of quick-sort on a sequence of size s n Good call: the sizes of L and G are each less than 3s/4 n Bad call: one of L and G has size greater than 3s/ Good call Bad call A call is good with probability 1/2 n 1/2 of the possible pivots cause good calls: Bad pivots Good pivots Bad pivots 2004 Goodrich, Tamassia Quick-Sort 14

15 Expected Running Time, Part 2 Probabilistic Fact: The expected number of coin tosses required in order to get k heads is 2k. For a node of depth i, we expect: n i/2 ancestors are good calls. n The size of the input sequence for the current call is at most (3/4) i/2 n. Therefore, we have n For a node of depth 2log 4/3 n, the expected input size is one. n The expected height of the quick-sort tree is O(log n). The time spent at the nodes of the same depth is O(n). Thus, the expected running time of quick-sort is O(n log n). expected height O(log n) 2004 Goodrich, Tamassia Quick-Sort 15 s(a) s(r) s(b) s(c) s(d) s(e) s(f) time per level O(n) O(n) O(n) total expected time: O(n log n)

16 In-Place Quick-Sort Quick-sort can be implemented to run in-place. In the partition step, use replace operations to rearrange the elements of the input sequence such that: n the elements less than the pivot have rank less than h, n the elements equal to the pivot have rank between h and k, n the elements greater than the pivot have rank greater than k. The recursive calls consider n elements with rank less than h, n elements with rank greater than k. Algorithm inplacequicksort(s, l, r) Input sequence S, ranks l and r Output sequence S with the elements of rank between l and r rearranged in increasing order if l r return i a random integer between l and r x S.elemAtRank(i) (h, k) inplacepartition(x) inplacequicksort(s, l, h - 1) inplacequicksort(s, k + 1, r) 2004 Goodrich, Tamassia Quick-Sort 16

17 In-Place Partitioning Perform the partition using two indices to split S into L and E U G (similar method splits E U G into E and G). j k Repeat until j and k cross: n Scan j to the right until finding an element > x. n Scan k to the left until finding an element < x. n Swap elements at indices j and k. (pivot = 6) j k Goodrich, Tamassia Quick-Sort 17

18 Merge-Sort Merge-sort on an input sequence S with n elements consists of three steps: Divide: partition S into two sequences S 1 and S 2 of about n/ 2 elements each. Recur: recursively sort S 1 and S 2 Conquer: merge S 1 and S 2 into a unique sorted sequence. Algorithm mergesort(s) Input sequence S of size n Output sequence S sorted if n > 1 (S 1, S 2 ) partition(s, n/2) mergesort(s 1, C) mergesort(s 2, C) S merge(s 1, S 2 ) 2004 Goodrich, Tamassia Merge Sort 18

19 Merging Two Sorted Sequences l l The merge step of merge-sort merges two sorted sequences A and B into a single sorted sequence S. The running time for two sequences with a total of n elements is O(n) using doubly linked lists. Algorithm merge(a, B) Input sequences A and B with n/2 elements each Output sorted sequence of A B S empty sequence while A.empty() B.empty() if A.front() < B.front() S.addBack(A.front()); A.eraseFront(); else S.addBack(B.front()); B.eraseFront(); while A.empty() S.addBack(A.front()); A.eraseFront(); while B.empty() S.addBack(B.front()); B.eraseFront(); return S 2004 Goodrich, Tamassia Merge Sort 19

20 Merge-Sort Tree An execution of merge-sort is depicted by a binary tree. Each node represents a recursive call of merge-sort and stores: unsorted sequence before the execution and its partition, and sorted sequence at the end of the execution. The root is the initial call. The leaves are calls on subsequences of size 0 or Goodrich, Tamassia Merge Sort 20

21 Execution Example Partition Goodrich, Tamassia Merge Sort 21

22 Execution Example (cont.) Recursive call, partition Goodrich, Tamassia Merge Sort 22

23 Execution Example (cont.) Recursive call, partition Goodrich, Tamassia Merge Sort 23

24 Execution Example (cont.) Recursive call, base case Goodrich, Tamassia Merge Sort 24

25 Execution Example (cont.) Recursive call, base case Goodrich, Tamassia Merge Sort 25

26 Execution Example (cont.) Merge Goodrich, Tamassia Merge Sort 26

27 Execution Example (cont.) Recursive call,, base case, merge Goodrich, Tamassia Merge Sort 27

28 Execution Example (cont.) Merge Goodrich, Tamassia Merge Sort 28

29 Execution Example (cont.) Recursive call,, merge, merge Goodrich, Tamassia Merge Sort 29

30 Execution Example (cont.) Merge Goodrich, Tamassia Merge Sort 30

31 Analysis of Merge-Sort l The height h of the merge-sort tree is O(log n) because each recursive call divides the sequence in half. The total time at the nodes of depth i is O(n) because we partition and merge 2 i sequences of size n/2 i. l The total running time of merge-sort is O(n log n). depth #seqs size 0 1 n 1 2 n/2 i 2 i n/2 i 2004 Goodrich, Tamassia Merge Sort 31

32 Summary of Sorting Algorithms Algorithm Time Notes selection-sort insertion-sort O(n 2 ) O(n 2 ) in-place slow (good for small inputs) in-place slow (good for small inputs) quick-sort O(n log n) expected in-place, randomized fastest (good for large inputs) heap-sort O(n log n) in-place fast (good for large inputs) merge-sort O(n log n) sequential data access fast (good for huge inputs) 2004 Goodrich, Tamassia Quick-Sort 32