QuickSort(A[],beg,end)
1. if (beg < end)
2. pivotIndex = partition(arr,beg, end)
3. quickSort(arr, beg, pivotIndex)
4. quickSort(arr, pivotIndex + 1, end)
Partition(arr, beg, end)
1. set beg as pivot
2. small_no_count = 0
3.for i = beg to end
4. if arr[i] < pivot
5. small_no_count++
6.swap(beg,beg+small_no_count)
7.set i = beg & j = end
8.while i< j
9. if arr[i]<=pivot then i++
10. else if arr[j]>pivot then j--;
11. else swap(i,j)
11. return beg+small_no_count
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What is the working mechanism of Quick
Sort
QuickSortLike Merge Sort, QuickSort is a Divide and Conquer algorithm. It picks an element as a pivot and partitions the given array around the picked pivot. There are many different versions of quickSort that pick pivot in different ways.
Is Quick sort a stable,
in-place sorting algorithm or an
out-of-place sorting algorithm?
Quick sort is not a stable algorithm ,meaning that it won't be guaranteed
to preserve the order of elements as it reorders; two elements that are
exactly the same in the unsorted array could show up in a reversed order
in the sorted one! Stability ends up being what causes people to choose
merge sort over quicksort, and it's the one area where merge sort appears
as the obvious winner.
It needs memory in order of O(log n) this completely disqualifies quicksort as
an in-place algorithm. However, quicksort is always considered to be in-place,
it sorts the elements within the array with at most a constant amount of them
outside the array at any given time.
How does the number of elements impact the efficiency of Quick sort?
The number of elements in an array can have a significant impact on the
efficiency of the Quick sort algorithm. In general, Quick sort has an average
case time complexity of O(n log n), but the exact performance can vary based
on the specific implementation and the characteristics of the input data.
When the number of elements is small, Quick sort can sort the array quickly,
even in the worst-case scenario, as the algorithm has a low constant factor.
However, as the number of elements increases, the algorithm becomes less
efficient due to the increased number of comparisons and swaps that need to be made.
In particular, Quick sort can suffer from poor performance when the input array
is already sorted or nearly sorted, as the pivot selection can cause the algorithm
to divide the array into subarrays of unequal size. This can lead to the worst-case
time complexity of O(n^2).
To mitigate this issue, various optimizations have been proposed, such as
choosing a randomized pivot or selecting the median-of-three values as
the pivot. These optimizations can improve the performance of Quick sort,
particularly when sorting larger arrays.
What are advantages and disadvantages of Quick Sort?
Advantages: