1. 1 Merge Sort 7 2  9 4  2 4 7 9 7  2  2 79  4  4 9 7  72  29  94  4

2. 2 Divide-and-Conquer Divide-and conquer is a general algorithm design paradigm: Divide: divide the input data S in two disjoint subsets S 1 and S 2 Recur: solve the subproblems associated with S 1 and S 2 Conquer: combine the solutions for S 1 and S 2 into a solution for S The base case for the recursion are subproblems of size 0 or 1 Merge-sort is a sorting algorithm based on the divide-and-conquer paradigm Like heap-sort It uses a comparator It has O(n log n) running time Unlike heap-sort It does not use an auxiliary priority queue It accesses data in a sequential manner (suitable to sort data on a disk)

3. 3 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, C) Input sequence S with n elements, comparator C Output sequence S sorted according to C if S.size() > 1 (S 1, S 2 )  partition(S, n/2) mergeSort(S 1, C) mergeSort(S 2, C) S  merge(S 1, S 2 )

4. 4 Merging Two Sorted Sequences The conquer step of merge-sort consists of merging two sorted sequences A and B into a sorted sequence S containing the union of the elements of A and B Merging two sorted sequences, each with n  2 elements and implemented by means of a doubly linked list, takes O(n) time 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.isEmpty()   B.isEmpty() if A.first().element() < B.first().element() S.insertLast(A.remove(A.first())) else S.insertLast(B.remove(B.first())) while  A.isEmpty() S.insertLast(A.remove(A.first())) while  B.isEmpty() S.insertLast(B.remove(B.first())) return S

5. 5 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  sorted sequence at the end of the execution the root is the initial call the leaves are calls on subsequences of size 0 or 1 7 2  9 4  2 4 7 9 7  2  2 79  4  4 9 7  72  29  94  4

6. 6 Execution Example Partition 7 2 9 4  2 4 7 93 8 6 1  1 3 8 67 2  2 79 4  4 93 8  3 86 1  1 67  72  29  94  43  38  86  61  1 7 2 9 4  3 8 6 1  1 2 3 4 6 7 8 9

7. 7 Execution Example (cont.) Recursive call, partition 7 2  9 4  2 4 7 9 3 8 6 1  1 3 8 6 7 2  2 79 4  4 93 8  3 86 1  1 67  72  29  94  43  38  86  61  1 7 2 9 4  3 8 6 1  1 2 3 4 6 7 8 9

8. 8 Execution Example (cont.) Recursive call, partition 7 2  9 4  2 4 7 93 8 6 1  1 3 8 6 7  2  2 7 9 4  4 93 8  3 86 1  1 6 7  72  29  94  43  38  86  61  1 7 2 9 4  3 8 6 1  1 2 3 4 6 7 8 9

9. 9 Execution Example (cont.) Recursive call, base case 7 2  9 4  2 4 7 93 8 6 1  1 3 8 6 7  2  2 79 4  4 93 8  3 86 1  1 6 7  77  7 2  29  94  43  38  86  61  1 7 2 9 4  3 8 6 1  1 2 3 4 6 7 8 9

10. 10 Execution Example (cont.) Recursive call, base case 7 2  9 4  2 4 7 93 8 6 1  1 3 8 6 7  2  2 79 4  4 93 8  3 86 1  1 6 7  77  72  22  29  94  43  38  86  61  1 7 2 9 4  3 8 6 1  1 2 3 4 6 7 8 9

11. 11 Execution Example (cont.) Merge 7 2  9 4  2 4 7 93 8 6 1  1 3 8 6 7  2  2 7 9 4  4 93 8  3 86 1  1 6 7  77  72  22  29  94  43  38  86  61  1 7 2 9 4  3 8 6 1  1 2 3 4 6 7 8 9

12. 12 Execution Example (cont.) Recursive call, …, base case, merge 7 2  9 4  2 4 7 93 8 6 1  1 3 8 6 7  2  2 7 9 4  4 9 3 8  3 86 1  1 6 7  77  72  22  23  38  86  61  1 7 2 9 4  3 8 6 1  1 2 3 4 6 7 8 9 9  94  4

