There are many types of Sorting techniques, differentiated by their efficiency and space requirements. Following are some sorting techniques which we will be covering in next sections.

The following chart compares sorting algorithms on the various criteria outlined above; the algorithms with higher constant terms appear first, though this is clearly an implementation-dependent concept and should only be taken as a rough guide when picking between sorts of the same big-O efficiency.

# Bubble Sort

Bubble Sort is an algorithm which is used to sort N elements that are given in a memory for eg: an Array with N number of elements. Bubble Sort compares all the element one by one and sort them based on their values.

It is called Bubble sort, because with each iteration the smaller element in the list bubbles up towards the first place, just like a water bubble rises up to the water surface.

Sorting takes place by stepping through all the data items one-by-one in pairs and comparing adjacent data items and swapping each pair that is out of order.

Fig: Bubble Sorting

#### Sorting using Bubble Sort Algorithm

Let’s consider an array with values {5, 1, 6, 2, 4, 3}

``````int a[6] = {5, 1, 6, 2, 4, 3};
int i, j, temp;
for(i=0; i<6, i++)
{
for(j=0; j a[j+1])
{
temp = a[j];
a[j] = a[j+1];
a[j+1] = temp;
}
}
}
//now you can print the sorted array after this
``````

Above is the algorithm, to sort an array using Bubble Sort. Although the above logic will sort and unsorted array, still the above algorithm isn’t efficient and can be enhanced further. Because as per the above logic, the for loop will keep going for six iterations even if the array gets sorted after the second iteration.

Hence we can insert a flag and can keep checking whether swapping of elements is taking place or not. If no swapping is taking place that means the array is sorted and we can jump out of the for loop.

``````int a[6] = {5, 1, 6, 2, 4, 3};
int i, j, temp;
for(i=0; i<6, i++)
{
for(j=0; j a[j+1])
{
temp = a[j];
a[j] = a[j+1];
a[j+1] = temp;
flag = 1;         //setting flag as 1, if swapping occurs
}
}
if(!flag)             //breaking out of for loop if no swapping takes place
{
break;
}
}
``````

In the above code, if in a complete single cycle of j iteration(inner for loop), no swapping takes place, and flag remains 0, then we will break out of the for loops, because the array has already been sorted.

#### Complexity Analysis of Bubble Sorting

In Bubble Sort, n-1 comparisons will be done in 1st pass, n-2 in 2nd pass, n-3 in 3rd pass and so on. So the total number of comparisons will be

``````(n-1)+(n-2)+(n-3)+.....+3+2+1
Sum = n(n-1)/2
``````

Hence the complexity of Bubble Sort is O(n2).

The main advantage of Bubble Sort is the simplicity of the algorithm.Space complexity for Bubble Sort is O(1), because only single additional memory space is required for temp variable

Best-case Time Complexity will be O(n), it is when the list is already sorted.

# Insertion Sort

It is a simple Sorting algorithm which sorts the array by shifting elements one by one. Following are some of the important characteristics of Insertion Sort.

1. It has one of the simplest implementation
2. It is efficient for smaller data sets, but very inefficient for larger lists.
3. Insertion Sort is adaptive, that means it reduces its total number of steps if given a partially sorted list, hence it increases its efficiency.
4. It is better than Selection Sort and Bubble Sort algorithms.
5. Its space complexity is less, like Bubble Sorting, inerstion sort also requires a single additional memory space.
6. It is Stable, as it does not change the relative order of elements with equal keys

#### How Sorting Works

Fig: Insertion Sorting

#### Sorting using Insertion Sort Algorithm

``````int a[6] = {5, 1, 6, 2, 4, 3};
int i, j, key;
for(i=1; i<6; i++)
{
key = a[i];
j = i-1;
while(j>=0 && key < a[j])
{
a[j+1] = a[j];
j--;
}
a[j+1] = key;
}
``````

Now lets, understand the above simple insertion sort algorithm. We took an array with 6 integers. We took a variable key, in which we put each element of the array, in each pass, starting from the second element, that is a[1].

Then using the while loop, we iterate, until j becomes equal to zero or we find an element which is greater than key, and then we insert the key at that position.

In the above array, first we pick 1 as key, we compare it with 5(element before 1), 1 is smaller than 5, we shift 1 before 5. Then we pick 6, and compare it with 5 and 1, no shifting this time. Then 2 becomes the key and is compared with, 6 and 5, and then 2 is placed after 1. And this goes on, until complete array gets sorted.

#### Complexity Analysis of Insertion Sorting

Worst Case Time Complexity : O(n2)

Best Case Time Complexity : O(n)

Average Time Complexity : O(n2)

Space Complexity : O(1)

# Selection Sort

Selection sorting is conceptually the most simplest sorting algorithm. This algorithm first finds the smallest element in the array and exchanges 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 continues in this way until the entire array is sorted.

#### How Selection Sorting Works

Fig: Selection Sorting

In the first pass, the smallest element found is 1, so it is placed at the first position, then leaving first element, smallest element is searched from the rest of the elements, 3 is the smallest, so it is then placed at the second position. Then we leave 1 nad 3, from the rest of the elements, we search for the smallest and put it at third position and keep doing this, until array is sorted.

#### Sorting using Selection Sort Algorithm

``````void selectionSort(int a[], int size)
{
int i, j, min, temp;
for(i=0; i < size-1; i++ )
{
min = i;   //setting min as i
for(j=i+1; j < size; j++)
{
if(a[j] < a[min])   //if element at j is less than element at min position
{
min = j;    //then set min as j
}
}
temp = a[i];
a[i] = a[min];
a[min] = temp;
}
}
``````

#### Complexity Analysis of Selection Sorting

Worst Case Time Complexity : O(n2)

Best Case Time Complexity : O(n2)

Average Time Complexity : O(n2)

Space Complexity : O(1)

# Quick Sort

Quick Sort, as the name suggests, sorts any list very quickly. Quick sort is not stable search, but it is very fast and requires very less aditional space. It is based on the rule of Divide and Conquer(also called partition-exchange sort). This algorithm divides the list into three main parts :

1. Elements less than the Pivot element
2. Pivot element
3. Elements greater than the pivot element

In the list of elements, mentioned in below example, we have taken 25 as pivot. So after the first pass, the list will be changed like this.

`6 8 17 14 25 63 37 52`

Hnece after the first pass, pivot will be set at its position, with all the elements smaller to it on its left and all the elements larger than it on the right. Now `6 8 17 14` and `63 37 52` are considered as two separate lists, and same logic is applied on them, and we keep doing this until the complete list is sorted.

#### How Quick Sorting Works

Fig: Quick Sort

``````/*  a[] is the array, p is starting index, that is 0,
and r is the last index of array.  */

void quicksort(int a[], int p, int r)
{
if(p < r)
{
int q;
q = partition(a, p, r);
quicksort(a, p, q);
quicksort(a, q+1, r);
}
}

int partition(int a[], int p, int r)
{
int i, j, pivot, temp;
pivot = a[p];
i = p;
j = r;
while(1)
{
while(a[i]  pivot && a[j] != pivot)
j--;
if(i < j)
{
temp = a[i];
a[i] = a[j];
a[j] = temp;
}
else
{
return j;
}
}
}
``````

#### Complexity Analysis of Quick Sort

Worst Case Time Complexity : O(n2)

Best Case Time Complexity : O(n log n)

Average Time Complexity : O(n log n)

Space Complexity : O(n log n)

• Space required by quick sort is very less, only O(n log n) additional space is required.
• Quick sort is not a stable sorting technique, so it might change the occurence of two similar elements in the list while sorting.

# Merge Sort

Merge Sort follows the rule of Divide and Conquer. But it doesn’t divides the list into two halves. In merge sort the unsorted list is divided into N sublists, each having one element, because a list of one element is considered sorted. Then, it repeatedly merge these sublists, to produce new sorted sublists, and at lasts one sorted list is produced.

Merge Sort is quite fast, and has a time complexity of O(n log n). It is also a stable sort, which means the “equal” elements are ordered in the same order in the sorted list.

#### How Merge Sort Works

Fig: Merge Sort

Like we can see in the above example, merge sort first breaks the unsorted list into sorted sublists, and then keep merging these sublists, to finlly get the complete sorted list.

#### Sorting using Merge Sort Algorithm

``````/*  a[] is the array, p is starting index, that is 0,
and r is the last index of array.  */

Lets take a[5] = {32, 45, 67, 2, 7} as the array to be sorted.

void mergesort(int a[], int p, int r)
{
int q;
if(p < r)
{
q = floor( (p+r) / 2);
mergesort(a, p, q);
mergesort(a, q+1, r);
merge(a, p, q, r);
}
}

void merge(int a[], int p, int q, int r)
{
int b[5];     //same size of a[]
int i, j, k;
k = 0;
i = p;
j = q+1;
while(i <= q && j <= r)
{
if(a[i] < a[j])
{
b[k++] = a[i++];       // same as b[k]=a[i]; k++; i++;
}
else
{
b[k++] = a[j++];
}
}

while(i <= q)
{
b[k++] = a[i++];
}

while(j = p; i--)
{
a[i] = b[--k];        // copying back the sorted list to a[]
}

}
``````

#### Complexity Analysis of Merge Sort

Worst Case Time Complexity : O(n log n)

Best Case Time Complexity : O(n log n)

Average Time Complexity : O(n log n)

Space Complexity : O(n)

• Time complexity of Merge Sort is O(n Log n) in all 3 cases (worst, average and best) as merge sort always divides the array in two halves and take linear time to merge two halves.
• It requires equal amount of additional space as the unsorted list. Hence its not at all recommended for searching large unsorted lists.
• It is the best Sorting technique for sorting Linked Lists.

# Heap Sort

Heap Sort is one of the best sorting methods being in-place and with no quadratic worst-case scenarios. Heap sort algorithm is divided into two basic parts :

• Creating a Heap of the unsorted list.
• Then a sorted array is created by repeatedly removing the largest/smallest element from the heap, and inserting it into the array. The heap is reconstructed after each removal.

#### What is a Heap ?

Heap is a special tree-based data structure, that satisfies the following special heap properties :

1. Shape Property : Heap data structure is always a Complete Binary Tree, which means all levels of the tree are fully filled.

2. Heap Property : All nodes are either [greater than or equal to] or [less than or equal to] each of its children. If the parent nodes are greater than their children, heap is called a Max-Heap, and if the parent nodes are smalled than their child nodes, heap is called Min-Heap.

#### How Heap Sort Works

Initially on receiving an unsorted list, the first step in heap sort is to create a Heap data structure(Max-Heap or Min-Heap). Once heap is built, the first element of the Heap is either largest or smallest(depending upon Max-Heap or Min-Heap), so we put the first element of the heap in our array. Then we again make heap using the remaining elements, to again pick the first element of the heap and put it into the array. We keep on doing the same repeatedly untill we have the complete sorted list in our array.

In the below algorithm, initially heapsort() function is called, which calls buildheap() to build heap, which inturn uses satisfyheap() to build the heap.

``````/*  Below program is written in C++ language  */

void heapsort(int[], int);
void buildheap(int [], int);
void satisfyheap(int [], int, int);

void main()
{
int a[10], i, size;
cout << "Enter size of list";    // less than 10, because max size of array is 10
cin >> size;
cout << "Enter" << size << "elements";
for( i=0; i < size; i++)
{
cin >> a[i];
}
heapsort(a, size);
getch();
}

void heapsort(int a[], int length)
{
buildheap(a, length);
int heapsize, i, temp;
heapsize = length - 1;
for( i=heapsize; i >= 0; i--)
{
temp = a[0];
a[0] = a[heapsize];
a[heapsize] = temp;
heapsize--;
satisfyheap(a, 0, heapsize);
}
for( i=0; i < length; i++)
{
cout << "\t" << a[i];
}
}

void buildheap(int a[], int length)
{
int i, heapsize;
heapsize = length - 1;
for( i=(length/2); i >= 0; i--)
{
satisfyheap(a, i, heapsize);
}
}

void satisfyheap(int a[], int i, int heapsize)
{
int l, r, largest, temp;
l = 2*i;
r = 2*i + 1;
if(l <= heapsize && a[l] > a[i])
{
largest = l;
}
else
{
largest = i;
}
if( r <= heapsize && a[r] > a[largest])
{
largest = r;
}
if(largest != i)
{
temp = a[i];
a[i] = a[largest];
a[largest] = temp;
satisfyheap(a, largest, heapsize);
}
}
``````

#### Complexity Analysis of Heap Sort

Worst Case Time Complexity : O(n log n)

Best Case Time Complexity : O(n log n)

Average Time Complexity : O(n log n)

Space Complexity : O(n)

• Heap sort is not a Stable sort, and requires a constant space for sorting a list.
• Heap Sort is very fast and is widely used for sorting.