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# Merge sort and quick sort

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CSC-391. Data Structure and Algorithm course material

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### Merge sort and quick sort

1. 1. Merge Sort and Quick Sort CSC 391
2. 2. 2 Sorting • Insertion sort – Design approach: – Sorts in place: – Best case: – Worst case: • Bubble Sort – Design approach: – Sorts in place: – Running time: Yes (n) (n2) incremental Yes (n2) incremental
3. 3. 3 Sorting • Selection sort – Design approach: – Sorts in place: – Running time: • Merge Sort – Design approach: – Sorts in place: – Running time: Yes (n2) incremental No Let’s see!! divide and conquer
4. 4. 4 Divide-and-Conquer • Divide the problem into a number of sub-problems – Similar sub-problems of smaller size • Conquer the sub-problems – Solve the sub-problems recursively – Sub-problem size small enough  solve the problems in straightforward manner • Combine the solutions of the sub-problems – Obtain the solution for the original problem
5. 5. 5 Merge Sort Approach • To sort an array A[p . . r]: • Divide – Divide the n-element sequence to be sorted into two subsequences of n/2 elements each • Conquer – Sort the subsequences recursively using merge sort – When the size of the sequences is 1 there is nothing more to do • Combine – Merge the two sorted subsequences
6. 6. 6 Merge Sort Alg.: MERGE-SORT(A, p, r) if p < r Check for base case then q ← (p + r)/2 Divide MERGE-SORT(A, p, q) Conquer MERGE-SORT(A, q + 1, r) Conquer MERGE(A, p, q, r) Combine • Initial call: MERGE-SORT(A, 1, n) 1 2 3 4 5 6 7 8 62317425 p rq
7. 7. 7 Example – n Power of 2 1 2 3 4 5 6 7 8 q = 462317425 1 2 3 4 7425 5 6 7 8 6231 1 2 25 3 4 74 5 6 31 7 8 62 1 5 2 2 3 4 4 7 1 6 3 7 2 8 6 5 Divide
8. 8. 8 Example – n Power of 2 1 5 2 2 3 4 4 7 1 6 3 7 2 8 6 5 1 2 3 4 5 6 7 8 76543221 1 2 3 4 7542 5 6 7 8 6321 1 2 52 3 4 74 5 6 31 7 8 62 Conquer and Merge
9. 9. 9 Example – n Not a Power of 2 62537416274 1 2 3 4 5 6 7 8 9 10 11 q = 6 416274 1 2 3 4 5 6 62537 7 8 9 10 11 q = 9q = 3 274 1 2 3 416 4 5 6 537 7 8 9 62 10 11 74 1 2 2 3 16 4 5 4 6 37 7 8 5 9 2 10 6 11 4 1 7 2 6 4 1 5 7 7 3 8 Divide
10. 10. 10 Example – n Not a Power of 2 77665443221 1 2 3 4 5 6 7 8 9 10 11 764421 1 2 3 4 5 6 76532 7 8 9 10 11 742 1 2 3 641 4 5 6 753 7 8 9 62 10 11 2 3 4 6 5 9 2 10 6 11 4 1 7 2 6 4 1 5 7 7 3 8 74 1 2 61 4 5 73 7 8 Conquer and Merge
11. 11. 11 Merge - Pseudocode Alg.: MERGE(A, p, q, r) 1. Compute n1 and n2 2. Copy the first n1 elements into L[1 . . n1 + 1] and the next n2 elements into R[1 . . n2 + 1] 3. L[n1 + 1] ← ; R[n2 + 1] ←  4. i ← 1; j ← 1 5. for k ← p to r 6. do if L[ i ] ≤ R[ j ] 7. then A[k] ← L[ i ] 8. i ←i + 1 9. else A[k] ← R[ j ] 10. j ← j + 1 p q 7542 6321 rq + 1 L R   1 2 3 4 5 6 7 8 63217542 p rq n1 n2
12. 12. Algorithm: mergesort( int [] a, int left, int right) { if (right > left) { middle = left + (right - left)/2; mergesort(a, left, middle); mergesort(a, middle+1, right); merge(a, left, middle, right); } } Assumption: N is a power of two. For N = 1: time is a constant (denoted by 1) Otherwise, time to mergesort N elements = time to mergesort N/2 elements + time to merge two arrays each N/2 elements. Time to merge two arrays each N/2 elements is linear, i.e. N Thus we have: (1) T(1) = 1 (2) T(N) = 2T(N/2) + N Next we will solve this recurrence relation. First we divide (2) by N: (3) T(N) / N = T(N/2) / (N/2) + 1 Complexity of Merge Sort
