I need to calculate Heap Sort's time complexity using Master's Theorem, but I don't know which is the recurrence relation. I know that it's complexity is O(n log n), since it traverses n times a binary tree. But I need to specifically use Master's Theorem, for which I need the recurrence relation. Which is the relation for Heap Sort?
Let us start with the heapsort algorithm:
heap_sort(int Arr[])
{
int heap_size = n;
build_maxheap(Arr);
for(int i = n; i >= 2 ; i--)
{
swap(Arr[1], Arr[i]);
heap_size = heap_size - 1;
heapify(Arr, 1, heap_size);
}
}
The build_maxheap() funnction has a standard implementation of O(n).
The important part of the sorting is the for loop, which executes for n times. Inside that we have a swap method call and heapify method call. The heapify method is a standard walk through of complete binary tree. Hence, the complexity is O(log n)
T(n) = O(n) + n * O(log n) = O(n * log n)
Master theorem is useful for solving recurrence relations of many divide and conquer algorithms.
Now, if you are interested in application of master theorem. We can implement a recursive algorithm
heap_sort(int Arr[])
{
int heap_size = n;
build_maxheap(Arr);
heap_sort_recurse(Arr, heap_size);
}
heap_sort_recurse(int Arr[], heap_size)
{
swap(Arr[1], Arr[n]);
heap_size = heap_size - 1;
heapify(Arr, 1, heap_size);
}
In this case you may have a recurrence equation as below
T(n) = T(n-1) + O(log n)
Clearly, this cannot be solved directly by master theorem. There is a modified formula derived for Subtract-and-Conquer type.
This link might be useful.
For recurrences of form,
T(n) = aT(n-b) + f(n)
where n > 1, a>0, b>0
If f(n) is O(nk) and k>=0, then
Applying this,
We have a = 1, b = 1, k = 0
Therefore, 2nd case is applicable. Hence,
T(n) = O(n0+1 * log n) = O(n * log n)
Hope it helps!