# Merge Sort In C++ Tutorial With Example | C++ Merge Sort Program

Merge Sort In C++ Tutorial With Example | C++ Merge Sort Program is today’s topic. Merge sort is an efficient and comparison based algorithm to sort an array or a list of integer you can say. It is a** Divide and Conquer** algorithm which was invented by John von Neumann in 1945. Now you may question what is Divide and Conquer method.

**Merge sort** first divides an array into equal halves and then combines them in a sorted manner.

You can check out Bubble Sort and Selection Sort examples in this blog.

Content Overview

**Merge Sort In C++ Tutorial With Example**

Let’s discuss the divide and concur method. It has the following three tasks.

**Divide**the problems into subproblems that are similar to the original but smaller in size.**Conquer**the sub-problems in recursively. If they are smaller in size solve them in a straight forward manner.**Combine**those solutions to a solution which is the answer to the original problem.

Merge sort keeps on dividing the list into equal halves until it can no more be divided. By definition, if it is only one element in the list, it is sorted. Then, merge sort combines the smaller sorted lists keeping the new list sorted too.

**How Divide and Conquer Algorithm works in Merge Sort**

**Sorting Problem:** Sort a sequence of *n* elements into non-decreasing order.

*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 answer.

The top-down merge sort approach a methodology which uses the recursion mechanism. It starts from the Top and proceeds downwards, with each recursive turn asking the same question such as “What is required to be done to sort the array?” and having an answer as, “split an array into two, make a recursive call, and merge the results.”, until one gets to the bottom of an array tree.

**Example of Merge Sort**

Okay, now before going to the code and all first understand a model of Merge Sort, this will help you to understand the code.

Let us consider a list of integer:

43 | 63 | 93 | 24 | 15 | 27 | 21 |

So, as per the algorithm, we first need to divide the length of the list (n) into two parts and so on until they are divided into one node only.

So here **n=7** now **n/2=3**. Thus, the first part will of 3 integers and the second part will be of **7-3=4** part.

**Note that: If the size of the list is even then both side will be divided equally. But for the case of ODD smaller size will be at the left side and more significant will be on the right side.**

So, after **Step1** the list will be divided like the following.

43 | 63 | 93 | 24 | 15 | 27 | 21 |

Like this again we will divide each of the lists by 2. So, Left side list will be in 1+2 and right side list will appear in the 2+2 part. The same thing will happen again until they all get separated. So, here we will see the Final list after the divide method. See the following figure.

Okay, after all the Divide operation performed, we will now perform **Conquer **that is Sort the two subsequences recursively using merge sort and** Combine **that is Merge the two sorted subsequences to produce the sorted answer operation.

So here we will see what will be the result after each step:

So, what happened here is… First it compared two sub-sequence, for example here 63 and 93 are checked and then they were placed in ascending order after the merge, similarly, 27 and 21 are checked and then placed ascending order after merged. Again in step two 43,63,93 were checked and placed in ascending order after merge. By this way, we got our **final shorted list.**

15 |
21 |
24 |
27 |
43 |
63 |
93 |

**Pseudo Code for Merge Sort**

Merge Sort Pseudo Code is following.

procedure mergesort( var a as array ) if ( n == 1 ) return a var l1 as array = a[0] ... a[n/2] var l2 as array = a[n/2+1] ... a[n] l1 = mergesort( l1 ) l2 = mergesort( l2 ) return merge( l1, l2 ) end procedure procedure merge( var a as array, var b as array ) var c as array while ( a and b have elements ) if ( a[0] > b[0] ) add b[0] to the end of c remove b[0] from b else add a[0] to the end of c remove a[0] from a end if end while while ( a has elements ) add a[0] to the end of c remove a[0] from a end while while ( b has elements ) add b[0] to the end of c remove b[0] from b end while return c end procedure

**C++ Program of Merge Sort**

See the following **mergesort.cpp** file.

#include<bits/stdc++.h> using namespace std; int Merge(int A[],int p, int q,int r) { int n1,n2,i,j,k; //size of left array=n1 //size of right array=n2 n1=q-p+1; n2=r-q; int L[n1],R[n2]; //initializing the value of Left part to L[] for(i=0;i<n1;i++) { L[i]=A[p+i]; } //initializing the value of Right Part to R[] for(j=0;j<n2;j++) { R[j]=A[q+j+1]; } i=0,j=0; //Comparing and merging them //into new array in sorted order for(k=p;i<n1&&j<n2;k++) { if(L[i]<R[j]) { A[k]=L[i++]; } else { A[k]=R[j++]; } } //If Left Array L[] has more elements than Right Array R[] //then it will put all the // reamining elements of L[] into A[] while(i<n1) { A[k++]=L[i++]; } //If Right Array R[] has more elements than Left Array L[] //then it will put all the // reamining elements of L[] into A[] while(j<n2) { A[k++]=R[j++]; } } //This is Divide Part //This part will Divide the array into //Sub array and then will Merge them //by calling Merge() int MergeSort(int A[],int p,int r) { int q; if(p<r) { q=(p+r)/2; MergeSort(A,p,q); MergeSort(A,q+1,r); Merge(A,p,q,r); } } int main() { int n; cout<<"Enter size of the Array: "; cin>>n; int A[n],i; cout<<"Enter array values:\n"; for(i=0;i<n;i++) cin>>A[i]; //Calling the MergeSort() //First we are passing the array //the start index that is 0 //and the size of the array n MergeSort(A,0,n-1); cout<<"The Sorted List is\n"; for(i=0;i<n;i++) { cout<<A[i]<<" "; } return 0; }

The output is following.

**Time Complexity for Merge Sort**

Running time *T***(***n***)** of Merge Sort:

- Divide: computing the middle takes Θ(1)
- Conquer: solving 2 sub-problems takes 2
*T*(*n*/2) - Combine: merging
*n*elements takes Θ(*n*) - Total time complexity is following.

T(n) = Θ(1) if n = 1T(n) = 2T(n/2) + Θ(n) if n > 1 T(n)= 2 T(n/2) + n = 2 ((n/2)log(n/2) + (n/2)) + n = n (log(n/2)) + 2n = n log n – n + 2n = n log n + n = O(n log n )

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 repetition is. O(n log n)

The time complexity of the Merge Sort is O(n log n) 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.

With worst-case time complexity being Ο(n log n), it is one of the most respected algorithms.

**Applications of Merge Sort**

- Merge Sort is useful for sorting linked lists in O(nLogn) time.
- It is used in Inversion Count Problem.
- We can use in External Sorting.

Conclusively, Merge Sort In C++ Tutorial With Example article is over.