Merge sort and quick sort

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CSC-391. Data Structure and Algorithm course material

Transcript of Merge sort and quick sort

  • 1. Merge Sort and Quick Sort CSC 391
  • 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 Sorting Selection sort Design approach: Sorts in place: Running time: Merge Sort Design approach: Sorts in place: Running time: Yes (n2) incremental No Lets see!! divide and conquer
  • 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 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 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 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 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 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 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 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. 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. 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 Quicksort Sort an array A[pr] 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[pq] A[q+1r]
  • 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[pq] A[q+1r]
  • 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 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[pq] A[q+1r] p r Each element is visited once!
  • 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(nk) T(k 1) n
  • 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. 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. Average-Case Complexity Average case is rather complex, but is where the algorithm earns its name. The bottom line is: T(n) = (nlgn)
  • 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. Best case: split in the middle ( n log n) Worst case: sorted array! ( n2) Average case: random arrays ( n log n) Worst Case Partitioning