13. 13 Execution Example (cont.) Merge 7 2  9 4  2 4 7 9 3 8 6 1  1 3 8 6 7  2  2 79 4  4 93 8  3 86 1  1 6 7  77  72  22  29  94  43  38  86  61  1 7 2 9 4  3 8 6 1  1 2 3 4 6 7 8 9

14. 14 Execution Example (cont.) Recursive call, …, merge, merge 7 2  9 4  2 4 7 9 3 8 6 1  1 3 6 8 7  2  2 79 4  4 93 8  3 86 1  1 6 7  77  72  22  29  94  43  33  38  88  86  66  61  11  1 7 2 9 4  3 8 6 1  1 2 3 4 6 7 8 9

15. 15 Execution Example (cont.) Merge 7 2  9 4  2 4 7 93 8 6 1  1 3 6 8 7  2  2 79 4  4 93 8  3 86 1  1 6 7  77  72  22  29  94  43  33  38  88  86  66  61  11  1 7 2 9 4  3 8 6 1  1 2 3 4 6 7 8 9

16. 16 Mergesort: sort a collection of given integers in increasing order Example: { 4, 7, 2, 3, 0, 1, 9, 11, 5, 12, 13, 6, 15, 8, 10, 14 } sorts to { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 }

17. 17 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists.

18. 18 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36

19. 19 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36

20. 20 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1,

21. 21 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2,

22. 22 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2, 3,

23. 23 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2, 3, 5,

24. 24 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2, 3, 5, 6,

25. 25 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2, 3, 5, 6, 7,

26. 26 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2, 3, 5, 6, 7, 10,

27. 27 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2, 3, 5, 6, 7, 10, 11,

28. 28 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2, 3, 5, 6, 7, 10, 11, 12,

29. 29 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2, 3, 5, 6, 7, 10, 11, 12, 16,

30. 30 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2, 3, 5, 6, 7, 10, 11, 12, 16, 16,

31. 31 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2, 3, 5, 6, 7, 10, 11, 12, 16, 16, 24,

32. 32 Merge: given two lists of numbers in increasing order, produce a list of numbers in increasing order which contains all the numbers of the given lists. 3, 5, 7, 11, 16, 24 1, 2, 6, 10, 12, 16, 32, 36 1, 2, 3, 5, 6, 7, 10, 11, 12, 16, 16, 24, 32, 36

33. 33 { 4, 7, 2, 3, 0, 1, 9, 11, 5, 12, 13, 6, 15, 8, 10, 14 }

34. 34 { 4, 7, 2, 3, 0, 1, 9, 11, 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3, 0, 1, 9, 11 }{ 5, 12, 13, 6, 15, 8, 10, 14 }

35. 35 { 4, 7, 2, 3, 0, 1, 9, 11, 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3, 0, 1, 9, 11 }{ 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3 } { 0, 1, 9, 11 }{ 5, 12, 13, 6 } { 15, 8, 10, 14 }

36. 36 { 4, 7, 2, 3, 0, 1, 9, 11, 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3, 0, 1, 9, 11 }{ 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3 } { 0, 1, 9, 11 }{ 5, 12, 13, 6 } { 15, 8, 10, 14 } { 4, 7 } { 2, 3 } { 0, 1 } { 9, 11 } { 5, 12 } { 13, 6 } { 15, 8 } { 10, 14 }

37. 37 { 4, 7, 2, 3, 0, 1, 9, 11, 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3, 0, 1, 9, 11 }{ 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3 } { 0, 1, 9, 11 }{ 5, 12, 13, 6 } { 15, 8, 10, 14 } { 4, 7 } { 2, 3 } { 0, 1 } { 9, 11 } { 5, 12 } { 13, 6 } { 15, 8 } { 10, 14 } { 4, 7 } { 2, 3 } { 0, 1 } { 9, 11 }{ 5, 12 } { 6, 13 } { 8, 15 } { 10, 14 }

38. 38 { 4, 7, 2, 3, 0, 1, 9, 11, 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3, 0, 1, 9, 11 }{ 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3 } { 0, 1, 9, 11 }{ 5, 12, 13, 6 } { 15, 8, 10, 14 } { 4, 7 } { 2, 3 } { 0, 1 } { 9, 11 } { 5, 12 } { 13, 6 } { 15, 8 } { 10, 14 } { 4, 7 } { 2, 3 } { 0, 1 } { 9, 11 }{ 5, 12 } { 6, 13 } { 8, 15 } { 10, 14 } { 2, 3, 4, 7 } { 0, 1, 9, 11 }{ 5, 6, 12, 13 } { 8, 10, 14, 15 }

