Definition
- one of sort array
- data structure based on binary tree
- insert data into binary tree and print out from the tree
Algorithm steps
- insert data into binary tree
- The data sorted as binary tree
Java code
public static void heapSort(int arr[]) {
int n = arr.length;
// Build heap (rearrange array)
for (int i = n / 2 - 1; i >= 0; i--)
heapTree(arr, n, i);
// One by one extract an element from heap
for (int i = n - 1; i > 0; i--) {
// Move current root to end
int temp = arr[0];
arr[0] = arr[i];
arr[i] = temp;
// call max heapify on the reduced heap
heapTree(arr, i, 0);
}
}
// To heapify a subtree rooted with node i which is
// an index in arr[]. n is size of heap
public static void heapTree(int arr[], int n, int i) {
int largest = i; // Initialize largest as root
int l = 2 * i + 1; // left = 2*i + 1
int r = 2 * i + 2; // right = 2*i + 2
// If left child is larger than root
if (l < n && arr[l] > arr[largest])
largest = l;
// If right child is larger than largest so far
if (r < n && arr[r] > arr[largest])
largest = r;
// If largest is not root
if (largest != i) {
int swap = arr[i];
arr[i] = arr[largest];
arr[largest] = swap;
// Recursively heapify the affected sub-tree
heapTree(arr, n, largest);
}
}
*this code is from https://www.geeksforgeeks.org/heap-sort/
Avg. time complexity
$O(n, log, n)$
Worst time complexity
$O(n, log, n)$
space complexity
$O(1)$
stability
no
How to calculate
numbers of loop
- insert to binary tree : $O(log, n)$
- visit every node to print : $O(n)$
Time Complexity
$O(n) \cdot O(log, n)=O(n, log, n)$