# Merge Sort Algorithm

## Merge Sort Algorithm

- Merge Sort is a Divide and Conquer algorithm.
- It divides input array in two halves, calls itself for the two halves and then merges the two sorted halves.
**The merge() function**is used for merging two halves. The merge(arr, l, m, r) is key process that assumes that arr[l..m] and arr[m+1..r] are sorted and merges the two sorted sub-arrays into one.

### Let’s See the following C implementation:

MergeSort(arr[], l, r)If r > l1.Find the middle point to divide the array into two halves: middle m = (l+r)/22.Call mergeSort for first half: Call mergeSort(arr, l, m)3.Call mergeSort for second half: Call mergeSort(arr, m+1, r)4.Merge the two halves sorted in step 2 and 3: Call merge(arr, l, m, r)

The following diagram shows the complete merge sort process for an example array {38, 27, 43, 3, 9, 82, 10}.

If we take a closer look at the above process, we can see that the array is recursively divided in two halves till the size becomes 1. Once the size becomes 1, the merge processes comes into action and starts merging arrays back till the complete array is merged.

### Let’s Understand the complete process

- Divide the unsorted list into N sub lists, each containing 1 element.
- Take adjacent pairs of two singleton lists and merge them to form a list of 2 elements. N will now convert into N/2 lists of size 2.
- Repeat the process till a single sorted list of obtained.

Time Complexity of Merge Sort

Time Complexity of Merge Sort

**Time Complexity:** Sorting arrays on different machines. Merge Sort is a recursive algorithm and time complexity can be expressed as following recurrence relation.

T(n) = 2T(n/2) +

The above recurrence can be solved either using Recurrence Tree method or Master method. It falls in case II of Master Method and solution of the recurrence is .

Time complexity of Merge Sort is in all 3 cases (worst, average and best) as merge sort always divides the array into two halves and take linear time to merge two halves.

**Applications of Merge Sort**

- Merge Sort is useful for sorting linked lists in O(nLogn) time.In the case of linked lists, the case is different mainly due to the difference in memory allocation of arrays and linked lists. Unlike arrays, linked list nodes may not be adjacent in memory. Unlike an array, in the linked list, we can insert items in the middle in O(1) extra space and O(1) time. Therefore merge operation of merge sort can be implemented without extra space for linked lists.

In arrays, we can do random access as elements are contiguous in memory. Let us say we have an integer (4-byte) array A and let the address of A[0] be x then to access A[i], we can directly access the memory at (x + i*4). Unlike arrays, we can not do random access in the linked list. Quick Sort requires a lot of this kind of access. In linked list to access i’th index, we have to travel each and every node from the head to i’th node as we don’t have a continuous block of memory. Therefore, the overhead increases for quicksort. Merge sort accesses data sequentially and the need of random access is low. - Inversion Count Problem
- Used in External Sorting

### C Program Example of Merge Sort

#include <stdio.h> #define max 10 int a[11] = { 10, 14, 19, 26, 27, 31, 33, 35, 42, 44, 0 }; int b[10]; void merging(int low, int mid, int high) { int l1, l2, i; for(l1 = low, l2 = mid + 1, i = low; l1 <= mid && l2 <= high; i++) { if(a[l1] <= a[l2]) b[i] = a[l1++]; else b[i] = a[l2++]; } while(l1 <= mid) b[i++] = a[l1++]; while(l2 <= high) b[i++] = a[l2++]; for(i = low; i <= high; i++) a[i] = b[i]; } void sort(int low, int high) { int mid; if(low < high) { mid = (low + high) / 2; sort(low, mid); sort(mid+1, high); merging(low, mid, high); } else { return; } } int main() { int i; printf("List before sorting\n"); for(i = 0; i <= max; i++) printf("%d ", a[i]); sort(0, max); printf("\nList after sorting\n"); for(i = 0; i <= max; i++) printf("%d ", a[i]); }

If we compile and run the above program, it will produce the following result −

## Output

List before sorting 10 14 19 26 27 31 33 35 42 44 0 List after sorting 0 10 14 19 26 27 31 33 35 42 44

Pingback: My Homepage