Searching techniques Searching It is a process to

Searching techniques Searching : It is a process to find whether a particular value with specified properties is present or not among a collection of items. If the value is present in the collection, then searching is said to be successful, and it returns the location of the value in the array. Otherwise, if the value is not present in the array, the searching process displays the appropriate message and in this case searching is said to be unsuccessful. 1) Linear or Sequential Searching 2) Binary Searching int main( ) { int arr [ 50 ] , num , i , n , pos = -1; printf ("How many elements to sort : "); scanf ("%d", &n); printf ("n Enter the elements : nn"); for( i = 0; i < n; i++ ) { printf (“arr [%d ] : “ , i ); scanf( "%d", &arr[ i ] ); } printf(“n. Enter the number to be searched : “); scanf(“%d”, &num); for(i=0; i<n; i++) if( arr [ i ] == num ) { pos = i ; break; } if ( pos == -1 ) printf(“ %d does not exist ”, num); else printf(“ %d is found at location : %d”, num , pos); Linear_Search (A[ ], N, val , pos ) Step 1 : Set pos = -1 and k = 0 Step 2 : Repeat while k < N Begin Step 3 : if A[ k ] = val Set pos = k print pos Goto step 5 End while Step 4 : print “Value is not present” Step 5 : Exit Searches -- for each item one by one in the list from the first, until the match is found. Efficiency of Linear search : -- Executes in O ( n ) times where n is the number of elements in the list.

Binary Searching Algorithm: • Before searching, the list of items should be sorted in ascending order. • We first compare the key value with the item in the position of the array. If there is a match, we can return immediately the position. • if the value is less than the element in middle location of the array, the required value is lie in the lower half of the array. • if the value is greater than the element in middle location of the array, the required value is lie in the upper half of the array. • We repeat the above procedure on the lower half or upper half of the array. Binary_Search (A [ ], U_bound, VAL) Step 1 : set BEG = 0 , END = U_bound , POS = -1 Step 2 : Repeat while (BEG <= END ) Step 3 : set MID = ( BEG + END ) / 2 Step 4 : if A [ MID ] == VAL then POS = MID print VAL “ is available at “, POS Go. To Step 6 End if if A [ MID ] > VAL then set END = MID – 1 Else set BEG = MID + 1 End if End while Step 5 : if POS = -1 then print VAL “ is not present “ End if Step 6 : EXIT void binary_serch ( int a [], int n, int val ) { int end = n - 1, beg = 0, pos = -1; while( beg <= end ) { mid = ( beg + end ) / 2; if ( val == a [ mid ] ) { pos = mid; printf(“%d is available at %d”, val, pos ); break; } if ( a [ mid ] > val ) end = mid – 1; else beg = mid + 1; } if ( pos = - 1) printf( “%d does not exist “, val ); }

Sorting is a technique to rearrange the elements of a list in ascending or descending order, which can be numerical, lexicographical, or any user-defined order. Ranking of students is the process of sorting in descending order. EMCET Ranking is an example for sorting with user-defined order. EMCET Ranking is done with the following priorities. i) First priority is marks obtained in EMCET. ii) If marks are same, the ranking will be done with comparing marks obtained in the Mathematics subject. iii) If marks in Mathematics subject are also same, then the date of births will be compared. Internal Sorting : If all the data that is to be sorted can be accommodated at a time in memory is called internal sorting. External Sorting : It is applied to Huge amount of data that cannot be accommodated in memory all at a time. So data in disk or file is loaded into memory part by part. Each part that is loaded is sorted separately, and stored in an intermediate file and all parts are merged into one single sorted list. Types of Internal Sortings Ø Bubble Sort Ø Insertion Sort Ø Selection Sort Ø Quick Sort Ø Merge Sort