39. 39 { 4, 7, 2, 3, 0, 1, 9, 11, 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3, 0, 1, 9, 11 }{ 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3 } { 0, 1, 9, 11 }{ 5, 12, 13, 6 } { 15, 8, 10, 14 } { 4, 7 } { 2, 3 } { 0, 1 } { 9, 11 } { 5, 12 } { 13, 6 } { 15, 8 } { 10, 14 } { 4, 7 } { 2, 3 } { 0, 1 } { 9, 11 }{ 5, 12 } { 6, 13 } { 8, 15 } { 10, 14 } { 2, 3, 4, 7 } { 0, 1, 9, 11 }{ 5, 6, 12, 13 } { 8, 10, 14, 15 } { 0, 1, 2, 3, 4, 7, 9, 11 } { 5, 6, 8, 10, 12, 13, 14, 15 }

40. 40 { 4, 7, 2, 3, 0, 1, 9, 11, 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3, 0, 1, 9, 11 }{ 5, 12, 13, 6, 15, 8, 10, 14 } { 4, 7, 2, 3 } { 0, 1, 9, 11 }{ 5, 12, 13, 6 } { 15, 8, 10, 14 } { 4, 7 } { 2, 3 } { 0, 1 } { 9, 11 } { 5, 12 } { 13, 6 } { 15, 8 } { 10, 14 } { 4, 7 } { 2, 3 } { 0, 1 } { 9, 11 }{ 5, 12 } { 6, 13 } { 8, 15 } { 10, 14 } { 2, 3, 4, 7 } { 0, 1, 9, 11 }{ 5, 6, 12, 13 } { 8, 10, 14, 15 } { 0, 1, 2, 3, 4, 7, 9, 11 } { 5, 6, 8, 10, 12, 13, 14, 15 } { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 }

41. 41 Quick-Sort 7 4 9 6 2  2 4 6 7 9 4 2  2 47 9  7 9 2  29  9

42. 42 Outline Quick-sort Algorithm Partition step Quick-sort tree Execution example

43. 43 Quick-Sort Quick-sort is a randomized sorting algorithm based on the divide-and-conquer paradigm: Divide: pick a random element x (called pivot) and partition S into  L elements less than x  E elements equal x  G elements greater than x Recur: sort L and G Conquer: join L, E and G x x L G E x

44. 44 Partition We partition an input sequence as follows: We remove, in turn, each element y from S and We insert y into L, E or G, depending on the result of the comparison with the pivot x Each insertion and removal is at the beginning or at the end of a sequence, and hence takes O(1) time Thus, the partition step of quick-sort takes O(n) time Algorithm partition(S, p) Input sequence S, position p of pivot Output subsequences L, E, G of the elements of S less than, equal to, or greater than the pivot, resp. L, E, G  empty sequences x  S.remove(p) while  S.isEmpty() y  S.remove(S.first()) if y < x L.insertLast(y) else if y = x E.insertLast(y) else { y > x } G.insertLast(y) return L, E, G

45. 45 Quick-Sort Tree An execution of quick-sort is depicted by a binary tree Each node represents a recursive call of quick-sort and stores  Unsorted sequence before the execution and its pivot  Sorted sequence at the end of the execution The root is the initial call The leaves are calls on subsequences of size 0 or 1 7 4 9 6 2  2 4 6 7 9 4 2  2 47 9  7 9 2  29  9

46. 46 Execution Example Pivot selection 7 2 9 4  2 4 7 9 2  2 7 2 9 4 3 7 6 1  1 2 3 4 6 7 8 9 3 8 6 1  1 3 8 6 3  38  8 9 4  4 9 9  94  4

47. 47 Execution Example (cont.) Partition, recursive call, pivot selection 2 4 3 1  2 4 7 9 9 4  4 9 9  94  4 7 2 9 4 3 7 6 1  1 2 3 4 6 7 8 9 3 8 6 1  1 3 8 6 3  38  8 2  2

