1. C Programming Week 10 Sorting Algorithms 1

2. Sorting In many queries we handle a list of values and want a specific value. We can go through all the list until we find the requested value. But better… We can sort the list and then find it more easily (e.g., by binary search)

3. Sorting In class we saw several examples for sorting: –Bubble Sort –Merge Sort Today we will continue with examples… In these examples, each time we compare between two elements, resulting in a sorted list.

4. Quick Sort Divide and conquer –Divide the list into two sublists such that all elements from one list are greater than all elements from other list. –Sort each of these sublists recursively…

5. Quick Sort How we divide the list into two sublists? We pick up an element (pivot) and put all bigger elements to its right and all smaller elements to its left 8 39 15141367 13 815 679314

6. Quick Sort At this stage, the pivot is in its final position 8 39 15141367 13 815 679314

7. How do we choose the pivot? Ideally, we should take the median value However, it would take time to find what is the median, so we can take the: –First –Middle –Last –Any random element In practice, each such choice is relatively efficient.

8. Some visualization… http://www.youtube.com/watch?v=vxENKlcs2Tw http://www.youtube.com/watch?v=y_G9BkAm6B8

9. How do we implement this? First, we look on the ‘backbone’ of the algorithm: –Partitioning the list according to a pivot into two sublists. –After this stage, the pivot value is in its final position. –Recursively repeat this procedure to each of the two sublists.

10. Quick Sort (backbone) void quickSort (int list[], int start_index, int end_index) // This function implements the Quick Sort algorithm { int pivot_index; if (start_index<end_index) // more than one element in list { // We now partition the list into two sublists: pivot_index=partition(list, start_index, end_index); // Performing Quick Sort on both sublists: quickSort(list,start_index,pivot_index-1); quickSort(list,pivot_index+1,end_index); } 10 Where is the termination of the recursive function?

11. Partition 11 int partition (int list[], int start_index, int end_index) { int i,j; int pivot_index=choosePivot(start_index,end_index); int pivot_value=list[pivot_index]; swap(&list[start_index],&list[pivot_index]); i=start_index+1; j=end_index; while (i<=j) { while ((i<=end_index) &&(list[i]<=pivot_value)) i++; while ((j>=start_index) && (list[j]>pivot_value)) j--; if (i<j) swap(&list[i],&list[j]); } swap(&list[start_index],&list[j]); pivot_index=j; return pivot_index; }

12. main 12 int main() { int array[SIZE]={0}; int i; printf ("Please insert the numbers:\n"); for (i=0;i<SIZE;i++) { scanf ("%d",&array[i]); } printf ("The numbers before:\n"); printList(array,SIZE); quickSort (array,0,SIZE-1); printf ("The sorted numbers:\n"); printList(array,SIZE); return 0; }

13. Auxiliary Functions 13 int choosePivot(int start_index,int end_index) { return (end_index); } void swap(int *x, int *y) { int temp=*x; *x=*y; *y=temp; } void printList(int list[], int size) { int i; for (i=0;i<size;i++) { printf ("%d ",list[i]); } printf ("\n"); }

14. Sorting strings Similar to numbers we can sort strings according to their lexicographical order. What should we change for that? –Instead of comparing two numbers we should compare two strings according to their lexicographical order –Handling strings in input/printing etc.

15. Changing the comparison From (numbers): while (i<=j) { while ((i<=end_index) &&(list[i]<=pivot_value)) i++; while ((j>=start_index) && (list[j]>pivot_value)) j--; if (i<j) swap(&list[i],&list[j]); }

16. Changing the comparison To (strings): while (i<=j) { while ((i<=end_index) &&(strcmp(list[i],pivot_value)<=0)) i++; while ((j>=start_index) && (strcmp(list[j],pivot_value)>0)) j--; if (i<j) swap(list[i],list[j]); } Reminder: strcmp gets two strings and returns 0 if they are identical. Otherwise, it returns a negative or positive number if the first or second string appears first in lexicographical order, respectively. Now each element is a string, so we don’t need the ‘&’

