Sorting plays a very important role in data structure. Different type of sorting is given that is most popular and mostly used. It is described in very easy and brief way.

1. Data Structures

2. Recurrences
Divide-and-Conquer Problems
problem
Divide
subproblemsubproblem subproblem
Conquer subproblem subproblem
subproblem
Combine problem

3. Recurrences
Divide-and-Conquer Problems
•Binary Search
• Merge Sort
• Quick Sort
• Selection Sort
• Search Maximum
• Multiplying Large Integers
• Multiplying Chain of Matrices

4. Divide the problems into a number of sub
problems.
Conquer the sub problems by solving
them recursively. If the sub-problem
sizes are small enough, just solve the
problems in a straight forward manner.
Combine the solutions to the sub
problems into the solution for the original
problem.
Divide and Conquer Approach

5. Divide the n element sequence to be
sorted into two subsequences of n/2
elements each.
Conquer: Sort the two subsequences to
produce the sorted answer.
Combine: Merge the two sorted sub
sequences to produce the sorted answer.
Merge Sort

6. Merge Sort
Base Case: When the sequences to be sorted has length
1.
108 56 1214 89 3466Unsorted
108 56 1466
Divide
10866
Divide
66
Divide
66
BCase
66
Merge
108
Divide
108
BCase
108
Merge
66 108
Merge
56 14
Divide
56
Divide
56
BCase
56
Merge
14
Divide
14
BCase
14
Merge
14 56
Merge
56 66 10814
Merge
1289 34
Divide
1289
Divide
89
Divide
89
BCase
89
Merge
12
Divide
12
BCase
12
Merge
8912
Merge
34
Divide
34
BCase
34
Merge
3412 89
Merge
14 34 8956 66 10812Sorted

7. Merge Sort Algorithm
MergeSort(A, i, j)
if j > i then
mid ← (i + j)/2
MergeSort(A, i, mid )
MergeSort(A, mid + 1, j )
Merge(A, i, mid, j )

8. Merge Algorithm
The basic merging algorithms takes
Two input arrays, A[] and B[],
An output array C[]
And three counters aptr, bptr and cptr. (initially set
to the beginning of their respective arrays)
The smaller of A[aptr] and B[bptr] is copied to the next
entry in C i.e. C[cptr].
The appropriate counters are then advanced.
When either of input list is exhausted, the remainder of
the other list is copied to C.

9. Merge Algorithm
56 66 10814
A[]
aptr
3412 89
B[]
bptr
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
14 > 12 therefore
C[]
cptr
C[cptr] = 12
12

10. Merge Algorithm
56 66 10814
A[]
aptr
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
14 > 12 therefore
cptr++
cptr
C[]
bptr
C[cptr] = 12
12

11. Merge Algorithm
56 66 10814
A[]
aptr
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
14 > 12 therefore
cptr++
cptr
C[]
bptr
bptr++
C[cptr] = 12
12

12. Merge Algorithm
56 66 10814
A[]
aptr
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
14 < 34 therefore
cptr
C[]
bptr
12
C[cptr] = 14
14

13. Merge Algorithm
56 66 10814
A[]
aptr
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
14 < 34 therefore
cptr++
cptr
C[]
bptr
12
C[cptr] = 14
14

14. Merge Algorithm
56 66 10814
A[]
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
14 < 34 therefore
cptr++
cptr
C[]
bptr
aptr
aptr++
12
C[cptr] = 14
14

15. Merge Algorithm
56 66 10814
A[]
aptr
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
56 > 34 therefore
cptr
C[]
bptr
12 14
C[cptr] = 34
34

16. Merge Algorithm
56 66 10814
A[]
aptr
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
56 > 34 therefore
cptr++
cptr
C[]
bptr
12
C[cptr] = 34
14 34

17. Merge Algorithm
56 66 10814
A[]
aptr
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
56 > 34 therefore
cptr++
cptr
C[]
bptr
bptr++
12
C[cptr] = 34
14 34

18. Merge Algorithm
56 66 10814
A[]
aptr
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
56 < 89 therefore
cptr
C[]
bptr
12 14 34
C[cptr] = 56
56

19. Merge Algorithm
56 66 10814
A[]
aptr
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
56 < 89 therefore
cptr++
cptr
C[]
bptr
12 14 34
C[cptr] = 56
56

20. Merge Algorithm
56 66 10814
A[]
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
56 < 89 therefore
cptr++
cptr
C[]
bptr
aptr
aptr++
12 14 34
C[cptr] = 56
56

21. Merge Algorithm
56 66 10814
A[]
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
66 < 89 therefore
cptr
C[]
bptr
aptr
12 14 34 56
C[cptr] = 66
66

22. Merge Algorithm
56 66 10814
A[]
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
66 < 89 therefore
cptr++
cptr
C[]
bptr
aptr
12 14 34
C[cptr] = 66
56 66

23. Merge Algorithm
56 66 10814
A[]
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
66 < 89 therefore
cptr++
cptr
C[]
bptr
aptr
aptr++
12 14 34
C[cptr] = 66
56 66

24. Merge Algorithm
56 66 10814
A[]
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
108 > 89 therefore
cptr
C[]
bptr
aptr
12 14 34 56 66
C[cptr] = 89
89

25. Merge Algorithm
56 66 10814
A[]
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
108 > 89 therefore
cptr++
cptr
C[]
bptr
aptr
12 14 34 56 66
C[cptr] = 89
89

26. Merge Algorithm
56 66 10814
A[]
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
108 > 89 therefore
cptr++
cptr
C[]
bptr
aptr
bptr++
12 14 34 56 66
C[cptr] = 89
89

27. Merge Algorithm
56 66 10814
A[]
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
Array B is now
finished, copy
remaining elements of
array A in array C
cptr
C[]
bptr
aptr
12 14 34 56 66 89

28. Merge Algorithm
56 66 10814
A[]
3412 89
B[]
If A[aptr] < B[bptr]
C[cptr++] = A[aptr++]
Else
C[cptr++] = B[bptr++]
Array B is now
finished, copy
remaining elements of
array A in array C
cptr
C[]
bptr
aptr
12 14 34 56 66 89 108

29. Computation Tree
108 56 1214 89 3466
108 56 1466 1289 34
10866 56 14 1289 34
66 108 56 14 89 12
N = 7
lg 7  = 3
Tree Depth = 3

30. Problem with Merge Sort
Merging two sorted lists requires linear extra
memory.
Additional work spent copying to the
temporary array and back through out the
algorithm.
This problem slows down the sort considerably.