48. 48 Execution Example (cont.) Partition, recursive call, base case 2 4 3 1  2 4 7 1  11  1 9 4  4 9 9  94  4 7 2 9 4 3 7 6 1   1 2 3 4 6 7 8 9 3 8 6 1  1 3 8 6 3  38  8

49. 49 Execution Example (cont.) Recursive call, …, base case, join 3 8 6 1  1 3 8 6 3  38  8 7 2 9 4 3 7 6 1  1 2 3 4 6 7 8 9 2 4 3 1  1 2 3 4 1  11  14 3  3 4 9  94  44  4

50. 50 Execution Example (cont.) Recursive call, pivot selection 7 9 7 1  1 3 8 6 8  8 7 2 9 4 3 7 6 1  1 2 3 4 6 7 8 9 2 4 3 1  1 2 3 4 1  11  14 3  3 4 9  94  44  4

51. 51 Execution Example (cont.) Partition, …, recursive call, base case 7 9 7 1  1 3 8 6 8  8 7 2 9 4 3 7 6 1  1 2 3 4 6 7 8 9 2 4 3 1  1 2 3 4 1  11  14 3  3 4 9  94  44  4 9  99  9

52. 52 Execution Example (cont.) Join, join 7 9 7  17 7 9 8  8 7 2 9 4 3 7 6 1  1 2 3 4 6 7 7 9 2 4 3 1  1 2 3 4 1  11  14 3  3 4 9  94  44  4 9  99  9

53. 53 Expected Running Time Consider a recursive call of quick-sort on a sequence of size s Good call: the sizes of L and G are each less than 3s  4 Bad call: one of L and G has size greater than 3s  4 A call is good with probability 1  2 1/2 of the possible pivots cause good calls: 7 9 7 1  1 7 2 9 4 3 7 6 1 9 2 4 3 1 7 2 9 4 3 7 61 7 2 9 4 3 7 6 1 Good callBad call 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Good pivotsBad pivots

54. 54 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 i  2 ancestors are good calls The size of the input sequence for the current call is at most ( 3  4 ) i  2 n Therefore, we have For a node of depth 2log 4  3 n, the expected input size is one The expected height of the quick-sort tree is O(log n) The amount or work done at the nodes of the same depth is O(n) Thus, the expected running time of quick-sort is O(n log n)

55. 55 Selection

56. 56 The Selection Problem Given an integer k and n elements x 1, x 2, …, x n, taken from a total order, find the k-th smallest element in this set. Of course, we can sort the set in O(n log n) time and then index the k-th element. Can we solve the selection problem faster? 7 4 9 6 2  2 4 6 7 9 k=3

57. 57 Quick-Select Quick-select is a randomized selection algorithm based on the prune-and-search paradigm: Prune: pick a random element x (called pivot) and partition S into  L elements less than x  E elements equal x  G elements greater than x Search: depending on k, either answer is in E, or we need to recur on either L or G x x L G E k < |L| |L| < k < |L|+|E| (done) k > |L|+|E| k’ = k - |L| - |E|

58. 58 Partition We partition an input sequence as in the quick-sort algorithm: We remove, in turn, each element y from S and We insert y into L, E or G, depending on the result of the comparison with the pivot x Each insertion and removal is at the beginning or at the end of a sequence, and hence takes O(1) time Thus, the partition step of quick-select takes O(n) time Algorithm partition(S, p) Input sequence S, position p of pivot Output subsequences L, E, G of the elements of S less than, equal to, or greater than the pivot, resp. L, E, G  empty sequences x  S.remove(p) while  S.isEmpty() y  S.remove(S.first()) if y < x L.insertLast(y) else if y = x E.insertLast(y) else { y > x } G.insertLast(y) return L, E, G

59. 59 Quick-Select Visualization An execution of quick-select can be visualized by a recursion path Each node represents a recursive call of quick-select, and stores k and the remaining sequence k=5, S=(7 4 9 3 2 6 5 1 8) 5 k=2, S=(7 4 9 6 5 8) k=2, S=(7 4 6 5) k=1, S=(7 6 5)

60. 60 Summary of Sorting Algorithms AlgorithmTimeNotes selection-sort O(n2)O(n2) in-place slow (good for small inputs) insertion-sort O(n2)O(n2) 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)