Bubble Sort Bubbles up the highest Sorted Unsorted 10 54 54 54 47 10 47 47 12 47 10 23 23 23 54 12 23 10 19 19 19 23 12 19 10 12 23 19 19 12 12 10 After Pass 2 After Pass 3 After Pass 4 After Pass 5 Original After List Pass 1 Bubble_Sort ( A [ ] , N ) Step 1 : Repeat For P = 1 to N – 1 Begin Step 2 : Repeat For J = 1 to N – P Begin Step 3 : If ( A [ J ] < A [ J – 1 ] ) Swap ( A [ J ] , A [ J – 1 ] ) End For Step 4 : Exit Complexity of Bubble_Sort The complexity of sorting algorithm is depends upon the number of comparisons that are made. Total comparisons in Bubble sort is n ( n – 1) / 2 ≈ n 2 – n Complexity = O ( n 2 )

void print_array (int a[ ], int n) { int i; for (i=0; I < n ; i++) printf("%5 d", a[ i ]); } void bubble_sort ( int arr [ ], int n) { int pass, current, temp; for ( pass=1; (pass < n) ; pass++) { for ( current=1; current <= n – pass ; current++) { if ( arr[ current - 1 ] > arr[ current ] ) { temp = arr[ current - 1 ]; arr[ current - 1 ] = arr[ current ]; arr[ current ] = temp; } } int main() { int count, num[50], i ; printf ("How many elements to be sorted : "); scanf ("%d", &count); printf("n Enter the elements : nn"); for ( i = 0; i < count; i++) { printf ("num [%d] : ", i ); scanf( "%d", &num[ i ] ); } printf("n Array Before Sorting : nnn"); print_array ( num, count ); bubble_sort ( num, count); printf("nnn Array After Sorting : nnn"); print_array ( num, count ); } Bubble Sort For pass = 1 to N - 1 For J = 1 to N - pass A[J– 1]>A[J] F Return T Temp = A [ J – 1 ] A[J– 1]=A[J] A [ J ] = Temp

Insertion Sort TEMP 78 23 45 8 32 36 23 78 45 8 32 36 23 45 78 8 32 36 8 23 45 78 32 36 23 45 8 32 8 23 32 45 78 36 8 23 32 36 45 78 36 Complexity of Insertion Sort Best Case : O ( n ) Average Case : O ( n 2 ) Worst Case : O ( n 2 ) Insertion_Sort ( A [ ] , N ) Step 1 : Repeat For K = 1 to N – 1 Begin Step 2 : Set Temp = A [ K ] Step 3 : Set J = K – 1 Step 4 : Repeat while Temp < A [ J ] AND J >= 0 Begin Set A [ J + 1 ] = A [ J ] Set J = J - 1 End While Step 5 : Set A [ J + 1 ] = Temp End For Step 4 : Exit insertion_sort ( int A[ ] , int n ) { int k , j , temp ; for ( k = 1 ; k < n ; k++ ) { temp = A [ k ] ; j = k - 1; while ( ( temp < A [ j ] ) && ( j >= 0 ) ) { A[j+1] =A[j]; j--; } A [ j + 1 ] = temp ; } }

Selection Sort ( Select the smallest and Exchange ) Smallest 8 23 78 45 8 32 56 23 8 78 45 23 32 56 32 8 23 45 78 32 56 45 8 23 32 78 45 56 56 8 23 32 45 78 56 8 23 32 45 56 78 Complexity of Selection Sort Best Case : O ( n 2 ) Average Case : O ( n 2 ) Worst Case : O ( n 2 ) Selection_Sort ( A [ ] , N ) Step 1 : Repeat For K = 0 to N – 2 Begin Step 2 : Set POS = K Step 3 : Repeat for J = K + 1 to N – 1 Begin If A[ J ] < A [ POS ] Set POS = J End For Step 5 : Swap A [ K ] with A [ POS ] End For Step 6 : Exit selection_sort ( int A[ ] , int n ) { int k , j , pos , temp ; for ( k = 0 ; k < n - 1 ; k++ ) { pos = k ; for ( j = k + 1 ; j <= n ; j ++ ) { if ( A [ j ] < A [ pos ] ) pos = j ; } temp = A [ k ] ; A [ k ] = A [ pos ] ; A [ pos ] = temp ; } }