31. Quick Sort
Division of arrays in such a way that
The sorted sub arrays do not need to be later
merged.
This can be done by rearranging the elements in
A(1:n) such that
A(i) <= A(j) for all i between 1 and m and all j
between m+1 and n.
Thus the elements in A(1:m) and A(m+1: n)
may be independently sorted. No merge is

32. Quick Sort Algorithm
To sort an array S
1. If the number of elements in S is 0 or 1, then
return.
2. Pick any element V in S. This is called pivot.
3. Partition S-{V} (the remaining elements in S)
into two disjoint groups:
S1= {x ∈ S – {V} | x <= V},
S2= {x ∈ S – {V} | x >= V}
4. Return {quickSort(S1) followed by v followed
by quickSort(S ) }

33. Quick Sort Partitioning
Rearrange the array such that
Element V (pivot) is in its final position
None of elements in A[1] .. A[m] is > V
None of elements in A[m+1] .. A[n] is < V
pivot
elements lower
than pivot
elements higher
than pivot
← unsorted → ← unsorted →

34. Quick Sort Partitioning
Partition step/strategy is a design
decision.
Picking the Pivot
Best strategy would be to pick the median
of the array.
Median of three partitioning.

35. Quick Sort Partitioning
Picking the Pivot:
A = 8, 1, 4, 9, 6, 3, 5, 2, 7, 0
Pick three elements randomly and the
median of these three numbers is the
pivot.

36. Quick Sort Partitioning
1 4 39 6 58 2 7 0
Left = 8, Right = 0,
Center = (Left + Right)/2 = 4
• 4th
elements of the array started with 0 is the
pivot => 6
1 4 39 6 58 2 7 0

37. Quick Sort Partitioning
Replace the pivot with the last element
1 4 39 6 58 2 7 0
1 4 39 0 58 2 7 6
i j
Two indicies, i starts from the first element.
j starts from the second last element
Move all the smaller elements to the left and all
the larger elements to the right (small and large is
relative to the pivot).

38. Quick Sort Partitioning
1 4 39 0 58 2 7 6
i j
A[ j ] > pivot therefore decrement j
1 4 39 0 58 2 7 6
i j
A[i] > pivot

39. Quick Sort Partitioning
Swap A[i] and A[ j ]
1 4 39 0 58 2 7 6
i j
A[ j ] < pivot
1 4 39 0 52 8 7 6
i j
A[ i ] < pivot increment i until A[ i ] becomes
greater than pivot

40. Quick Sort Partitioning
1 4 39 0 52 8 7 6
i j
1 4 39 0 52 8 7 6
i j
i
1 4 39 0 52 8 7 6
j
A[ i ] > pivot.
A[ j ] > pivot therefore decrement j until A[ j ] < pivot

41. Quick Sort Partitioning
i
1 4 39 0 52 8 7 6
j
A[ j ] < pivot. Swap A[ i ] and A [ j ]
i
1 4 35 0 92 8 7 6
j
A[ i ] < pivot increment i until A[ i ] becomes
greater than pivot

42. Quick Sort Partitioning
i
1 4 35 0 92 8 7 6
j
i
1 4 35 0 92 8 7 6
j
i
1 4 35 0 92 8 7 6
j
A[ i ] > pivot.
A[ j ] > pivot therefore decrement j until A[ j ] < pivot

43. Quick Sort Partitioning
i
1 4 35 0 92 8 7 6
j
A[ j ] < pivot.
i and j crosses each other therefore swap A[ i ]
with pivot.
All less then pivot
1 4 35 0 62 8 7 9
All greater then
pivot

44. Quick Sort Partitioning
All less then pivot
Apply same strategy on
this sublist separately
1 4 35 0 62 8 7 9
All greater then
pivot
Apply same
strategy on this
sublist
separately

45. Quick Sort Algorithm
QuickSort(A, left, right) {
if right > left then {
Pivot = Partition(A, left, right);
QuickSort(A, left, pivot-1);
QuickSort(A, pivot+1, right);
}
}

46. Partition Algorithm
Partition(a[ ], left, right ) {
int i, j;
int Pivot = (left+right) / 2;
Swap(A[Pivot], A[right]);
i = left; j = right - 1;
Pivot = right;
while ( i < j ) {
while( a[ i ] <= a[Pivot]) i++;
while( a[ j ] >= a[Pivot]) j- -;
if ( i < j ) SWAP(a[i], a[ j ]);
}
Swap(A[ i ], A[Pivot])
return i
}

48. Selection Sort
Algorithm
To sort an array A[1…n], consisting of n elements,
the selection sort procedure is as follows. Scan
array A[1..n] to find the minimum element.
Swap minimum element with A[1]
Scan sub-array A[2..n] to find the minimum element.
Swap minimum element with A[2]
Scan sub-array A[3..n] to find the minimum element.
Swap minimum element with A[3]
Scan sub-array A[n-1.n] to find the minimum
element. Swap minimum element with A[n-1]
At the end array A[1…n] is sorted