Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- Quick sort algo analysis by sonabir 752 views
- The Pillars Of Self Mastery by professor shamshad 473 views
- family by mohammedqassim1963 284 views
- 5 60 Kumarasiri VP and seedling tea by University of Sri... 888 views
- Containers virtaulization and docker by Luqman Shareef 153 views
- Library orientation by KVander 398 views

No Downloads

Total views

1,817

On SlideShare

0

From Embeds

0

Number of Embeds

253

Shares

0

Downloads

133

Comments

0

Likes

1

No embeds

No notes for slide

- 1. 1302251 Data Structures and Algorithms Lecture 11 Sorting Aj. Khwunta Kirimasthong School of Information Technology Mae Fah Luang University
- 2. Outline Sorting Concept Sorting Algorithm Selection Sort Insertion Sort Merge Sort Quick Sort 2
- 3. Sorting Concept Sorting is the operation of arranging data in some given order, such as Ascending the dictionary, the telephone book Descending grade-point average for honor students The sorted data can be numerical data or character data. Sorting is one of the most common data-processing applications. 3
- 4. Sorting Concept (Cont.) Sorting problem Let A be a sequence of n elements. Sorting A refers to the operation of rearranging the content of A so that they are increasing in order. Input: A: the sequence of n numbers ( a1 , a 2 , ..., a n ) Output: A: the reordering ( a1 , a 2 ,..., a n ) of the input sequence such that a1 a2 an 4
- 5. Selection Sort One of basic sorting algorithms Selection Sort works as follows: First, find the smallest elements in the data sequence and exchange it with the element in the first position, Then, find the second smallest element and exchange it with the element in the second position and Continue in this way until the entire sequence is sorted. 5
- 6. Selection Sort Example 1 2 3 4 5 6 1 2 3 4 5 6 A 5 2 4 6 1 3 (1) A 1 2 4 6 5 3 1 2 3 4 5 6 (2) A 1 2 4 6 5 3 1 2 3 4 5 6 (3) A 1 2 3 6 5 4 1 2 3 4 5 6 (4) A 1 2 3 4 5 6 1 2 3 4 5 6 (5) A 1 2 3 4 5 6 1 2 3 4 5 6 (6) A 1 2 3 4 5 6 6
- 7. Selection Sort Algorithm Algorithm SelectionSort(A,n) Input: A: a sequence of n elements n: the size of A Output: A: a sorted sequence in ascending order for i = 1 to n min = i for j = i+1 to n if A[j] < A[min] then min = j Swap(A,min,i) 7
- 8. Selection Sort Algorithm (Cont.) Algorithm: Swap(A,min,i) Input: A : a sequence of n elements min: the position of the minimum value of A while considers at index i of A i: the considered index of A Output: exchange A[i] and A[min] temp = a[min] a[min] = a[i] a[i] = temp 8
- 9. Selection Sort Analysis For i = 1, there are _(n-1)_ comparisons to find the first smallest element. For i = 2, there are _(n-2)_ comparisons to find the second smallest element, and so on. Accordingly, n 1 (n-1) + (n-2) + (n-3) + … + 2 + 1 = i = n(n-1)/2 i 1 The best case running time is ___ (n2)__ The worst case running time is __ O(n2)__ 9
- 10. Insertion Sort To solve the sorting problem Insertion sort algorithm is almost as simple as selection sort but perhaps more flexible. Insertion sort is the method people often use for sorting a hand of cards. 10