17. The new Partition 17 int partition (char list[][SIZE], int start_index, int end_index) { int i,j; int pivot_index=choosePivot(start_index,end_index); char pivot_string[SIZE]=""; strcpy(pivot_string,list[pivot_index]); swap(list[start_index],list[pivot_index]); i=start_index+1; j=end_index; while (i<=j) { while ((i<=end_index) &&(strcmp(list[i],pivot_string)<=0)) i++; while ((j>=start_index) && (strcmp(list[j],pivot_string)>0)) j--; if (i<j) swap(list[i],list[j]); } swap(list[start_index],list[j]); pivot_index=j; return pivot_index; }

18. main 18 int main() { char list[NUM][SIZE]={""}; int i; printf ("Please insert the strings:\n"); for (i=0;i<NUM;i++) { readString(list[i],SIZE-1); } printf ("The strings before sorting:\n"); printList(list,NUM); quickSort (list,0,NUM-1); printf ("The sorted strings:\n"); printList(list,NUM); return 0; }

19. Auxiliary Functions 19 void readString(char string[],int size) { int i=0; char c; scanf("%c",&c); while ((c!='\n')&&(i<size)) { string[i]=c; i++; scanf("%c",&c); } string[i]='\0'; } void swap(char x[], char y[]) { int i=0; char temp[SIZE]=""; strcpy(temp,x); strcpy(x,y); strcpy(y,temp); }

20. Insertion Sort Another example for sorting algorithm is Insertion Sort: –In average, It is less efficient as compared to Quick Sort. –For small lists it is quicker as compared to other algorithms. –Many people perform similar algorithm when sorting elements (cards etc.) We will explain this algorithm in class while you will implement it in HW…

21. Insertion Sort The idea behind the algorithm: Go through the list from the first element and at each step put the i’th elements in its position in the sorted list that contain 1,…,i-1,i elements. At this stage, the first i elements are sorted. Stopping in i=n, we have a sorted list.

22. Example

23. Comparing Sorting… Quick Sort Insertion Sort

24. Complexity of algorithms We can compare the two algorithms in respect to time complexity. Here we shall introduce only the highlights of that complexity analysis.

25. Insertion Sort Best case scenario: –The list is already sorted. In this case, the i’th element is compared only to i-1 element. Thus, we have ~n comparisons. Worst case scenario: –The list is sorted backwards. In this case the i’th element needs to be shifted all the way back to the start of the list. –Quadratic complexity: 1+2+3+..+n-1=~n 2 Average case (without proving): –It also takes ~n 2 operations.

26. Quick Sort The partition step takes ~n operations (we go through the list and change the location of the elements according to the pivot). Best case scenario: –In each partition, we divide the list into almost equal size lists (each containing (k-1)/2 elements). That means we have ~log(n) partitions. Therefore total time complexity is ~n*log(n).

27. Quick Sort Worst case scenario: –In each step we divide the list of k elements (starting from k=n) into k-1 and 1 elements. In this case each step takes ~n-k operations. Thus we have: (n-1)+(n-2)+(n-3)+…+1=~n 2.

28. Quick Sort Average case (without proving): –In this case it takes ~1.39n*log(n) operations. Thus, Quick Sort is faster than Insertion Sort on average. In fact, any algorithm for sorting n elements using pairwise comparisons cannot do it faster than ~n*log(n) in the average case.

29. Selection Sort Selection Sort is another sorting algorithm which is relatively simple: Given a list of elements: –Find the minimum and swap it with the first element. –Repeat it for the remainder of the list (now swapping with the 2 nd, 3 rd element etc.).

30. Selection Sort - Example

31. Complexity analysis In each step we look for the minimal element – thus we go through all the remaining elements. This is done n times. Time complexity is: (n)+(n-1)+(n-2)+…+1=~n 2 This is done on all inputs, therefore best case=worst case=average case.

32. And the winner is… Quick Sort Insertion Sort Selection Sort