Python Program for Iterative Quick Sort
# Python program for implementation of Quicksort # This function is same in both iterative and recursive def partition(arr,l,h): i = ( l - 1 ) x = arr[h] for j in range(l , h): if arr[j] <= x: # increment index of smaller element i = i+1 arr[i],arr[j] = arr[j],arr[i] arr[i+1],arr[h] = arr[h],arr[i+1] return (i+1) # Function to do Quick sort # arr[] --> Array to be sorted, # l --> Starting index, # h --> Ending index def quickSortIterative(arr,l,h): # Create an auxiliary stack size = h - l + 1 stack = [0] * (size) # initialize top of stack top = -1 # push initial values of l and h to stack top = top + 1 stack[top] = l top = top + 1 stack[top] = h # Keep popping from stack while is not empty while top >= 0: # Pop h and l h = stack[top] top = top - 1 l = stack[top] top = top - 1 # Set pivot element at its correct position in # sorted array p = partition( arr, l, h ) # If there are elements on left side of pivot, # then push left side to stack if p-1 > l: top = top + 1 stack[top] = l top = top + 1 stack[top] = p - 1 # If there are elements on right side of pivot, # then push right side to stack if p+1 < h: top = top + 1 stack[top] = p + 1 top = top + 1 stack[top] = h # Driver code to test above arr = [4, 3, 5, 2, 1, 3, 2, 3] n = len(arr) quickSortIterative(arr, 0, n-1) print ("Sorted array is:") for i in range(n): print ("%d" %arr[i]), # |
Output:
Sorted array is: 1 2 2 3 3 3 4 5
The above mentioned optimizations for recursive quick sort can also be applied to iterative version.
1) Partition process is same in both recursive and iterative. The same techniques to choose optimal pivot can also be applied to iterative version.
2) To reduce the stack size, first push the indexes of smaller half.
3) Use insertion sort when the size reduces below a experimentally calculated threshold.