- 11. Insertion Sort (Cont.) Insertion sort uses an incremental approach: having the sorted subarray A[1…j-1], we insert the single element A[ j ] into its proper place and then we will get the sorted subarray A[ 1…j ] pick up Sorted order Unsorted order 11
- 12. Insertion Sort Example 1 2 3 4 5 6 5 2 4 6 1 3 key = A[j] 1 2 3 4 5 6 (1) A 5 2 4 6 1 3 key 2 1 2 3 4 5 6 (2) A 2 5 4 6 1 3 key 4 1 2 3 4 5 6 (3) A 2 4 5 6 1 3 key 6 in-place sorted 12
- 13. Insertion Sort Example (Cont.) 1 2 3 4 5 6 (4) A 2 4 5 6 1 3 key 1 1 2 3 4 5 6 (5) A 1 2 4 5 6 3 key 3 1 2 3 4 5 6 (6) A 1 2 3 4 5 6 in-place sorted 13
- 14. Insertion Sort Algorithm Algorithm: INSERTION-SORT(A) Input: A : A sequence of n numbers Output: A : A sorted sequence in increasing order of array A for j = 2 to length(A) key = A[j] //Insert A[j] into the sorted sequence A[1…j-1] i = j-1 while i > 0 and A[i] > key A[i+1] = A[i] i = i-1 A[i+1] = key 14
- 15. Insertion Sort Analysis The running – time of INSERTION-SORT depends on the size of input. 15
- 16. Insertion Sort Analysis (cont.) INSERTION-SORT(A) cost times 1. for j = 2 to length(A) c1 n 2. key = A[j] c2 n-1 3. //Insert A[j] into the sorted sequence A[1…j-1] 0 n-1 4. i = j-1 c4 n-1 n 5. while i > 0 and A[i] > key c5 tj j 2 n 6. A[i+1] = A[i] c6 (t j 1) j 2 n 7. i = i-1 c7 (t j 1) j 2 8. A[i+1] = key c8 n-1 16
- 17. Analysis of Insertion Sort (Cont.) To Compute the total running time of INSERTION- SORT, T ( n ) n T (n) c1 n c2 (n 1) 0(n 1) c4 (n 1) c5 tj j 2 n n c6 (t j 1) c7 (t j 1) c8 ( n 1) j 2 j 2 17
- 18. Analysis of Insertion Sort (Cont.) Best – case If input array is already sorted. Thus tj = 1 T(n) = c1n+c2(n-1)+(0)(n-1)+c4(n-1)+c5(n-1)+c6(0)+c7(0)+c8(n-1) = (c1+c2+c3+c4+c5+c8)n - (c2+c3+c4+c5+c8) an + b for constant a and b Base-case running time of INSERTION-SORT is (n) 18
- 19. Analysis of Insertion Sort (Cont.) Worse – case If input array is in reverse sorted order. Thus tj = j n(n 1) T (n) c1 n c2 (n 1) c4 (n 1) c5 1 2 (n 1) n (n 1) n c6 c7 c8 ( n 1) 2 2 c5 c6 c6 2 c5 c6 c7 T (n) n c1 c2 c4 c8 n 2 2 2 2 2 2 (c2 c4 c5 c8 ) an 2 bn c for constant a, b and c 2 Worse-case running time of INSERTION-SORT is (n ) 19
- 20. Analysis of Insertion Sort (Cont.) Worse – case (Cont.) If input array is in reverse sorted order. Thus tj = j n n n n(n 1) n(n 1) j tj j 1 j 1 2 j 2 j 2 2 n n (n 1)(( n 1) 1) (n 1) n (t j 1) (j 1) (1 1) j 2 j 2 2 2 20
- 21. Divide and Conquer Approach Merge Sort Quick Sort
- 22. Divide-and-Conquer Approach The divide-and-conquer approach involves three steps at each level of the recursion: Divide the problem into a number of sub-problems that are similar to the original problem but smaller in size. Conquer the sub-problems by solving them recursively. Combine the solution of subproblems into the solution for the original problem. 22
- 23. Merge Sort The Merge Sort algorithm closely follows the divide-and-conquer approach. It operates as follows, Divide: Divide the n-element sequence to be sorted into two subsequences of n/2 elements each. Conquer: Sort the two subsequences recursively using merge sort Combine: Merge the two sorted subsequences to produce the sorted solution. 23