Insertion sort k = 1; k < n ; k++ temp = a [ k ] j=k-1 Selection sort k = 0; k < n - 1 ; k++ pos = k j = k + 1 ; j < n ; j++ temp < a [ j ] && j >= 0 a[ j ] < a[ pos ] a[j+1]=a[j] j=j-1 a [ j + 1 ] = temp pos = j temp = a[ k ] a [ k ] = a [ pos ] = temp return

Bubble sort – Insertion sort – Selection sort Bubble Sort : -- very primitive algorithm like linear search, and least efficient. -- No of swappings are more compare with other sorting techniques. -- It is not capable of minimizing the travel through the array like insertion sort. Insertion Sort : -- sorted by considering one item at a time. -- efficient to use on small sets of data. -- twice as fast as the bubble sort. -- 40% faster than the selection sort. -- no swapping is required. -- It is said to be online sorting because it continues the sorting a list as and when it receives new elements. -- it does not change the relative order of elements with equal keys. -- reduces unnecessary travel through the array. -- requires low and constant amount of extra memory space. -- less efficient for larger lists. Selection sort : -- No of swappings will be minimized. i. e. , one swap on one pass. -- generally used for sorting files with large objects and small keys. -- It is 60% more efficient than bubble sort and 40% less efficient than insertion sort. -- It is preferred over bubble sort for jumbled array as it requires less items to be exchanged. -- uses internal sorting that requires more memory space. -- It cannot recognize sorted list and carryout the sorting from the beginning, when new elements are added to the list.

Quick Sort – A recursive process of sorting Original-list of 11 elements : 8 3 2 11 5 14 0 2 9 4 20 Set list [ 0 ] as pivot : pivot 8 3 2 11 5 14 0 2 9 4 20 Rearrange ( partition ) the elements into two sub lists : pivot 4 3 2 2 5 Sub-list of lesser elements 0 8 11 9 14 20 Algorithm for Quick_Sort : -- set the element A [ start_index ] as pivot. -- rearrange the array so that : -- all elements which are less than the pivot come left ( before ) to the pivot. -- all elements which are greater than the pivot come right ( after ) to the pivot. -- recursively apply quick-sort on the sub-list of lesser elements. -- recursively apply quick-sort on the sub-list of greater elements. -- the base case of the recursion is lists of size zero or one, which are always sorted. Sub-list of greater elements Complexity of Quick Sort Apply Quick-sort recursively on sub-list Best Case : O ( n log n ) Average Case : O ( n log n ) Worst Case : O ( n 2 )

Partitioning for ‘ One Step of Quick Sort ’ Pivot 9 9 3 12 8 16 1 25 10 12 16 3 3 1 8 25 10 12 16 25 10 12

Quick Sort – Program int partition ( int a [ ], int beg, int end ) { int left , right , loc , flag = 0, pivot ; loc = left = beg; right = end; pivot = a [ loc ] ; while ( flag == 0 ) { while( (pivot <= a [ right ] )&&( loc != right ) ) right - - ; if( loc == right ) flag = 1; else { a [ loc ] = a [ right ] ; left = loc + 1 ; loc = right; } while ( (pivot >= a [ left ] ) && ( loc != left ) ) left++; if( loc == left ) flag = 1; else { a [ loc ] = a [ left ] ; right = loc - 1; loc = left; } } a [ loc ] = pivot; return loc; } void quick_sort(int a[ ] , int beg , int end ) { int loc; if ( beg < end ) { loc = partition( a , beg , end ); quick_sort ( a , beg , loc – 1 ); quick_sort ( a , loc + 1 , end ); } } void print_array (int a [ ], int n) { int i; for ( i = 0 ; I < n ; i++ ) printf( "%5 d“ , a [ i ] ) ; } int main () { int count , num[ 50 ] , i ; printf ("How many elements to sort : "); scanf ("%d", &count ); printf ("n Enter the elements : nn"); for( i = 0; i < count; i++ ) { printf ("num [%d ] : “ , i ); scanf( "%d", &num[ i ] ); } printf (“ n Array Before Sorting : nnn“ ); print_array ( num , count ) ; quick_sort ( num , 0 , count-1) ; printf ( "nnn Array After Sorting : nnn“ ); print_array ( num , count ); }