13. 13. N is a power of two, so we can write (4) T(N/2) / (N/2) = T(N/4) / (N/4) +1 (5) T(N/4) / (N/4) = T(N/8) / (N/8) +1 (6) T(N/8) / (N/8) = T(N/16) / (N/16) +1 (7) …… (8) T(2) / 2 = T(1) / 1 + 1 Now we add equations (3) through (8) : the sum of their left-hand sides will be equal to the sum of their right-hand sides: T(N) / N + T(N/2) / (N/2) + T(N/4) / (N/4) + … + T(2)/2 = T(N/2) / (N/2) + T(N/4) / (N/4) + ….+ T(2) / 2 + T(1) / 1 + LogN (LogN is the sum of 1s in the right-hand sides) After crossing the equal term, we get (9) T(N)/N = T(1)/1 + LogN T(1) is 1, hence we obtain (10) T(N) = N + NlogN = O(NlogN) Hence the complexity of the MergeSort algorithm is O(NlogN). Complexity of Merge Sort
14. 14. 14 Quicksort • Sort an array A[p…r] • Divide – Partition the array A into 2 subarrays A[p..q] and A[q+1..r], such that each element of A[p..q] is smaller than or equal to each element in A[q+1..r] – Need to find index q to partition the array ≤A[p…q] A[q+1…r]
15. 15. 15 Quicksort • Conquer – Recursively sort A[p..q] and A[q+1..r] using Quicksort • Combine – Trivial: the arrays are sorted in place – No additional work is required to combine them – The entire array is now sorted A[p…q] A[q+1…r]≤
16. 16. 16 Recurrence Alg.: QUICKSORT(A, p, r) if p < r then q  PARTITION(A, p, r) QUICKSORT (A, p, q) QUICKSORT (A, q+1, r) Recurrence: Initially: p=1, r=n T(n) = T(q) + T(n – q) + n
17. 17. 17 Partitioning the Array Alg. PARTITION (A, p, r) 1. x  A[p] 2. i  p – 1 3. j  r + 1 4. while TRUE 5. do repeat j  j – 1 6. until A[j] ≤ x 7. do repeat i  i + 1 8. until A[i] ≥ x 9. if i < j 10. then exchange A[i]  A[j] 11. else return j Running time: (n) n = r – p + 1 73146235 i j A: arap ij=q A: A[p…q] A[q+1…r]≤ p r Each element is visited once!
18. 18. Partition can be done in O(n) time, where n is the size of the array. Let T(n) be the number of comparisons required by Quicksort. If the pivot ends up at position k, then we have To determine best-, worst-, and average-case complexity we need to determine the values of k that correspond to these cases. Analysis of quicksort T(n) T(nk)  T(k 1)  n
19. 19. Best-Case Complexity The best case is clearly when the pivot always partitions the array equally. Intuitively, this would lead to a recursive depth of at most lg n calls We can actually prove this. In this case – T(n) T(n/2)  T(n/2)  n  (n lg n)
20. 20. Best Case Partitioning • Best-case partitioning – Partitioning produces two regions of size n/2 • Recurrence: q=n/2 T(n) = 2T(n/2) + (n) T(n) = (nlgn) (Master theorem)
21. 21. Average-Case Complexity Average case is rather complex, but is where the algorithm earns its name. The bottom line is: T(n) = (nlgn)
22. 22. Worst Case Partitioning The worst-case behavior for quicksort occurs when the partitioning routine produces one region with n - 1 elements and one with only l element. Let us assume that this Unbalanced partitioning arises at every step of the algorithm. Since partitioning costs (n) time and T(1) = (1), the recurrence for the running time is T(n) = T(n - 1) + (n). To evaluate this recurrence, we observe that T(1) = (1) and then iterate: n n - 1 n - 2 n - 3 2 1 1 1 1 1 1 n n n n - 1 n - 2 3 2 (n2)
23. 23. Best case: split in the middle — Θ( n log n) Worst case: sorted array! — Θ( n2) Average case: random arrays — Θ( n log n) Worst Case Partitioning