- 24. Merge Sort (Cont.) The recursion “bottom out” when the sequence to be sorted has length 1, in which case there is no work to be done, since every sequence of length 1 is already in sorted order. 24
- 25. Merge Sort Example DIVIDE & 0 1 2 3 4 5 CONQUER A 5 2 4 6 1 3 0 1 2 3 4 5 5 2 4 6 1 3 0 1 2 3 4 5 5 2 4 6 1 3 0 1 3 4 5 2 6 1 25
- 26. Merge Sort Example (cont.) 0 1 2 3 4 5 COMBINE A 1 2 3 4 5 6 0 1 2 3 4 5 2 4 5 1 3 6 0 1 2 3 4 5 2 5 4 1 6 3 0 1 3 4 5 2 6 1 26
- 27. Merge Sort Algorithm Algorithm MergeSort( A, p, q ) Input: A: a sequence of n elements p: the beginning index of A q: the last index of A Output: A: a sorted sequence of n elements if(p < q) then r = floor((p+q)/2) MergeSort(A,p,r) MergeSort(A,r+1,q) Merge(A,p,r,q) 27
- 28. Algo Merge(A,p,r,q) Input: A, p, r, q : a sorted subsequences A[p…r] and A[r+1…q] Output: A: a sorted sequence A[p…q] let i=p, k=p, j=r+1 while (i ≤ r) and (j ≤ q) if (A[i] ≤ A[j]) then B[k] = A[i] k++ i++ else B[k] = A[j] k=k+1 j=j+1 if (i>r) then /* If the maximum value of left subsequence is less than the right subsequence */ while(j ≤ q) B[k] = A[j] k++ j++ else if(j > q) then /* If the maximum value of left subsequence is gather than the right subsequence */ while(i ≤ r) B[k] = A[i] k++ i++ for k = p to q A[k] = b[k] 28
- 29. Merge Sort Analysis The best case running time is _ (n log n)_ The worst case running time is _ O(n log n) _ 29
- 30. Quick Sort Quick Sort, likes merge sort, is based on the divide-and-conquer approach. To sort an array A[p…r] Divide: Partition (rearrange) the array A[p…r] into two subarray A[p…q -1] and subarray A[q+1…r] such that: Each element of A[p…q -1] is less than or equal to A[q] Each element of A[q+1…r] is greater than A[q] 30
- 31. Quick Sort (Cont.) To sort sorting an array A[p…r] (cont.) Conquer: Sort the two subarray A[p…q -1] and A[q+1…r] by recursive calls to QuickSort. Combine: Since the subarrays are sorted in place, no work is needed to combine them: the entire array A[p…r] is now sorted. 31
- 32. Quick Sort Example 0 1 2 3 4 5 A 5 2 4 6 1 3 32
- 33. Quick Sort Algorithm Algo QuickSort(A, p, r) Input: A: A sequence of n numbers p: The first index of A r: The last index of A Output: A: A sorted sequence in increasing order of array A • Rearrange the subarray if (p < r) then A[p…r] in place. • The elements that is less q = Partition(A,p,r) than A[q] are placed at the left side of A[q] and QuickSort(A,p,q-1) • The elements that is greater than A[q] are placed QuickSort(A,q+1,r) at the right side of A[q]. 33
- 34. Quick Sort Algorithm (Cont.) Algo Partition(A, p, r) Input: A: A sequence of n numbers p: The first index of A r: The last index of A Output: A: The rearranged subarray A[p…r] key = A[r] i=p–1 for j = p to r -1 if A[ j ] ≤ key then i = i+1 exchange A[i] and A[j] exchange A[i+1] and key return i+1 34
- 35. Quick Sort Analysis The best case running time is _ (n log n)_ The worst case running time is _ O(n2)_ 35
- 36. Q&A

No public clipboards found for this slide

Be the first to comment