partition ( int a [ ], int beg, int end ) B A F loc = left = beg flag = 0, right = end pivot = a [ loc ] = a [ left ] right = loc - 1 ; loc = left; Flag == 0 T loc == left flag = 1 pivot <= a [ right ] && loc != right a[ loc ] = pivot right = right - 1 return loc F loc == right a [ loc ] = a [ right ] left = loc + 1 ; loc = right; T flag = 1 quick_sort ( int a [ ], int beg, int end ) F loc == left T loc = partition( a , beg , end ) pivot >= a [ left ] &&loc != left + 1 A B quick_sort ( a , beg , end ) return

Merge Sort ( Divide and conquer ) Divide the array 39 39 39 9 81 45 90 27 72 18 81 45 90 27 72 18 Merge the elements to sorted array 39 9 9 39 9 81 45 45 81 39 45 81 9 90 27 27 90 72 18 18 72 18 27 72 90 18 27 39 45 72 81 90 -- Merge sort technique sorts a given set of values by combining two sorted arrays into one larger sorted arrays. -- A small list will take fewer steps to sort than a large list. -- Fewer steps are required to construct a sorted list from two sorted lists than two unsorted lists. -- You only have to traverse each list once if they're already sorted. Merge_sort Algorithm 1. If the list is of length 0 or 1, then it is already sorted. Otherwise: 2. Divide the unsorted list into two sublists of about half the size. 3. Sort each sublist recursively by re-applying merge sort. 4. Merge the two sublists back into one sorted list. Time complexity Worst case - O(n log n) Best case - O(nlogn) typical, O(n) natural variant Average case - O( n log n )

Merge Sort - Program void merge(int a[ ], int low, int high, int mid){ int i, j, k, c[50]; i=low; j=mid+1; k=low; while( ( i<=mid )&&( j <= high ) ) { if( a[ i ]<a[ j ] ){ c[ k ]=a[ i ]; k++; i++; }else { c[ k ]=a[ j ]; k++; j++; } } while( i<=mid ) { c[k]=a[ i ]; k++; i++; } while(j<=high) { c[k]=a[ j ]; k++; j++; } for(i=low; i<k; i++) a[ i ]=c[ i ]; } void merge_sort(int a[ ], int low, int high){ int mid; if( low < high) { mid=(low+high)/2; merge_sort (a, low, mid); merge_sort (a, mid+1 , high); merge (a, low, high, mid); } } void print_array (int a [ ], int n) { int i; for ( i = 0 ; I < n ; i++ ) printf( "%5 d“ , a [ i ] ) ; } int main () { int count , num[ 50 ] , i ; printf ("How many elements to sort : "); scanf ("%d", &count ); printf ("n Enter the elements : nn"); for( i = 0; i < count; i++ ) { printf ("num [%d ] : “ , i ); scanf( "%d", &num[ i ] ); } printf (“ n Array Before Sorting : nnn“ ); print_array ( num , count ) ; merge_sort ( num , 0 , count-1) ; printf ( "nnn Array After Sorting : nnn“ ); print_array ( num , count ); }

merge i =low ; j = mid+1; k = low Merge_Sort i <= mid && j <= high F T a[ i ] < a[ j ] c[ k ] =a [ i ] ; k++ ; i++ c[ k ] =a [ j ] ; k++ ; j++ F low < high T mid = ( low + high ) / 2 merge_sort (a, low, mid) i <= mid c[ k ] =a [ i ] ; k++ ; i++ j <= high merge_sort (a, mid, high ) Merge (a, low, high , mid) c[ k ] =a [ j ] ; k++ ; j++ i = low ; i < k ; i ++ a[ i ] = c [ i ] return Return