Consider the following algorithm for building a Heap of an input array A.
BUILD-HEAP(A) heapsize := size(A); for i := floor(heapsize/2) downto 1 do HEAPIFY(A, i); end for END
A quick look over the above algorithm suggests that the running time is , since each call to Heapify costs and Build-Heap makes such calls.
This upper bound, though correct, is not asymptotically tight.
We can derive a tighter bound by observing that the running time of Heapify depends on the height of the tree ‘h’ (which is equal to lg(n), where n is number of nodes) and the heights of most sub-trees are small.
The height ’h’ increases as we move upwards along the tree. Line-3 of Build-Heap runs a loop from the index of the last internal node (heapsize/2) with height=1, to the index of root(1) with height = lg(n). Hence, Heapify takes different time for each node, which is .
For finding the Time Complexity of building a heap, we must know the number of nodes having height h.
For this we use the fact that, A heap of size n has at most nodes with height h.
Now to derive the time complexity, we express the total cost of Build-Heap as-
Step 2 uses the properties of the Big-Oh notation to ignore the ceiling function and the constant 2(). Similarly in Step three, the upper limit of the summation can be increased to infinity since we are using Big-Oh notation.
Sum of infinite G.P. (x < 1)
On differentiating both sides and multiplying by x, we get
Putting the result obtained in (3) back in our derivation (1), we get
Hence Proved that the Time complexity for Building a Binary Heap is .
This article is contributed by Chirag Manwani. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to firstname.lastname@example.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.
Attention reader! Don’t stop learning now. Get hold of all the important DSA concepts with the DSA Self Paced Course at a student-friendly price and become industry ready.
- Difference between Binary Heap, Binomial Heap and Fibonacci Heap
- Python Code for time Complexity plot of Heap Sort
- Building Heap from Array
- Complexity analysis of various operations of Binary Min Heap
- Convert min Heap to max Heap
- Heap Sort for decreasing order using min heap
- Difference between Min Heap and Max Heap
- An interesting time complexity question
- Time Complexity where loop variable is incremented by 1, 2, 3, 4 ..
- Time Complexity of a Loop when Loop variable “Expands or Shrinks” exponentially
- Time complexity of recursive Fibonacci program
- Practice Questions on Time Complexity Analysis
- Time Complexity Analysis | Tower Of Hanoi (Recursion)
- C program for Time Complexity plot of Bubble, Insertion and Selection Sort using Gnuplot
- Time Complexity of Loop with Powers
- A Time Complexity Question
- Understanding Time Complexity with Simple Examples
- What does 'Space Complexity' mean?
- Complexity of different operations in Binary tree, Binary Search Tree and AVL tree
- Knowing the complexity in competitive programming